Welcome to the Huberman Lab podcast,
where we discuss science and
I'm Andrew Huberman and I'm a professor
of neurobiology and opthalmology at
Stamford School of Medicine. My guest
today is Dr. Poppyrum. Dr. Poppyrum is a
neuroscientist, a professor at Stanford,
and the former chief scientist at Dolby
Laboratories. Her work focuses on how
technology can accelerate
neuroplasticity and learning and
generally enrich our life experience.
You've no doubt heard about and perhaps
use wearables and sleep technologies
that can monitor your sleep, tell you
how much slowwave sleep you're getting,
how much REM sleep, and technologies
that can control the temperature of your
sleep environment and your room
environment. Well, you can soon expect
wearables and hearable technologies to
be part of your life. Hearable
technologies are, as the name suggests,
technologies that can hear your voice
and the voice of other people and deduce
what is going to be best for your
immediate health and your states of
mind. Believe it or not, these
technologies will understand your brain
states, your goals, and it will make
changes to your home and working and
other environments so that you can focus
better, relax more thoroughly, and
connect with other people on a deeper
level. As Poppy explains, all of this
might seem kind of space age and maybe
even a little aversive or scary now. But
she explains how it will vastly improve
life for both kids and adults and indeed
increase human human empathy. During
today's episode, you'll realize that
Poppy is a true out ofthe- box thinker
and scientist. She has a really unique
story. She discovered she has perfect
pitch at a young age. She explains what
that is and how that shaped her
worldview and her work. Poppy also
graciously built a zerocost step-by-step
protocol for all of you. It allows you
to build a custom AI tool to improve at
any skill you want and to build better
health protocols and routines. I should
point out that you don't need to know
how to program in order to use this tool
that she's built. Anyone can use it and
as you'll see, it's extremely useful. We
provide a link to it in the show note
captions. Today's conversation is unlike
any that we've previously had on the
podcast. It's a true glimpse into the
future and it also points you to new
tools that you can use now to improve
your life. Before we begin, I'd like to
emphasize that this podcast is separate
from my teaching and research roles at
Stanford. It is however part of my
desire and effort to bring zero cost to
consumer information about science and
science related tools to the general
public. In keeping with that theme,
today's episode does include sponsors.
And now for my conversation with Dr.
Poppyrum. Dr. Poppyrum, welcome.
>> Thanks, Andy. It's great to be here.
>> Great to see you again. We should let
people know now we were graduate
students together, but that's not why
you're here. You're here because you do
incredibly original work. You've worked
in so many different domains of
technology, neuroscience, etc. Today I
want to talk about a lot of things, but
I want to start off by talking about
neuroplasticity. This incredible ability
of our nervous systems to change in
response to experience. I know how I
think about neuroplasticity, but I want
to know how you think about
neuroplasticity. In particular, I want
to know, do you think our brains are
much more plastic than most of us
believe? Like, can we change much more
than we think? and we just haven't
accessed the ways to do that. Or do you
think that our brains are pretty fixed
and in order to make progress as a
species, we're gonna have to, I don't
know, create robots or something to to
do the work that we're not able to do
because our brains are fixed. Let's
start off by just getting your take on
what neuroplasticity is and what you
think the limits on it are. I do think
we're much more plastic than and and and
then than than we talk about or we
realize in our daily lives and and just
to your point about creating robots, the
more we create robots, there's
neuroplasticity that comes with comes
with using robots as humans when we use
them in partnerships or as you know
tools to accelerate our capabilities. So
neuroplasticity the way the the where I
resonate with it a lot is uh trying to
understand and and this is what I've
done a lot of in my career is thinking
about building and developing
technologies but with an understanding
of how they shape our brain. Everything
we engage with in our daily lives,
whether it's the statistics of our
environments and our contexts or the
technologies we use on a daily basis are
shaping our brains in ways through
neuroplasticity. Um, some more than
others. Some we know as we age are very
dependent on how attentive and engaged
we are as opposed to passively just
consuming and and mo and and changing.
But we are in a place where everyone I
believe needs to be thinking more about
how the technologies they're using,
especially in the age of AI and
immersive technologies, how they are
shaping, you know, or architecting our
brains as we move forward. You go to any
neuroscience 101 medical school textbook
and there's something you'll you'll see
a few pages on something called the
homunculus. Now, what is the homunculus?
It's a data representation, but it it'll
be this sort of funnyl looking creature
when you see it. But that picture of
this sort of distorted human that you're
looking at is really just um a data
representation of how many cells in your
brain are helping or coding and
representing information for your sense
of touch, right? And that that image
though and this is where things get kind
of funny. That image comes from Wilder
Penfield back in the 40s. He recorded
the he would semataensory
cells of uh of patients just before they
were to have you know surgery for
epilepsy and such. And you know since we
don't have pain receptors in our cortex
he could have this awake human and be
able to touch different parts of their
brain and ask them you know to report
what sensation they felt on their
bodies. And so he mapped that part of
their their cortex and then that that's
how we ended up with the homunculus and
you'll see you know it'll have bigger
lips. It'll have you know smaller parts
of your back in the areas where you just
don't have the same sensitivities.
Well fast forward to today when you look
at that homunculus one of the things I
always will ask people to think about is
you know what's wrong with this image?
You know, this is an image from 1940
that is still in every textbook. And you
know, any Stamford student will look at
it and they'll immediately say, "Well,
the thumb should be bigger because we do
this all day long and I've got more
sensitivity in my fingers because I'm
always typing on my mobile device."
Which is absolutely true. Or maybe
they'll say something like, "Well, the
the ankles are the same size and and we
drive cars now a lot more than we did in
the 40s." or maybe if I live different
part of the world I drive on one side
versus the other and in in a few years
you know we probably won't be driving
and those resources get optimized
elsewhere. So what the hunculus is is
it's a representation of how our brain
has allocated resources to help us be
successful and those resources are the
limited cells we have that support
whatever we need to flourish in our
world. And the the beauty of that is
when you develop expertise, you develop
more support, more resources go to
helping you do that thing. But they also
get more specific. They develop more
specificity. So that you know I might
have suddenly a lot more cells in my
brain devoted to helping me yet you know
I'm a violinist and my well my left hand
my right hemisphere on my semata sensory
cortex I'm going to have a lot more
cells that are helping me you know feel
my fingers and and the the tips of
everything so that I can you know be
fluid and and more virtuosic but that
means I have more cells but they're more
specified they're giving me more
sensitivity they're giving me more data
that's differentiated and that's what my
brain needs and that's what my brain's
responding to. And so when we think
about that, you know, my practice as a
musician versus my practice playing
video games, all of these things
influence our brain um in and influence
our our plasticity. Now, where things
get kind of interesting to me and sort
of my obsession on that side is every
time we engage with a technology, it's
going to shape our brain, right? It's
both, you know, our environments, but
our environments are changing. Those are
shaping who we are. You know, I think
you can look at um people's hearing
thresholds and predict what city they
live in. Then absolutely. Yes.
>> Can you just briefly explain explain
thresholds and why that would be? I
mean, I was visiting the city of Chicago
a couple years ago. Beautiful city.
Yeah. Amazing food. Love the people.
>> Very loud city.
>> Wide downtown streets. Not a ton of trees
trees
>> compared to what I'm used to.
>> And I was like, "Wow, it's really loud
here." And I grew up in the suburbs. Got
out as quickly as I could. Don't like
the suburbs. Sorry. Suburb dwellers not
for me. Um I like the wilderness and I
like cities. Um, but you're telling me
that you can actually predict people's
hearing thresholds for loudness simply
based on where they were raised or where
they currently live.
>> In part, it can be both, right? Because
cities have sonic imprints, types of
noise, things that are very, you know,
very loud cities, but also what's
creating that noise, right? That's often
unique. the the the inputs, the types of
vehicles, the types of density of people
or and and um con you even the
construction in those environments, it
is changing what noise exists. That's
shaping, you know, people's hearing
thresholds at the lowest level. It's
also shaping their sensitivities. If
you're used to hearing, you know,
certain animals in your environment and
they come with, you know, uh, you should
be heightened to a certain response in
that, you're going to develop increased
sensitivity to that, right? Whereas, if
it's really abnormal, you know, to I
hear chickens. I have a neighbor who has
chickens in the city, but roosters, too.
>> Yes. Yes.
>> I grew up near a rooster. I can still
hear that rooster. >> Yeah.
>> Yeah.
>> Those those sounds are embedded deeply
in my mind. There's the semantic context
and then just the sort of spectrum,
right? And the intensity of that
spectrum. And meaning when I say
spectrum, I mean the different
frequency, amplitudes and and what that
shaping is like.
>> High pitch, low pitch, the same.
>> Yeah. Yeah. And that affects how your
neural system is is changing even at the
lowest level of what you know what it's
your your ear is your brain your cookia
is getting exposed to. But then also
where you know so that would be the
lower level you know what what sort of
noise damage might exist what exposures
but then also then there's the
amplification of you know coming from
your higher level areas that are helping
you know that these are frequencies are
more important in your context in your
environment there is a a funny like this
is kind of funny um there was a film
called I think it's the sound of silence
and it started I I love Peter Sarsgard
he was one of the the actors in it And
um it was sort of meant to be a bit
fantastical or is that a word? Is that
the right word?
But in fact to me so the the filmmakers
had inter you talked to me a lot as had
um and to to inform the sort of main
character and the way he behaved because
I have absolute pitch and there were
certain things that they were trying to
emulate in this um in this film. He he
ends up being this person who tunes
people's lives. He'll walk into their
environments and be like, "Oh, you know,
things are going badly at work or your
relationships because your your you
know, you've got this tritone, your or
your your water heater is making this,
you know, pitch and your teapot is at this."
this."
>> Oh my god, this would go over so well in
LA. People would pay millions of dollars
in Los Angeles.
>> Totally funny.
>> Do you do this for people?
>> Um, no.
>> Okay. Okay.
>> I I will tell you I I will walk into
hotel rooms and immediately if I hear
something, I'm I've moved. And so you
know that is I
>> because you have perfect pitch. Could
you define perfect pitch? Does that mean
that you can always hit a note perfectly
with your voice?
>> There is no such thing as perfect pitch.
there's absolute pitch and so think only
because uh the idea of so like that
would be a equal 440 hertz right but
that's a standard that we use in modern
time and the you know different what a
is has actually changed throughout the
our lives with aesthetic with what
people liked with the tools we used to
create music and you know in the broke
era a was 415 hertz and that
>> you hit that
>> awesome And um in any case, so that's
why it's it's absolute because you know,
guess what? As my uh Basler membrane
gets more rigid as I might age or my
temporal processing slows down, my
brain's going to still think I'm in, you
know, I'm singing 440 Hz, but it might
not be. It's
>> baselor membrane is a portion of the
internal ear that uh converts sound
waves into electrical signals, right?
Yeah. Okay, fair enough. Well,
>> I'm talking to an auditory physiologist
that help I I teach auditory physiology,
but I want to just make sure because I'm
I'm sitting across from an expert.
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>> Okay, so our brains are customized to
our experience. Yeah.
>> Especially our childhood experience, but
also our adult experience. >> Yes.
>> Yes.
>> You mentioned the homunculus, this
representation of the body surface. And
you said something that I just have to
pick up on and ask some questions about,
which is that um
>> this hypothetical Stanford student could
be any student anywhere says, "What?
Wait, nowadays, uh, we spend a lot of
time writing with our thumbs and
thinking as we write with our thumbs and
emoting, right? I mean, when we text
with our thumbs, we're sometimes
involved in an emotional exchange. >> Yeah.
>> Yeah.
>> My question is this.
The last 15 years or so have represented
an unprecedented time of new technology
integration, right? I mean, the smartphone,
smartphone,
>> um, texting. And when I text, I realized
that I'm hearing a voice in my head as I text,
text,
which is my voice. Because if I'm
texting outward, I'm sending a text.
But then I'm also internalizing the
voice of the person writing to me if I
know them.
>> But it's coming through filtered by my
brain. Right. So it's like I'm not
trying to micro dissect something here
for the sake of micro dissection but the
conversation that we have by text it's
all happening in our own head but there
are two or more players group text was
too complicated to even consider right
now but what is that transformation
really about previously I would write
you a letter would send you a letter I'd
write you an email I'd send you an email
and so the process was really slowed now
you can be in a conversation with
somebody that's fast back and forth,
>> right? Some people can type fast. You
can email fast, but nothing like what
you can do with text, right? I can even
know when you're thinking because it's
dot dot dot or you're writing, right?
And so is it possible that we've now
allocated an entire region of the
homunculus or of some other region of cortex
cortex
brain to conversation that prior to 2010
or so the brain just was not involved in
conversations of any sort. In other
words, we now have the integration of
writing with thumbs. That's new.
hearing our own voice, hearing the
hypothetical voice of the other person
at the other end and doing that all at
rapid speed. Are we talking about like a
new brain area or are we talking about
using old brain areas and just trying to
find and push the overlap in the ven
diagram? Because I remember all of this
happening very quickly and very
seamlessly. I remember like texting
showed up and it was like, "All right,
well, it's a little slow, a little
clunky." Pretty soon it was autofill.
Pretty soon it was learning us. Now we
can do voice recognition. And it's it's
it you know people picked this up very
fast. So the question is are we taking
old brain areas and combining them in
new ways or is it possible that we're
actually changing the way that our brain
works fundamentally in order to be able
to carry out something as what seems to
be nowadays trivial but as uh as basic
to everyday life as texting. What's
going on in our brain? we aren't
developing new resources. we've got the
same cells that are or I mean there's
neurogenesis of course but um it's how
those are getting allocated and you know
just one one quick comment from what we
said before when we talk about the
monculus the homunculus is an example of
a map in the brain a cortical map and
maps are important in the brain because
they you know allow cells that need to
interact to give us specificity to make
us fast to have you know tight reaction
times and things you know because you
got shorter distance and you know things
that belong together. Also there's a lot
of motility in terms of you know what
those cells respond to potentially
dependent on our inputs. So the
homunculus might be one map but there
are maps all over our brain and those
maps still have a lot of cross input. So
what you're talking about is are you
having areas where we didn't used to
allocate and differentiate in you
specificity of what those cells were
doing that are now quite related to the
different ways my brain is having to
interpret a text message and the
subtlety and the nuance of that that
actually now I'm I get faster at I have
faster reaction times I also have faster
interpretations. So am I allocating
cells that used to do something else to
allow me to have that? Probably. But I'm
also building, you know, where like
think about me as a multi-ensory object
that has, you know, I have to integrate
information across sight, sound, smell
to form a holistic, you know, object
experience. That same sort of, you know,
integration and and pattern is happening
now when we communicate in ways that it
didn't used to. So what does that mean?
It means there's a lot more
repeatability, a lot faster pattern
matching, a lot more integration that is
allowing us to go faster.
>> I completely agree. I feel like there's
an entire generation of people who grew
up with smartphones,
>> uh, for which it's just part of life. I
think one of the most impactful
statements I ever heard in this kind of
general domain was I gave a talk down at
Santa Clara University one evening to
some students.
>> Um, and I made a comment about putting
the phone away and how much easier it is
to focus when you put the phone away and
how much better life is when you take
space from your smartphone and all of
this kind of thing. And afterwards, this
young guy came up to me. He's probably
in his early 20s and he said, "Listen,
you don't get it at all." Said, "What do
you And he said, "You adopted this
technology into your life and after your
brain had developed." He said, "When,"
he's speaking for himself. He said,
"When my phone runs out of charge, I
feel the life drain out of my body and
it is unbearable
or nearly unbearable until that phone
pops back on."
And then I feel life returned to my
body. And it's because I can communicate
with my friends again. I don't feel
alone. I don't feel cut off from the
rest of the world. And I was thinking to
myself, wow. Like his statements really
stuck with me because I realized that
his brain, as he was pointing out, is
indeed fundamentally different than mine
in terms of social context,
communication, feelings of safety, and
on and on. And I don't think he's alone.
I think for some people it might not be
quite as extreme,
>> but for many of us um to see that dot
dot dot in the midst of a conversation
where we really want the answer to
something um or it's an emotionally
charged conversation can be uh a very
intense human experience.
>> That's interesting. So we've we've sped
up the rate that we transfer information
between one another. But even about
trivial things, it doesn't have to be an
argument or like is it, you know, stage
four cancer or is it benign, right? Like
these are those are extreme conditions,
right? Are they alive? Are they dead?
You know, did they find him or her or
did they not? You know, those are
extreme cases. But there's just the
everyday life of um and I noticed this
like if I go um up the coast sometimes
or I'll go to Big Su and I I will
intentionally have time away from my
phone. It takes about a an hour or two
or maybe even a half day to really drop
into the local environment where you're
not looking for stimulation coming in
through the smartphone. And I don't
think I'm unusual in that regard either.
So I guess the question is do you think that
that
the technology is good, bad, neutral or
are you agnostic as to how the
technologies are shaping our brain?
>> It goes in lots of different directions.
Um, one thing I did want to say though
with what with smartphones specifically
and sort of everything, you know, in in
audio, you know, that our ability to
have, you know, carry uh our lifetime of
music and and content with us has been
because of, you know, huge advances in
the last 25, 30 years and maybe maybe
even slightly more around um compression
algorithms that have enabled us to have
really effective what we call perceptual
compression, lossy perceptual algorithms
and things like MP3 and and you know my
my past work with companies like Dolby.
But whenever you're talking about what's
the goal of content compression
algorithms, it's to translate the
entirety of the experience, the entirety
of a signal in, you know, with with a
lot of the information removed, right?
But in intelligent ways. When you look
at the way someone is communicating with
acronyms and the shorthand that the next
generations use to communicate, it is
such a rich communication. Even though
they might just say LOL, I mean, it's
like or they might you you know, it's
it's it's actually a lossy compression
that's triggering a huge cognitive
experience, right?
>> Can you explain lossy for people who
might not be familiar with it? Lossy
means that in your encoding and decoding
of that information, there is actually
information that's lost when you decode
it. But hopefully that information is
not impacting the perceptual experience.
Imagine I have, you know, a song and I
want to represent that song. I could
take out to make my file smaller. I
could take out every other, you know,
every 500 milliseconds of that and it
would sound really horrible, right? or I
could be a lot more intelligent and
instead basically, you know, if you look
at early models like MP3, they're
they're they're kind of like
computational models of the brain. They
stop, you know, they might stop at like
the auditory nerve, but they're trying
to put a model of how our brain would
deal with sound, what we would hear,
what we wouldn't. If this sound's
present, and it's present at the same
time as this sound, then this sound
wouldn't be heard, but this sound would
be. So we don't need to spend any of our
our bits coding this sound. Instead, we
just need to code this one. And so it
becomes an intelligent way for the model
and the algorithm of deciding what
information needs to be represented and
what doesn't to create the same, you
know, the best ex perceptual experience
which perceptual meaning what we get to
you know take home. I think one of the
things that's important then why I think
whenever I had used to have to teach
some of you know what it means to
represent a rich experience with minimal
data you think with minimal information
um some of the acronyms that exist in in
like mobile texting they've taken on a
very rich life in internal
>> yeah well those are simplistic ones but
I think people can have communication
now that we can't understand entirely This
This
is because you have a 10-year-old
daughter. Does she does she have
communication by acronym that to you is cryptic
cryptic
>> sometimes. But I I have to figure it out
then. But yes, but but the point is it
that is an example of a lossy
compression algorithm that actually has
a much richer perceptual experience,
right? And it often needs context, but
it's still, you know, you're using few
bits of information to try to represent
a much richer feeling in a much richer
state, right? And you know, if you look
at different people, they're going to
have, you know, bigger physiological
experience dependent on, you know, how
how they've grown up with that kind of context.
context.
>> It sounds to me, >> yeah,
>> yeah,
>> uh I don't want to um project here, but
it sounds to me like you see the great
opportunity of the of data compression.
Like let's just stay with the use of
acronyms in texting. That's a that's a
vast data compression compared to the
kind of speech and direct exchange that
people uh engaged in 30 years ago. So
there's less data being exchanged. Um
but the experience is just as rich if
not more rich is what you're saying,
which implies to me that you look at it
as generally neutral to to benevolent.
Like it's good.
>> It's just different.
>> I'm coming up on 50 in a couple months.
as opposed to somebody saying, "Well,
you know, when I was younger, we'd write
our boyfriend or girlfriend a letter.
Uh, you know, I would um I would
actually write out a birthday card. I
would um go You'd have a face tof face
conversation." And you got this younger
generation that are saying, "Yeah,
whatever." You know, this is like what
we heard about, I used to trudge to
school in the snow kind of thing. It's
like, well, we have heated school buses
now and we've got uh you driverless
cars. So um I think this is important
and useful for people of all ages to
hear that the richness of an experience
can be maintained even though the there
are data or some elements of the
exchange are being completely removed.
>> Absolutely. But it's maintained because
of the neural connections that are built
in those individuals. Right. and that
generation. I I always think of okay and
the nervous system likes to code um
along a continuum but like yum yuck or
meh like do you think that that that a
technology is kind of neutral like yeah
you lose some things you gain some
things or do you think like this is bad
these days we hear a lot of AI fear
we'll talk about that um or you hear
also people who are super excited about
what AI can do what smartphones can do I
mean some people uh like my sister and
her daughter love smartphones because
they can communicate it gives a feeling
of safety at a distance like quick
communications are easier. It's hard to
sit down and write write a letter. Um
she's going off to college soon. So the
question is like how often will you be
in touch? It raises expectations about
frequency but it reduces of contact but
it reduces expectations of depth
>> because you can do like a hey was
thinking about you this morning and that
can feel like a lot but a letter if I
sent a letter home you know during
college to my own like hey was thinking
about you this morning love Andrew and
be like okay like I don't know how that
would be like well that didn't take long
right so I think that there's a it's a
seessaw you know
>> you get more frequency and then it comes
with different levels of you know
expectation Sean those my daughter's at
camp right now and we were only allowed
to write letters for two weeks.
>> Handwritten letters.
>> Handwritten letters. How did that get
over that? It's happening. I mean,
>> I'd lost their home in a flood years
ago. And um one of the only things I
saved out of the flood, which is this
>> and and I just brought these back
because I I got them for my brother is
the the they're this communication
between one of my ancestors, you know,
during the Civil War, like they were
courting and that was all saved these
letters back and forth between the women
and you know, and it's, you know, with
these it's like 1865. And
>> you have those letters?
>> I do. I do. I had them in my in my
computer bag until flew up here and um
but you know they were on parchment and
even though they went through a flight
they they you know they didn't run they
say and it's this very different era of
communication and it's wonderful to have
that preserved because that doesn't
translate right through um and without
um that history in any case I am a hu
huge advocate for integration of
technology but it's for me the world is
data and and I I do think that way.
It's, you know, and and I I look at what
the way my daughter behaves. I'm like,
okay, well, what data is coming in? Why
did she, you know, respond that way?
And, you know, there's this an example I
I can give. But, you know, you think we
were talking about neuroplasticity. It's
like we are the creatures of sort of
three things. One is uh you know our
sensory systems how they've evolved and
be it from by you know the intrinsic
noise that is you know causing our
sensory receptors or the external stren
you know I my brain is going to have
access to about the same amount of
information as someone with hearing loss
if I'm in a very noisy environment and
so suddenly you've induced you know
you've compromised the data I have
access to and then also our sort of
experientially established priors right
our prior is being if you think about
the brain as sort of a basian model you
things aren't always deterministic for
us like they are for some creatures our
brains having to take data and make
decisions about it and respond
>> basian we should just explain for people
deterministic would be input A leads to
output B yeah
>> Beijian is it depends on the statistics
of what's happening externally and
internally yeah
>> these are probabilistic models like
there's a likelihood of A
>> becoming B or there's a likelihood of A
driving B but there's also a probability
that A will drive C, D or F.
>> Absolutely. And you know Frank and we
should get into I mean some of the
things that make us the most effective
in our environments and just in
interacting in the world is how fast and
effective we are with dealing with those
probabilistic you know situations. Those
things where your brain it's it's like
probabilistic inference is a great
indicator of success in an environment.
And you know, be it a work environment,
be it just, you know, walking down the
street and um how that's how do we deal
with this like data that doesn't just
tell us we have to go right or left, but
there's a lot of different inputs and
it's our sort of situational
intelligence in the world. And there you
we can break that down into a lot of
different ways. In any case, we are the
products of our, you know, our sensory
systems, our experience, our priors,
which are the statistics that and data
we've had up until that moment that our
brain's using to wait how it's going to
behave in the decisions it makes, but
also then our expectations, the context
of that, you know, that have shaped
where we are. And so there's this funny
story like my daughter when she was two
and a half, we're in the planetarium at
the Smithsonian and we're watching, I
think, one typical film you might watch
in a planetarium. We started in LA, zoom
out on our way to the sun, and we pass
that sort of, you know, quintessential
NASA image of the Earth, and it's
totally dark and silent. And my
daughter, as loud as she possibly could,
yells, "Minions." And I'm like, "What's
going on?"
I'm like, "Oh, yes, of course." Her
experientially established prior of that
image is coming from the Universal logo.
And you know, she never, you know, that
says Universal.
It was totally valid, but it was this
very uh you know honest and true part of
what it is to be human. Like each of us
is experiencing very different you know
having very different experiences of the
same physical information and we need to
recognize that but it is driven by our
exposures and our priors and our sensory
systems. It's sort of that trifecta and
our expectations of the moment. And once
you unpack that, you really start to rep
and and appreciate the influence of
technology. Now I am a huge advocate for
technology improving us as humans, but
also improving the data we have to make
better decisions and the sort of
insights that drive us. At the same
time, I think sometimes we're pennywise
pound foolish with how we use technology
and the quick things that make us faster
can also make us dumber and take away
our cognitive capabilities. And you know
where you'll end up with those that are
using the technologies might be to to
you know to write papers all the time
are maybe well and we we we can talk
about that more are putting themselves
in a place where they are going to be
compromised trying to do anything
without that technology and also in
terms of their their learning of that
data that information. And so you start
even ending up with bigger
differentiations and cognitive
capabilities by whether how you use a
tool a a technology tool to make you
better or faster or not. One of my sort
of things I've always done is teach at
Stanford that thus we also have that in common.
common.
>> I need to sit in on one of your lectures
>> and you know but my my class there has
been is called neuroplasticity and video
gaming and um I'm a neurohysiologist but
I'm I'm really a technologist. I like
buildings. I like you know innovation
across many domains and while that class
says video gaming it's really more well
video games are powerful in the sense
that there's this sort of closed loop
environment you give feedback you get
data on your performance but you get to
control that and know what you randomize
how you build and what our aim is in
that class is to build technology and
games with an understanding of the
neural circuits you're impacting and how
you want to what you want to train I'll
have um students that are musicians.
I'll have students that are computer
scientists. I'll have students that are,
you know, some of Samford's top
athletes. I've had a number of their top
athletes go through my my course and um
it's always focused on okay, there's
some aspect of human performance I want
to dissect and I want to really amplify
the sensitivity or the the access to
that type of learning in a closed loop
way. Just for anyone that isn't familiar
with the role or the history of gaming
in the neuroscience space, you know,
there's been some great papers in the
past. Um, take a gamer versus a
non-gamer just to start with someone
self-identified. a typical gamer um
actually has what we would call um more
sensitive and this is your domain so you
can counter me on this anytime but you
know contrast sensitivity functions and
like a contrast sensitivity function is
um you know ability to see uh edges and
differentiation um in a visual
landscape. Okay, they can see uh faster
and uh you know more they're more
sensitive to that sort of differentiation.
differentiation.
So than someone who says I'm not a video
game player or or selfidentifies that way
way
>> because they've trained it
>> like like a first person shooter game
which I've played occasionally in an
arcade or something like that. Uh I
didn't play a lot of video games growing
up. I don't these days either but um
yeah a lot of it is based on contrast
sensitivity knowing are is that a friend
or foe are you supposed to shoot them or
not? Yeah. you have to make these
decisions very fast. Yeah. Um like right
on the threshold of of what you would
call like reflexive like no no thinking
involved but but it's just it's just
rapid rapid iteration and decision-m and
then the rules will switch. Yeah.
>> Right. Like suddenly you're supposed to
uh turn other other things into targets
and other things into into
>> you're spot on because then you take
someone who that selfidentified
non-gamer, make them play 40 hours of
Call of Duty and now their contrast
sensitivity looks like a video game
player and it persists. You know, go
back, measure them a year later, but you
know, 40 hours of playing Call of Duty
and I see the world differently, not
just in my video game. I actually have
foundational shifts in how I experience
the world that give me more greater
sensitivity to my situational awareness,
my situational intelligence, real life.
>> Yeah. Yeah.
>> Yeah. Because that's a low-level
processing capability. I love
intersecting those when you can. But
what's even I think more interesting is
you also and there these were some this
was a great study by Alex Puge um and
Daphne um devel uh where it's not just
the contrast sensitivity it's let's go
to that next level where we were talking
about basian like probabilistic
decisions where things aren't
deterministic um and 40 a video game
player and I can train this they're
going to make the same decisions as a
nonvideo game player in those you know
probabilistic envir inferential
situations, but they're going to do it a
lot faster. And so that edge, that
ability to get access to that
information is phenomenal, I think. And
and and when you can tap into that, that
becomes a very powerful thing. So like
probabilistic inference goes up when
I've, you know, played 40 hours of Call
of Duty. But then what I like to do is
take it and say, okay, here's, you know,
a training environment. You know, I had
a couple of uh de of Stanford's top
soccer players on my in my course this
this year and we got um our focus was
okay, what data do you not have and how
can we build a closed loop environment
and make it something so that you're
gaining better neurological access to
your performance based on data like my
acceleration, my velocity, not at the
end of my, you know, two-hour practice,
but in real time and getting auditory
feedback. back so that I am actually
tapping into more neural training. So,
we had uh sensors, you know, like on on
their calves that were measuring
acceleration velocity and give able to g
give us um feedback in real time as they
were doing, you know, a sort of
somewhat gamified training. I I don't
want to use gamified, it's so overused,
but let's say it's it felt like fun
environment, but it's also based on
computation of that acceleration data
and what their targets were. It's
feeding them different sonic cues so
that they're building um they're
building that resolution. When I say
resolution, what I mean is, especially
as a novice, I can't tell the difference
between whether I've accelerated
successfully or not. But if you give me
more gradation in the feedback that I
get, with that sort of that closed loop
behavior, I start to my my neural
representation of that is going to start
differentiating more. So with that,
that's where the auditory feedback. So
they're getting that in real time and we
you build that kind of closed loop
environment that helps build that, you
know, create greater resolution in the
brain and greater sensitivity to differentiation.
differentiation.
>> I'd love for you to uh share the story
about your daughter um improving her
swimming stroke, right? because she's
not a D1 athlete yet. Maybe she will be
someday, but she's a swimmer, right? And
in the past, if you wanted to get better
at swimming, you needed a swimming
coach. And if you wanted to get really
good at swimming, you'd have to find a
really good swimming coach and you'd
have to work with them repeatedly. Uh,
you took a slightly different direction
that really points to just how
beneficial and inexpensive this
technology can potentially be or
relatively inexpensive.
>> First, I'll say this. Number one is
having good swimming coaches.
>> Okay, sure. I'm not trying to do away
with swimming coaches. parents who are
uh data centric and and really like
building technologies are sometimes
maybe can be red herring distractions
but hopefully not.
>> Okay. All right. Well, yes,
>> that's one of them.
>> Let's keep the swimming coaches uh h happy.
happy.
>> Yeah. So, for example, like you go and
train with elite athletes and um if you
go to a lot of um swimming camps where
you're you or training programs, it's
always about under you know work with
cameras and and you know what what
they're they're recording you. they're,
you know, assessing your strokes. But
the point is what I mean I you can use
and I did this uh you know knowing the
things that the coaches you or frankly
you can go online and learn some of
those things that matter to different
strokes. You can use you know use
perplexity labs use replet use some of these
these
>> these are online resources.
>> Yeah. Yeah. And you can build quickly
build a computer vision app that is
giving you data analytics on your
strokes and in real time.
>> So how's that work? You you're taking
the phone underwater analyzing the stroke.
stroke.
>> In this case I'm using mobile phone so
I'm doing everything above you know.
>> Okay. So you're you're filming if you
could walk us through this. So you film
your daughter doing freestyle stroke for
right or breast stroke or butterfly.
There's a lot of core things that you
know maybe you want to care about
backstroke and freestyle. What's the you
know and I am not a I was we used to run
like I know you're a good runner but I
am a runner I'm a rock climber less a
swimmer but um you know things like the
roll or how high they're coming above
the water what's your you know what
what's your velocity on a you know you
can get actually very sophisticated once
you have the data right and you know
what's your velocity on entrance how
much you know where how far in front of
your your head is your arm coming in how
you know what is um maybe There's again
maybe there are things that you you know
are obvious which is you want to know
you know how consistent are your strokes
and your cadence across you know the
pool. Um so you don't just have your
speed you suddenly have access to what I
would call and and you'll hear me use
this a lot better resolution but also a
lot more analytics that can give you
insight. Now, important thing here is,
you know, my 10-year-old is not going to
resp I'm not going to go tell my
10-year-old that she needs to change her
her velocity on this head or stroke, but
it gives me information that I can at
least understand and help her know how
something is going and how consistent
she is on certain things that her
coaches have told her to do.
um you know and and what I love about
the idea is look this isn't just for the
ease of getting access to the type of
data and information that would
previously and I mean I do code in in a
lot of areas but you don't have to do
that anymore to build these apps in fact
you shouldn't you should leverage you
know AI for development of these types
of tools
>> you you tell AI to write a code so that
it would analyze you know trajectory
jumping into the pool how that could be
improved if the goal is to swim faster.
>> You you'd use AI to build an app that
would allow you to do that so that you
would have then access to that whatever
the data is that you want to do. Yeah.
So in that case you're trying to do
better stroke analytics and and
understand things as you move forward.
Um you could do the same thing for
running for gate for uh you could do you
know in a work environment you can
understand a lot more about where
vulnerabilities are where weaknesses
are. There are sort of two different
places where I see this type of um AI
acceleration and tool building really
having major impact. It's on sort of
democratizing data, analytics and
information that would normally be
reserved for the elite to everyone
that's really engaged and that has a
huge impact on improving performance
because that kind of data is really you
know useful in understanding um
learning. It also has applications for,
you know, when you're in a work
environment and you're trying to better
understand um success in that
environment ac in in some process or
skill of, you know, what you're doing.
Um you you can gain different analytics
than you otherwise would in ways that
are become much more uh successful but
also give you um new data to think about
with regard to what I would call a
digital twin. And when I use the word
digital twin, the goal of a digital twin
is not to digitize and represent a
physical system in its entirety. It's to
gain use different interoperable meaning
data sets coming from different sources
to gain insights you know digitized data
of a physical system or a physical
environment or physical world be it a
hospital be it airplanes be it my body
be it my fish tank to give me insights
that are you know continuous and in real
time that I otherwise wouldn't be able
to gain access to
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We will definitely talk more about
digital twins and but what I'm hearing
is that it can be very um as nerd speak
but domain specific. I mean, like the
lowest level example I can think of,
which would actually be very useful to
me, would be a digital twin of my
refrigerator that would place an order
for the things that I need, not for the
things I don't need. Um, eliminate the
the need for a shopping list. Um, it
would just keep track of like, hey, like
you usually run out of strawberries on
this day and this day. And it would just
keep track of it in the background and
the stuff would just arrive and it would
just be there. And like eliminate what
seemed like like, well, gosh, isn't
going to the store nice? Yeah, this
morning I walked to the corner store,
bought some produce. I had the time to
do that, the the eight minutes to do
that, but really I I would like the
fridge to be stocked with the things
that I like and need, and I could hire
someone to do that, but that's
expensive. This could be done trivially
and probably will be done trivially
soon, and I don't necessarily need to
even build an app into my phone.
>> So, I like to think in terms of kind of
lowest level, but highly useful
>> and easily available now
>> type technologies. There are a couple of
areas like when it comes to students
learning information. We've heard that,
you know, AI, we we've heard of AI
generally as like this really bad thing
like, oh, they're just going to use AI
to write essays and things like that.
But there's a use of AI for learning. I
know this cuz I'm still learning. I
teach and learn all the time for the
podcast, which is I've been using AI to
take large volumes of text from papers.
So this is an AI hallucinating just take
like just take large volumes of text
verbatim from from papers. >> Yes,
>> Yes,
>> I've read those papers literally printed
them out, taken notes, etc. And then
I've been using AI to design tests for
me of what's in those papers because I
learned uh you know about eight eight
months ago when researching a podcast on
how to study and learn best. The data
all point to the fact that when we self test
test
>> Yes. Especially when we self test away
from the material like when we're being
we're thinking oh yeah like what what is
the cascade of hormones driving the
cortisol negative feedback loop when I
have to think about that on a walk. >> Yes.
>> Yes.
>> As opposed to just looking it up. It's
the it's the self- testing that is
really most impactful for memory because
most of memory is anti-forgetting. This
is kind of one way to think about it.
So, what I've been doing is is having AI
build tests for me and having ask me
questions like, you know, uh what is the
the the you know, the signal between the
pituitary and the adrenals uh that
drives the release of cortisol and and
what layer of the adrenals does cortisol
come from?
>> And I love that
>> and and so it's it's I'm sure that the
information it's drawing from is is
accurate, at least to the best of
science and medicine's knowledge now.
>> And it's just testing me and it's
learning. This is what's so incredible
about AI and I don't consider myself
like extreme on AI technology at all.
It's learning where I'm weak and where
I'm strong at remembering things because
I'm asking it where am I weak and where
am I strong and they'll say oh like like
naming and this and like like like third
order conceptual links here need a
little bit of work and I go test me on
it and it starts testing me on it. It's
amazing like I'm blown away that the
technology can do this and I'm not
building apps with AI or anything. I'm
just using it to try and learn better.
Whether you're building naps or you're
building a tool, you're you're using it
as a tool that's helping you optimize
your cognition and find your weaknesses,
but also give you feedback on your
performance and and and accelerate your
learning in this, right? Because it's
the goal, but you're still putting in
the effort to learn. And I think even
the the ways that I'm using it to you
with your computer vision with mobile
devices, AI is a huge opportunity and
tool that like using the cameras and the
data that you've collected to, you know,
have much more sophisticated input is is
huge. Um, but in both of those cases,
you're shaping cognition. You're shaping
you're using data to enrich what you can
know. and AI is just, you know,
incredibly powerful and uh a great
opportunity in those spaces.
The the place where I think it is um and
I I sort of separate it into literally
just two categories. Maybe that's too
simplistic. It's am I using and and this
is true for any tool not just AI but am
I using the tool am I using the
technology in a way to make me smarter
about in a and and let me have more
information and make me more effective
but also cognitively more effective gain
different insights or am I using it to replace
replace
replace a cognitive skill I've done
before to be faster and it doesn't mean
you don't want to do those things I mean
GPS in our car is a perfect example of a
place where we're replacing a cognitive
tool of, you know, to make me faster and
more effective. And frankly, you know,
you take away your GPS and in a city you
drive around and and we're not very
good. And
>> I remember paper maps. I remember the
early studies of the Hippoc campus were
based on London taxi drivers that had
mental maps of the city. >> Absolutely.
>> Absolutely.
>> That you know at with all due respect to
London taxi drivers up until GPS like
that those mental maps are not necessary anymore.
anymore.
>> No. And I mean they had more gray matter
in their hippocampus and we know that
and you look at them today and they they
don't have to have that because the
people in their back seats have more
data have more information have eyes
from the sky. I mean satellite data is
so huge in our success in the future and
you know it can anticipate the things
that locally you can't and so it's been
replaced but it it still means when you
lose that data you don't don't expect
yourself to have the same spatial
navigation of that environment without
it right
>> I love your two your two batches right
you're either using it to make you
cognitively better or you're using it to
speed you up but you have to be here's
where I think
>> cognitively or physically
But you're still trying to gain insight
and data and information that's making
me a more effective human.
>> Right. And I think that the the place
where people are concerned >> Yes.
>> Yes.
>> including myself is when we use these
technologies that eliminate steps,
make things faster. >> Yeah.
>> Yeah.
>> But we fill in the additional time or
mental space with things that are
neutral to detrimental.
It's sort of like saying, "Okay, I can
get all the nutrients I need from a
drink that's 8 ounces." This is not
true. But then the question is like, how
do I make up the rest of my calories,
right? Am I making up with also
nutritious food, right? Um, let's just
say that keeps me at a neutral health
status or am I eating stuff that because
I need calories that I'm not necessarily
gaining weight, but I'm bringing in a
bunch of bad stuff with those calories.
is or in the mental version of this um
things are sped up but people are
filling the space with things that are
making them dumber in some cases. There
was a recent paper from MIT that I I
actually it it was
it is very much what I spend a lot of my
time talking about but and and thinking
about but um
>> yeah could you describe that study?
>> The upshot of the paper first was that
people there's a lot less uh mental
process or cognitive process that goes
on for people when they use LLMs to
write papers and they have they don't
have the same transfer and they don't
really learn the information. Surprise
surprise. So, so that to just to briefly
describe the study even though it got a
lot of popular press, it's you know um
MIT students writing papers using AI
versus writing papers the oldfashioned
way where you think and write.
>> So there were three different
categories. People who had to write the
papers uh you know just with their using
their brain only. Uh and that that would
be case one. Case two would be I get to
use search engines which would be sort
of a middle ground. Again these are you
know rough categories. And then a third
would be I use LLMs to write my paper.
And they're looking at you know sort of
what kind of transfer happened what you
know what kind of they were measuring
neural response. So they were using EEG
to look at neural patterns of uh across
the brain to understand how much neural
engagement happened during the writing
of the papers and during the the whole
process and then what they could do with
that what they knew about that
information down down the road. It's a
really nice paper, so I don't want to
want to diminish it in any way by
summarizing it. But what I think is a
really important upshot of that paper
and also just how we talk about it that
I liked was um they I I talk a lot about
cognitive load always. And you can
measure cognitive load and the diameter
of your pupil and body posture and how
people are thinking. It's really how
hard is my brain working right now uh to
solve a problem or just in my context.
And there are a lot of different cues we
give off as humans that tell us when
we're under states of different load and
cognitively and whether we are aware of
it or not. And there's something called
cognitive load theory that breaks down
sort of what happens when our brains are
under states of uh you know load. And
that load can come from sort of three
different places. It might be coming
from intrinsic uh what you would call
intrinsic information which is what and
this is all during learning the
intrinsic load cognitive load load would
be from uh you know the difficulty of
the material I'm trying to understand
how you know really some things are easy
to learn some things are a lot harder
and that's intrinsic load extraneous
load would be the load that comes from
how the information is presented uh is
it poorly taught is it poorly organized
or also in the environment. If it's I'm
trying to learn something auditorially
and it's noisy, that's introducing
extraneous cognitive load, right? It's
it just it's not the information itself,
but it's because of everything else
happening with that data. And then the
third is germaine cognitive load. And
that's the load that is used in my brain
to build mental schemas to build to
organize that information to to really
develop a representation of what that
information is that I'm taking in. And
that germaine cognitive load that's
that's the work right and if you don't
have gerine cognitive load you don't
have learning really and what they found
is basically the germaine cognitive load
is what gets impacted most by using LLMs
which is I mean it that it's a very
obvious thing like that's
>> meaning you don't engage quite as high
levels of germanine cognitive load
>> using LLMs you're not engaging the
mental effort to build cognitive schema
to build neural schemas and you sort of
the mental representation of the
information that you can interact with
it later and you have access it to
access to it later and this is really
important because without that you won't
be as intelligent on that topic that's
for sure down the road let me give two
examples I have a doctor I have a lawyer
and both of them use LLMs extensively
for searches say or for building
information in one case it's for patient
aggregation of patient data and in another case it's for you know history
another case it's for you know history of case files and that is the GPS that's
of case files and that is the GPS that's happening in those spaces and because
happening in those spaces and because those are the tools that are quickly
those are the tools that are quickly adopted where you have someone that is
adopted where you have someone that is maybe came you know from a different
maybe came you know from a different world has learned that information has
world has learned that information has gone and worked with data in a different
gone and worked with data in a different way worked their representation of that
way worked their representation of that information is going to be better at
information is going to be better at extrapolation it's going to be better at
extrapolation it's going to be better at generalization it's going to be better
generalization it's going to be better at seeing patterns that you know would
at seeing patterns that you know would exist the brain that has done everything
exist the brain that has done everything through LLMs is going to be in a place
through LLMs is going to be in a place where they will get the answer for that
where they will get the answer for that relevant task or using the tools they
relevant task or using the tools they have. But you're not the same level of
have. But you're not the same level of um richness and depth of information or
um richness and depth of information or generalization or extrapolation for
generalization or extrapolation for those topics as someone that has learned
those topics as someone that has learned in a different way. There's a
in a different way. There's a generational
generational difference in understanding, not because
difference in understanding, not because they don't have the same information,
they don't have the same information, but there is an an acknowledgement that
but there is an an acknowledgement that there's a gap even though we're getting
there's a gap even though we're getting to the same place as as fast. And that's
to the same place as as fast. And that's because of the learning that's happened.
because of the learning that's happened. >> The gerine cognitive load.
>> The gerine cognitive load. >> Absolutely. The cognitive load like
>> Absolutely. The cognitive load like you've got to do the work. your brain
you've got to do the work. your brain has to and you know what was beautiful
has to and you know what was beautiful about your descriptions Andy is when you
about your descriptions Andy is when you were talking about how you were using it
were talking about how you were using it which I I love you know to test yourself
which I I love you know to test yourself find your weak vulnerabilities is you
find your weak vulnerabilities is you know and and actually in the paper in
know and and actually in the paper in MIT which I think again these are things
MIT which I think again these are things that are somewhat obvious but we just
that are somewhat obvious but we just have to name I think we have to talk
have to name I think we have to talk about them more is people with higher
about them more is people with higher competency on the topic use the tools in
competency on the topic use the tools in ways that still engage more germaine
ways that still engage more germaine cognitive load but helped accelerate
cognitive load but helped accelerate their their learning it's you know where
their their learning it's you know where is the biggest vulnerability and gap.
is the biggest vulnerability and gap. It's when it's especially in areas and
It's when it's especially in areas and topics where you're you're trying to
topics where you're you're trying to learn a new domain fast or you're under
learn a new domain fast or you're under pressure and you're not putting in the
pressure and you're not putting in the domain effort or you're not using the
domain effort or you're not using the tools that you have access to that AI
tools that you have access to that AI can enable.
can enable. >> You're not using them to amplify your
>> You're not using them to amplify your cognitive, you know, gain, but instead
cognitive, you know, gain, but instead to deliver something faster, more rapid,
to deliver something faster, more rapid, and then walking away from it. I'm going
and then walking away from it. I'm going to try and present two parallel
to try and present two parallel scenarios
scenarios >> in order to go further into this
>> in order to go further into this question of how to use AI to our best
question of how to use AI to our best advantage to enrich our brains as
advantage to enrich our brains as opposed to diminish our brains.
opposed to diminish our brains. >> Mhm.
>> Mhm. >> So I could imagine a world because we
>> So I could imagine a world because we already live in it where there's this
already live in it where there's this notion of slow food like you cook your
notion of slow food like you cook your food, you get great ingredients from the
food, you get great ingredients from the farmers market like like a peach that
farmers market like like a peach that quote unquote really tastes like a peach
quote unquote really tastes like a peach this kind of thing. you um you you make
this kind of thing. you um you you make your own food. You you cook it and you
your own food. You you cook it and you taste it. It's just delicious. And and
taste it. It's just delicious. And and um I can also imagine a world where you
um I can also imagine a world where you order a peach pie online, it shows up
order a peach pie online, it shows up and you take a slice and you eat it. And
and you take a slice and you eat it. And you could take two different generations
you could take two different generations of people, maybe people that are
of people, maybe people that are currently now 50 or older and people
currently now 50 or older and people that are 15 or younger, and the older
that are 15 or younger, and the older generation would say, "Oh, isn't that
generation would say, "Oh, isn't that the peach pie that you made so much
the peach pie that you made so much better? Like these peaches are amazing."
better? Like these peaches are amazing." And I could imagine a real scenario
And I could imagine a real scenario where the younger person 15 to 30 let's
where the younger person 15 to 30 let's say would say like I don't know I
say would say like I don't know I actually really like the other pie. I
actually really like the other pie. I like it just as well. And the older
like it just as well. And the older generation is like this like what are
generation is like this like what are you talking about? Like this is how it's
you talking about? Like this is how it's done.
done. What's different? Well sure experience
What's different? Well sure experience is different etc. But from a neural
is different etc. But from a neural standpoint, from a neuroscience
standpoint, from a neuroscience standpoint,
standpoint, it very well could be that it tastes
it very well could be that it tastes equally good to the two of them, just
equally good to the two of them, just differs based on their experience.
differs based on their experience. Meaning that the person isn't lying.
Meaning that the person isn't lying. It's not like this kid um, you know,
It's not like this kid um, you know, isn't as fine-tuned to taste. It's that
isn't as fine-tuned to taste. It's that their neurons acclimated to like what
their neurons acclimated to like what sweetness is and what contrast between
sweetness is and what contrast between sweet and saltiness is and what a peach
sweet and saltiness is and what a peach should taste like cuz damn it, they had
should taste like cuz damn it, they had peach gummies and that tastes like a
peach gummies and that tastes like a peach, you know. And so we can be
peach, you know. And so we can be disparaging of the kind of what we would
disparaging of the kind of what we would call the lower level or diminished
call the lower level or diminished sensory input.
sensory input. >> Yeah.
>> Yeah. >> But it depends a lot on the neural what
>> But it depends a lot on the neural what those neural circuits were weaned on.
those neural circuits were weaned on. >> Couple of comments. I love the peach pie
>> Couple of comments. I love the peach pie example. Making bread is another example
example. Making bread is another example of that. And in the 90s, everyone I knew
of that. And in the 90s, everyone I knew when they graduated from high school got
when they graduated from high school got a bread maker that was shaped like a box
a bread maker that was shaped like a box and, you know, created this
and, you know, created this >> like loaf of bread with a giant, you
>> like loaf of bread with a giant, you know, rod through it. And it was just it
know, rod through it. And it was just it was the graduation gift for many years.
was the graduation gift for many years. >> And um, you know, you don't see those
>> And um, you know, you don't see those anymore. And you know if you even look
anymore. And you know if you even look at what happened with like the
at what happened with like the millennial generation in the la you know
millennial generation in the la you know in the last 5 years especially during
in the last 5 years especially during the pandemic suddenly breadmaking
the pandemic suddenly breadmaking sourdough that became a thing. What's
sourdough that became a thing. What's the difference? You know, you've got
the difference? You know, you've got bread. It's warm. It's, you know, with
bread. It's warm. It's, you know, with the bread maker, it's fresh and it is
the bread maker, it's fresh and it is not at all desired relative to bread
not at all desired relative to bread that takes a long period of time and is
that takes a long period of time and is tactile and in the process and the
tactile and in the process and the making of it and you know is clearly
making of it and you know is clearly much more ownorous than the other in its
much more ownorous than the other in its process of development. I think the key
process of development. I think the key part is it's in in the appreciation of
part is it's in in the appreciation of the bread. it. The process is part of it
the bread. it. The process is part of it and that process is development of sort
and that process is development of sort of the germaine knowledge and the
of the germaine knowledge and the commitment and connection to that
commitment and connection to that humanness of development but also the
humanness of development but also the tactile uh commitment the work that went
tactile uh commitment the work that went into it is really appreciated in the
into it is really appreciated in the same way that that peach pie for one
same way that that peach pie for one comes with that whole time series of
comes with that whole time series of data that wasn't just about my taste but
data that wasn't just about my taste but was also smell also physical also visual
was also smell also physical also visual and saw the process you know evolve and
and saw the process you know evolve and build a different prior going into that
build a different prior going into that experience and that is I think part of
experience and that is I think part of richness of human experience will it be
richness of human experience will it be part of the richness of how humans
part of the richness of how humans interact with AI absolutely or interact
interact with AI absolutely or interact with robots absolutely so it's what are
with robots absolutely so it's what are the relationships we're building and how
the relationships we're building and how are they you know how integrated are
are they you know how integrated are these tools these you know companions
these tools these you know companions whatever they may be in our existence
whatever they may be in our existence will shape us in different ways. What I
will shape us in different ways. What I am particularly I guess bullish on and
am particularly I guess bullish on and excited for is the robot that optimizes
excited for is the robot that optimizes my health, my comfort, my intent in my
my health, my comfort, my intent in my environment, in my you know be it in the
environment, in my you know be it in the cabin of a car, be it in the my my
cabin of a car, be it in the my my rooms, my spaces.
rooms, my spaces. >> So what would that look like if you uh
>> So what would that look like if you uh could you give me the lowest level
could you give me the lowest level example? um like like would it be an
example? um like like would it be an assistant that helps you travel today
assistant that helps you travel today when you head back to the Bay Area?
when you head back to the Bay Area? Would it like what is this non-physical
Would it like what is this non-physical robot?
robot? >> And I think we already have some of
>> And I think we already have some of these like it's the point where HVAC
these like it's the point where HVAC systems actually get sexy, right? Not
systems actually get sexy, right? Not sexy in that sense, but they're actually
sexy in that sense, but they're actually really interesting because they are the
really interesting because they are the heart of, you know,
heart of, you know, >> HVAC systems,
>> HVAC systems, >> heating ventilation
>> heating ventilation AC,
AC, >> but you think about a thermostat. You
>> but you think about a thermostat. You know, a thermostat right now is
know, a thermostat right now is optimizing for you an AI thermostat
optimizing for you an AI thermostat optimizing for my behavior, but it's
optimizing for my behavior, but it's trying to save me resources, trying to
trying to save me resources, trying to save me money, but it's not doesn't know
save me money, but it's not doesn't know if I'm hot or cold. It doesn't know to
if I'm hot or cold. It doesn't know to your point, it my intent, what I'm
your point, it my intent, what I'm trying to do at that moment where and
trying to do at that moment where and this is, you know, speaks more to a lot
this is, you know, speaks more to a lot of the the things you've studied in the
of the the things you've studied in the past. You know, it doesn't know what my
past. You know, it doesn't know what my optimal state is for my goal in that
optimal state is for my goal in that moment in time,
moment in time, >> but it can very easily, frankly, you
>> but it can very easily, frankly, you know, it can talk to me, but it can also
know, it can talk to me, but it can also know how my state of my body right now
know how my state of my body right now and what is going, you know, it's if
and what is going, you know, it's if it's 1:00 a.m. and I really need to work
it's 1:00 a.m. and I really need to work on a paper.
on a paper. >> You you know, my house should not get
>> You you know, my house should not get cold, but it also should be very, it
cold, but it also should be very, it should
should >> for me it shouldn't. I know for some
>> for me it shouldn't. I know for some people it should.
people it should. >> Yeah. My my eight sleep mattress, which
>> Yeah. My my eight sleep mattress, which I love, love, love. And yes, they're a
I love, love, love. And yes, they're a podcast sponsor, but I would use one
podcast sponsor, but I would use one anyway. It knows what temperature
anyway. It knows what temperature adjustments need to be made,
adjustments need to be made, >> right,
>> right, >> across the course of the night. I put in
>> across the course of the night. I put in what I think it it is best, but it's
what I think it it is best, but it's updating all the time now because it has
updating all the time now because it has updating sensors, like dynamically
updating sensors, like dynamically updating sensors. I'm getting close to
updating sensors. I'm getting close to two hours of REM sleep a night, which is
two hours of REM sleep a night, which is outrageously good for me.
outrageously good for me. >> Much more deep sleep, and that's a
>> Much more deep sleep, and that's a little micro environment. You're talking
little micro environment. You're talking about integrating that into an entire
about integrating that into an entire home environment.
home environment. >> Home vehicle. Yes. Because it needs to
>> Home vehicle. Yes. Because it needs to treat me as a dynamic time series. It
treat me as a dynamic time series. It needs to understand the context of
needs to understand the context of everything that's driving my state
everything that's driving my state internally. There's everything that's
internally. There's everything that's driving my state in my local
driving my state in my local environment, meaning my home or my car.
environment, meaning my home or my car. And then there's what's driving my state
And then there's what's driving my state externally, my in from, you know, my
externally, my in from, you know, my external environment. And we're in a
external environment. And we're in a place where those things are rarely
place where those things are rarely treated, you know, interacting together
treated, you know, interacting together for the optimization and the, you know,
for the optimization and the, you know, the dynamic interactions that happen.
the dynamic interactions that happen. But we can know these things. We can
But we can know these things. We can know so much about the human state from
know so much about the human state from non-cont sensors.
non-cont sensors. >> Yeah. And we're right at the point where
>> Yeah. And we're right at the point where the sensors can start to feed
the sensors can start to feed information to AI to be able to deliver
information to AI to be able to deliver what effectively again a lower level
what effectively again a lower level example would be like the the cooling
example would be like the the cooling the dynamically cooling mattress or
the dynamically cooling mattress or dynamically heating mattress. Like I
dynamically heating mattress. Like I discovered through the AI that my
discovered through the AI that my mattress was applying that and I was
mattress was applying that and I was told that heating your sleep environment
told that heating your sleep environment toward the end of the night
toward the end of the night >> yes
>> yes >> increases your REM sleep dramatically
>> increases your REM sleep dramatically whereas cooling it at the beginning of
whereas cooling it at the beginning of the night increases your deep sleep has
the night increases your deep sleep has been immensely beneficial for me to be
been immensely beneficial for me to be able to shorten my total sleep need
able to shorten my total sleep need which is something that for me is like
which is something that for me is like awesome because I I like sleep a lot but
awesome because I I like sleep a lot but I don't want to need to sleep so much in
I don't want to need to sleep so much in order to feel great. Well, you you want
order to feel great. Well, you you want to have your own choice about how you
to have your own choice about how you sleep. Yeah. Given the date, it's
sleep. Yeah. Given the date, it's helping you have that.
helping you have that. >> Sometimes I have six hours, sometimes I
>> Sometimes I have six hours, sometimes I have eight hours, this kind of thing.
have eight hours, this kind of thing. >> Here's where I'm I get stuck and I've
>> Here's where I'm I get stuck and I've been wanting to have a conversation
been wanting to have a conversation about this with someone, ideally a
about this with someone, ideally a neuroscientist who's interested in
neuroscientist who's interested in building technologies for a very long
building technologies for a very long time. So, I feel like this moment is a
time. So, I feel like this moment is a moment I've been waiting for for a very
moment I've been waiting for for a very long time, which is the following. I'm
long time, which is the following. I'm hoping you can solve this for all of us,
hoping you can solve this for all of us, Bobby.
Bobby. >> We're talking about sleep and we know a
>> We're talking about sleep and we know a lot about sleep. You got slow wave
lot about sleep. You got slow wave sleep, deep sleep, growth hormone
sleep, deep sleep, growth hormone release at the beginning of the night.
release at the beginning of the night. You have less metabolic need then. Then
You have less metabolic need then. Then you have rapid eye movement sleep which
you have rapid eye movement sleep which consolidates learning from the previous
consolidates learning from the previous day. It removes the emotional load of
day. It removes the emotional load of previous day experiences. We can make
previous day experiences. We can make temperature adjustments. You do all
temperature adjustments. You do all these things. Avoid caffeine too late in
these things. Avoid caffeine too late in the day. Lots of things to optimize
the day. Lots of things to optimize these known states that occupy this
these known states that occupy this thing that we call sleep. And AI and
thing that we call sleep. And AI and technology is, I would say, is doing a
technology is, I would say, is doing a really great job, as is pharmarmacology,
really great job, as is pharmarmacology, to try and enhance sleep. Sleep's
to try and enhance sleep. Sleep's getting better. We're getting better at
getting better. We're getting better at sleeping despite more forces um uh
sleeping despite more forces um uh potentially disrupting our sleep,
potentially disrupting our sleep, >> like smartphones and noise and city
>> like smartphones and noise and city noise, etc. Okay,
noise, etc. Okay, >> here's the big problem in my mind is
>> here's the big problem in my mind is that we have very little understanding
that we have very little understanding or even names for different awake
or even names for different awake states. We have names for the goal like
states. We have names for the goal like I want to be able to work. Okay, what's
I want to be able to work. Okay, what's work? What kind of work? Uh I want to
work? What kind of work? Uh I want to write a chapter of a book. What kind of
write a chapter of a book. What kind of book? A non-fiction book based on what?
book? A non-fiction book based on what? But like we don't we talk about alpha,
But like we don't we talk about alpha, beta waves, theta waves, but I feel like
beta waves, theta waves, but I feel like as neuroscientists, we have done a
as neuroscientists, we have done a pretty poor job as a field of defining
pretty poor job as a field of defining different states of wakefulness. And so
different states of wakefulness. And so the like the technology AI and other
the like the technology AI and other technologies are don't really have they
technologies are don't really have they don't know what to to shoot for. They
don't know what to to shoot for. They don't know what to help us optimize for.
don't know what to help us optimize for. Whereas with slow wave sleep and REM
Whereas with slow wave sleep and REM sleep like we've got it. I ask questions
sleep like we've got it. I ask questions of myself all the time like is my brain
of myself all the time like is my brain and what it requires in the first three
and what it requires in the first three hours of the day anything like what my
hours of the day anything like what my brain requires in the last three hours
brain requires in the last three hours of the day if I want to work in each one
of the day if I want to work in each one of those three-hour compartments. like
of those three-hour compartments. like and so I think like we don't really
and so I think like we don't really understand
understand what to try and uh adjust to. So here's
what to try and uh adjust to. So here's my question. Do you think AI could help
my question. Do you think AI could help us understand the different states that
us understand the different states that our brain and body go through during the
our brain and body go through during the daytime?
daytime? Give us some understanding of what those
Give us some understanding of what those are in terms of body temperature, focus
are in terms of body temperature, focus ability, etc. And then help us optimize
ability, etc. And then help us optimize for those the same way that we optimize
for those the same way that we optimize for sleep. Because whether it's a
for sleep. Because whether it's a conversation with your therapist,
conversation with your therapist, whether or not it's a podcast, whether
whether or not it's a podcast, whether or not it's playing with your kids,
or not it's playing with your kids, whether or not it's Netflix and chill,
whether or not it's Netflix and chill, whatever it is, the the goal and what
whatever it is, the the goal and what people have spent so much time, energy,
people have spent so much time, energy, money, etc. And whether or not they're
money, etc. And whether or not they're drinking alcohol, caffeine, taking rolin
drinking alcohol, caffeine, taking rolin or aderall, or running or what, like
or aderall, or running or what, like humans have have spent their entire
humans have have spent their entire existence trying to build technologies
existence trying to build technologies to get better at doing the things that
to get better at doing the things that they need to do. And yet we still don't
they need to do. And yet we still don't really understand waking states. So can
really understand waking states. So can AI
AI teach it to us? Can AI teach teach us a
teach it to us? Can AI teach teach us a goal that we don't even know we have?
goal that we don't even know we have? >> Can AI teach it to us? I would say AI is
>> Can AI teach it to us? I would say AI is part of the story. But before we get AI,
part of the story. But before we get AI, we need better more data. Not just me,
we need better more data. Not just me, right? So maybe I am very focused right
right? So maybe I am very focused right now, but without my belief and this is
now, but without my belief and this is my perspective is imagine I I'm very
my perspective is imagine I I'm very focused right now. I need to know the
focused right now. I need to know the context of my environment that's driving
context of my environment that's driving that. Like what are what what's in that
that. Like what are what what's in that environment? Is it internal focus that's
environment? Is it internal focus that's gotten me there? What what is my
gotten me there? What what is my environment? What is that external
environment? What is that external environment? So the understanding my
environment? So the understanding my awake state for me is very dependent on
awake state for me is very dependent on the data and interactions that happen
the data and interactions that happen from these different environments. Let
from these different environments. Let me give an example like if I'm in my
me give an example like if I'm in my home or I'm in a say I'm in a vehicle,
home or I'm in a say I'm in a vehicle, all right, and you are measuring
all right, and you are measuring information about me and you know I'm
information about me and you know I'm under stress or you know I'm uh
under stress or you know I'm uh experiencing joy or I'm or heightens
experiencing joy or I'm or heightens attention right now. Some different
attention right now. Some different states you may want to
states you may want to uh have my home or my system react to
uh have my home or my system react to mitigate. Well, like if you get sleepy
mitigate. Well, like if you get sleepy in a self-driving in in a smart vehicle,
in a self-driving in in a smart vehicle, >> it will make adjustments
>> it will make adjustments >> potentially. It will make adjustments,
>> potentially. It will make adjustments, but not necessarily right for you.
but not necessarily right for you. That's an important part is optimizing
That's an important part is optimizing for you personalization and how a system
for you personalization and how a system responds. And you know, it can make
responds. And you know, it can make adjust any home, an HVAC system or the
adjust any home, an HVAC system or the the internal state of a vehicle is going
the internal state of a vehicle is going to adjust, you know, sound, background
to adjust, you know, sound, background sound, music. It's going to adjust, you
sound, music. It's going to adjust, you know, whatever whether it can haptic
know, whatever whether it can haptic feedback, temperature, lighting, you
feedback, temperature, lighting, you know, any number of, you know, position
know, any number of, you know, position of your, you know, your chair dynamics
of your, you know, your chair dynamics of what's in your space. All of these
of what's in your space. All of these different systems in my home or my my
different systems in my home or my my other
other what what my vehicle if it or some other
what what my vehicle if it or some other system can react, right? But the
system can react, right? But the important thing is how you react is
important thing is how you react is going to shift me. And the goal is to
going to shift me. And the goal is to not measure me but to
not measure me but to actually intersect with my state and
actually intersect with my state and move it in some direction right some
move it in some direction right some >> yeah I always think of devices as good
>> yeah I always think of devices as good at measurement or uh modification
at measurement or uh modification >> right
>> right >> measurement or modification measurement
>> measurement or modification measurement is critical and that's yeah meas but
is critical and that's yeah meas but measurement not just of my me but also
measurement not just of my me but also of like my environment and understanding
of like my environment and understanding of the external environment this is
of the external environment this is where like things like Earth observation
where like things like Earth observation and understanding, you know, we're
and understanding, you know, we're getting to a place where we're getting
getting to a place where we're getting uh image, you know, really good image
uh image, you know, really good image quality data from sat the the satellites
quality data from sat the the satellites that are going in the sky at at much
that are going in the sky at at much lower um uh
lower um uh lower distances so that you now have,
lower distances so that you now have, you know, faster reaction times between
you know, faster reaction times between technologies and the information they
technologies and the information they have to understand and be dynamic with
have to understand and be dynamic with them. Right? Can you give me an example
them. Right? Can you give me an example where that impacts everyday life? Are we
where that impacts everyday life? Are we talking about like weather analysis?
talking about like weather analysis? >> Sure. Weather predictions, uh, car
>> Sure. Weather predictions, uh, car environ, you know, things happening.
environ, you know, things happening. >> And what about traffic? Why haven't they
>> And what about traffic? Why haven't they solved traffic yet given all the
solved traffic yet given all the knowledge of of um object flow and how
knowledge of of um object flow and how to optimize for object flow? And we've
to optimize for object flow? And we've got satellites that can basically look
got satellites that can basically look at at traffic and I mean and open up
at at traffic and I mean and open up roads dynamically like change number of
roads dynamically like change number of lanes. What why isn't that happening?
lanes. What why isn't that happening? The traffic problem gets resolved when
The traffic problem gets resolved when you have autonomous vehicles in ways
you have autonomous vehicles in ways that don't have like the the human side
that don't have like the the human side of things.
of things. >> That gets resolved.
>> That gets resolved. >> It does like
>> It does like >> autonomous vehicles.
>> autonomous vehicles. >> Only autonomous vehicles. You would
>> Only autonomous vehicles. You would probably you don't have traffic in the
probably you don't have traffic in the ways that you do with
ways that you do with >> goodness. That's reason alone.
>> goodness. That's reason alone. >> That's reason alone to to shift to
>> That's reason alone to to shift to autonomous vehicles.
autonomous vehicles. >> It is that injection from human the
>> It is that injection from human the human system that you know is
human system that you know is interrupting all the models. I think the
interrupting all the models. I think the world right now we think about wearables
world right now we think about wearables a lot. Wearables track us. You have
a lot. Wearables track us. You have smart mattresses um which are wonderful
smart mattresses um which are wonderful for understanding. So there's so much
for understanding. So there's so much you learn while you know from a smart
you learn while you know from a smart mattress and ways of also both measuring
mattress and ways of also both measuring as well as intervening to optimize your
as well as intervening to optimize your sleep which is the beauty uh and it's
sleep which is the beauty uh and it's this nice incredible period of time
this nice incredible period of time where you can measure so many things. Um
where you can measure so many things. Um but you know in our home so I was I use
but you know in our home so I was I use the example of a thermostat right? it
the example of a thermostat right? it it's pretty, you know, frankly dumb
it's pretty, you know, frankly dumb about what my goals are or what I'm
about what my goals are or what I'm trying to do at that moment in time, but
trying to do at that moment in time, but it doesn't have to be. And there are,
it doesn't have to be. And there are, you know, there's a company, Passive
you know, there's a company, Passive Logic. I love them. Uh they actually
Logic. I love them. Uh they actually have, I think, some of the smartest uh
have, I think, some of the smartest uh digital twin HVAC systems, but you know,
digital twin HVAC systems, but you know, their sensors measure things like sound.
their sensors measure things like sound. They measure carbon dioxide, uh your
They measure carbon dioxide, uh your carbon, your CO2 levels, like when when
carbon, your CO2 levels, like when when we breathe, we give off CO2, you know.
we breathe, we give off CO2, you know. So imagine, you know, there's a dynamic
So imagine, you know, there's a dynamic mixture of acetone, isoprene, and carbon
mixture of acetone, isoprene, and carbon dioxide that's constantly exchanging
dioxide that's constantly exchanging when my, you know, when I get stressed
when my, you know, when I get stressed or when I'm feeling, you know, happiness
or when I'm feeling, you know, happiness or suspense in my my in my state. And
or suspense in my my in my state. And that dynamic sort of cocktail mixture
that dynamic sort of cocktail mixture that's in my breath is both an indicator
that's in my breath is both an indicator of my state, but it's also something
of my state, but it's also something that, you know, it's just the spaces
that, you know, it's just the spaces around me, you know, have more
around me, you know, have more information to contribute about how I'm
information to contribute about how I'm feeling and can also be part of that
feeling and can also be part of that solution in ways that don't I don't have
solution in ways that don't I don't have to have things on my body, right? So, I
to have things on my body, right? So, I have sensors now that can measure CO2.
have sensors now that can measure CO2. You can watch my TED talk. I have given
You can watch my TED talk. I have given examples. We brought people in when I
examples. We brought people in when I when I was at Dolby and had um had them
when I was at Dolby and had um had them watching Free Solo, you know, the Alex
watching Free Solo, you know, the Alex Hold movie where they're climbing LCAP
Hold movie where they're climbing LCAP >> stressful.
>> stressful. >> So carbon dioxide's heavier than air. So
>> So carbon dioxide's heavier than air. So we can measure we could measure carbon
we can measure we could measure carbon dioxide from s, you know, just tubes on
dioxide from s, you know, just tubes on the ground and you could get the
the ground and you could get the real-time differential of CO2 in there.
real-time differential of CO2 in there. And
And >> were they scared throughout?
>> were they scared throughout? >> No. Well, but it's I mean I like to say
>> No. Well, but it's I mean I like to say we broadcast how we're feeling, right?
we broadcast how we're feeling, right? And we do that wherever we are. And in
And we do that wherever we are. And in this uh you could look at the time
this uh you could look at the time series of carbon dioxide levels and be
series of carbon dioxide levels and be able to you know know what what was
able to you know know what what was happening in the film or in the movie
happening in the film or in the movie without actually having it annotated.
without actually having it annotated. You could tell where he summited where
You could tell where he summited where he had to abandon his climb where he
he had to abandon his climb where he hurt his ankle.
hurt his ankle. >> Absolutely. There's another study I
>> Absolutely. There's another study I forget who the authors are and they're
forget who the authors are and they're you know they've got different audiences
you know they've got different audiences watching Hunger Games and you know
watching Hunger Games and you know different days different people you can
different days different people you can tell exactly where Katniss's dress
tell exactly where Katniss's dress catches on fire and uh you know it's
catches on fire and uh you know it's like we really are sort of you know it's
like we really are sort of you know it's like digital exhaust of how we're
like digital exhaust of how we're feeling but you know and and our
feeling but you know and and our thermals we you know radiate the things
thermals we you know radiate the things we're feeling um I'm very um bullish on
we're feeling um I'm very um bullish on the power of you know our eye or in in
the power of you know our eye or in in representing our cognitive load our
representing our cognitive load our stressors
stressors >> our Okay.
>> our Okay. >> Our eye. Yes. Like the diameter.
>> Our eye. Yes. Like the diameter. >> Our eye.
>> Our eye. >> Our
>> Our >> Yeah. Our eye. Sorry. Our our literally
>> Yeah. Our eye. Sorry. Our our literally our eyes. Our pupil pupil size.
our eyes. Our pupil pupil size. >> Yes. Yes. Yes. I you know back when I
>> Yes. Yes. Yes. I you know back when I was a physiologist I always you were
was a physiologist I always you were I've worked with a lot of species on in
I've worked with a lot of species on in you know understanding information
you know understanding information processing internally in cells but also
processing internally in cells but also then I you would very often use
then I you would very often use pupilometry as an indicator of you know
pupilometry as an indicator of you know perceptual engagement and experience.
perceptual engagement and experience. >> Yeah. Bigger pupil mean more arousal
>> Yeah. Bigger pupil mean more arousal higher levels of alertness.
higher levels of alertness. >> Yeah. more arousal, cognitive load or
>> Yeah. more arousal, cognitive load or you know obviously lighting changes but
you know obviously lighting changes but the the thing that's changing from you
the the thing that's changing from you know
know >> 20 years ago 15 years ago it was very
>> 20 years ago 15 years ago it was very expensive to track the kind of
expensive to track the kind of resolution and data to you know leverage
resolution and data to you know leverage all of those autonomic nervous system
all of those autonomic nervous system you know deterministic responses because
you know deterministic responses because those ones are deterministic and not
those ones are deterministic and not probabilistic right those are the ones
probabilistic right those are the ones that it's like the body's reacting even
that it's like the body's reacting even if the brain doesn't say anything about
if the brain doesn't say anything about >> detection and uh but Today we can do
>> detection and uh but Today we can do that with I mean do it well we can do it
that with I mean do it well we can do it right now with a you know open source
right now with a you know open source software on our laptops or our mobile
software on our laptops or our mobile devices right and every pair of smart
devices right and every pair of smart glasses will be tracking this
glasses will be tracking this information when we wear them uh so it
information when we wear them uh so it is becomes a channel of data and you
is becomes a channel of data and you know you it may be an ambiguous
know you it may be an ambiguous signature in the sense that there's you
signature in the sense that there's you know changes in lighting there's changes
know changes in lighting there's changes am I aroused or am I
am I aroused or am I >> those can be adjusted for right like if
>> those can be adjusted for right like if you you can you can literally take a
you you can you can literally take a measurement wear eyeglasses that are
measurement wear eyeglasses that are measuring pupil size.
measuring pupil size. >> Um, the eyeglasses could have a sensor
>> Um, the eyeglasses could have a sensor that detects levels of illumination in
that detects levels of illumination in the room
the room >> at the level of my eyes.
>> at the level of my eyes. >> Um, it could measure how dynamic that is
>> Um, it could measure how dynamic that is and we just make that the denominator in
and we just make that the denominator in a fraction, right? And then we just look
a fraction, right? And then we just look at changes in pupil size as the
at changes in pupil size as the numerator in that fraction, right? Um,
numerator in that fraction, right? Um, more or less you just have to have other
more or less you just have to have other sensors.
sensors. >> All you need to do is cancel. So as as
>> All you need to do is cancel. So as as you walk from a shadowed area to a
you walk from a shadowed area to a brighter area, sure the pupil size
brighter area, sure the pupil size changes, but then you can adjust for
changes, but then you can adjust for that change, right? just like normalize
that change, right? just like normalize for that and you end up with an index of
for that and you end up with an index of arousal,
arousal, >> right?
>> right? >> Which is amazing. You could also use the
>> Which is amazing. You could also use the index of of illumination as a useful
index of of illumination as a useful measure of like are you getting uh
measure of like are you getting uh compared to your vitamin D levels uh to
compared to your vitamin D levels uh to your levels of maybe you need more
your levels of maybe you need more illumination in order to get more
illumination in order to get more arousal. Like it could tell all of this.
arousal. Like it could tell all of this. It could literally say hey take a
It could literally say hey take a 5minute walk outside in to the left
5minute walk outside in to the left after work and you will um get your your
after work and you will um get your your require your photon requirement for the
require your photon requirement for the day. you know, this kind of thing, not
day. you know, this kind of thing, not just measuring steps. All this stuff is
just measuring steps. All this stuff is possible now.
possible now. >> I just don't know why it's not being
>> I just don't know why it's not being integrated into single devices more
integrated into single devices more quickly
quickly >> because you'd love to also know that
>> because you'd love to also know that person's blood sugar instead of like
person's blood sugar instead of like drawing their blood, taking it down to
drawing their blood, taking it down to like you think in the with with the
like you think in the with with the resident that's been up for for 13 hours
resident that's been up for for 13 hours because that's the standard in the field
because that's the standard in the field and they're making mistakes on a on a on
and they're making mistakes on a on a on a chart. It's like I think at some point
a chart. It's like I think at some point we're just going to go I can't believe
we're just going to go I can't believe we used to do it that way. It's crazy.
we used to do it that way. It's crazy. >> Yeah. No, and it's a lot of the consumer
>> Yeah. No, and it's a lot of the consumer devices and just computation we can do
devices and just computation we can do from you know whether it's cameras or
from you know whether it's cameras or excalent or you know other data in our
excalent or you know other data in our environments that tell us about our
environments that tell us about our physical state and some of these
physical state and some of these situations that you're talking about a
situations that you're talking about a lot of the I mean why isn't it happening
lot of the I mean why isn't it happening a lot of reasons are simply the
a lot of reasons are simply the regulatory process is antiquated and not
regulatory process is antiquated and not up to keeping up with the acceleration
up to keeping up with the acceleration of innovation that's happening you know
of innovation that's happening you know getting things through the FDA even if
getting things through the FDA even if they're you deemed uh you know in the
they're you deemed uh you know in the same ballpark and supposed to move fast.
same ballpark and supposed to move fast. you know, uh, with the regulatory costs
you know, uh, with the regulatory costs and processes is really high. And
and processes is really high. And >> you know you end up many years you know
>> you know you end up many years you know down the road from when the capability
down the road from when the capability and the data and technology actually you
and the data and technology actually you know should have arisen to be used in a
know should have arisen to be used in a hospital or to be used in a place where
hospital or to be used in a place where you actually have that kind of
you actually have that kind of appreciation for the data you know
appreciation for the data you know appreci and use. The consumer grade
appreci and use. The consumer grade devices for tracking of data of our
devices for tracking of data of our biological processes are on par and in
biological processes are on par and in many cases surpassed the medical grade
many cases surpassed the medical grade devices. And that's because they they
devices. And that's because they they just have but then they will have to
just have but then they will have to bill what they do and what they're
bill what they do and what they're tracking in some way that is consumer
tracking in some way that is consumer you know is not making the medical
you know is not making the medical claims to allow them to be able to be
claims to allow them to be able to be you know continue to move forward in
you know continue to move forward in those spaces. But there's no question
those spaces. But there's no question that that's that's a big part of what
that that's that's a big part of what can you know holds back the uh
can you know holds back the uh availability of a lot of these devices
availability of a lot of these devices and capabilities.
and capabilities. I'd like to take a quick break and
I'd like to take a quick break and acknowledge one of our sponsors,
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to get early access to function. Okay, so I agree that we need more data and
so I agree that we need more data and that there are a lot of different
that there are a lot of different sensors out there that can measure blood
sensors out there that can measure blood glucose and sleep and um temperature and
glucose and sleep and um temperature and breathing and all sorts of things, which
breathing and all sorts of things, which raises the question of are we going to
raises the question of are we going to need tons of sensors? I mean, are we
need tons of sensors? I mean, are we going to be just wrapped in sensors as
going to be just wrapped in sensors as clothing?
clothing? Are we going to be wearing 12 watches?
Are we going to be wearing 12 watches? Uh what's this going to look like?
Uh what's this going to look like? >> I'm an advocate for fewer things on, you
>> I'm an advocate for fewer things on, you know, not having all this stuff on our
know, not having all this stuff on our bodies. I'm, you know, there's so much
bodies. I'm, you know, there's so much we can get out of the computer vision
we can get out of the computer vision side, you know, from how, you know, the
side, you know, from how, you know, the cameras in our spaces and how they're
cameras in our spaces and how they're supporting us in our rooms, in our the
supporting us in our rooms, in our the sensors on our in our um you know, I
sensors on our in our um you know, I brought up HVAC systems earlier. So now
brought up HVAC systems earlier. So now you've got you effectively a digital
you've got you effectively a digital twin that's track, you know, and sensors
twin that's track, you know, and sensors that are tracking my metabolic rates
that are tracking my metabolic rates just in my space. They're tracking uh
just in my space. They're tracking uh carbon dioxide. They're tracking sound.
carbon dioxide. They're tracking sound. You're getting context because of that.
You're getting context because of that. You're getting intelligence. And now
You're getting intelligence. And now you're able to start having more
you're able to start having more information from, you know, what's
information from, you know, what's happening in my environment. The same is
happening in my environment. The same is true in my my vehicle. You can tell how
true in my my vehicle. You can tell how I'm whether I'm stressed or how I'm
I'm whether I'm stressed or how I'm feeling just by the posture I have it
feeling just by the posture I have it sitting in my car, right? And you need
sitting in my car, right? And you need AI. This is AI interpretation of data.
AI. This is AI interpretation of data. But what's driving that posture might be
But what's driving that posture might be coming from also an understanding of
coming from also an understanding of what else is happening in that
what else is happening in that environment. So it's suddenly this con
environment. So it's suddenly this con with contextual intelligence uh AIdriven
with contextual intelligence uh AIdriven understanding of what's happening in
understanding of what's happening in that space that's driving you know the
that space that's driving you know the state of me and how do I you know I keep
state of me and how do I you know I keep leaning to the side because I'm talking
leaning to the side because I'm talking thinking about you know my the way I
thinking about you know my the way I move and sit is you know it's a proxy
move and sit is you know it's a proxy for what's actually happening inside me
for what's actually happening inside me and then you've also got data around me
and then you've also got data around me coming from my environment what's
coming from my environment what's happening you know if I'm driving a car
happening you know if I'm driving a car or what's happening in my home in my you
or what's happening in my home in my you know in in the weather in not threats
know in in the weather in not threats that might be outside in noise that's
that might be outside in noise that's happening not inside the space but
happening not inside the space but things that give context to have more
things that give context to have more intelligence with the systems we have so
intelligence with the systems we have so I'm a a huge believer in you don't we
I'm a a huge believer in you don't we aren't anywhere until we have
aren't anywhere until we have integration of those systems between the
integration of those systems between the body the local environment and the
body the local environment and the external environment And we're finally
external environment And we're finally at a place where AI can help us start
at a place where AI can help us start integrating that data. Um, in terms of
integrating that data. Um, in terms of wearables though, uh, you so obviously
wearables though, uh, you so obviously some of the big companies, we've got the
some of the big companies, we've got the watch we have on our hand has a lot of
watch we have on our hand has a lot of information that is very relevant to our
information that is very relevant to our bodies. Um the devices we put in our
bodies. Um the devices we put in our ears. You may not realize but you know a
ears. You may not realize but you know a dimesized patch in your in in your consc
dimesized patch in your in in your consc we can use we can know heart rate pul
we can use we can know heart rate pul blood oxygen level uh because of the the
blood oxygen level uh because of the the electrical signature that your eye
electrical signature that your eye produces when it moves back and forth.
produces when it moves back and forth. we can know what you're looking at just
we can know what you're looking at just you know in from uh measuring a
you know in from uh measuring a signature measuring um your um
signature measuring um your um electrocul ocular in your ear we can
electrocul ocular in your ear we can measure EEG electronogs you can also get
measure EEG electronogs you can also get you know eye movements out of
you know eye movements out of electronograms but you can get attention
electronograms but you can get attention you can know what people are attending
you can know what people are attending to based on signatures in their ear so
to based on signatures in their ear so our earbuds you know that become sort of
our earbuds you know that become sort of a window to our state um and you've got
a window to our state um and you've got a number of companies working on that
a number of companies working on that right now. Uh, you know, so do we need
right now. Uh, you know, so do we need to wear lots of different sensors? No.
to wear lots of different sensors? No. Do we need to have the sensors, the data
Do we need to have the sensors, the data we have, whether it's on our bodies or
we have, whether it's on our bodies or off our bodies, be able to, you know,
off our bodies, be able to, you know, work together and not be proprietary to
work together and not be proprietary to just one company, but to be able to
just one company, but to be able to integrate great with other companies.
integrate great with other companies. That that becomes really important. You
That that becomes really important. You need integrative systems so that the the
need integrative systems so that the the data they have can interact with the
data they have can interact with the systems that surround surround you or
systems that surround surround you or surround my spaces or the mattress I'm
surround my spaces or the mattress I'm sleeping on. Right.
sleeping on. Right. >> Um because you've had a lot of specialty
>> Um because you've had a lot of specialty of design come from different developers
of design come from different developers and that's partly been a product of
and that's partly been a product of again the the FDA and the regulatory
again the the FDA and the regulatory pathways because of the cost of
pathways because of the cost of development. It tends to move companies
development. It tends to move companies towards specialization unless they're
towards specialization unless they're very large.
very large. >> But where we're at today is you're
>> But where we're at today is you're going, you know, we're getting to a
going, you know, we're getting to a point where you're going to start seeing
point where you're going to start seeing a lot of this data get integrated. I I
a lot of this data get integrated. I I think and and by all means, hopefully
think and and by all means, hopefully we're not going to be wearing a lot of
we're not going to be wearing a lot of things on our bodies. I sure as heck
things on our bodies. I sure as heck won't. You know, the more we put on our
won't. You know, the more we put on our bodies, it affects our gate. It affects
bodies, it affects our gate. It affects it has ramifications in so many
it has ramifications in so many different ways. Uh when I got here, I
different ways. Uh when I got here, I was talking to some of the people that
was talking to some of the people that work with you and they're like, "Well,
work with you and they're like, "Well, what what wearables do you wear?" And I
what what wearables do you wear?" And I actually don't wear many at all. And you
actually don't wear many at all. And you know, I I have worn rings, I've worn
know, I I have worn rings, I've worn watches at different times, but for me,
watches at different times, but for me, the importance is the point at which I
the importance is the point at which I get insights that, you know, I am a big
get insights that, you know, I am a big believer in um as little on my body as
believer in um as little on my body as possible when it comes to wearables. One
possible when it comes to wearables. One interesting company that I think is uh
interesting company that I think is uh worth mentioning is Pyson. and Python,
worth mentioning is Pyson. and Python, you know, again, they've got a form
you know, again, they've got a form factor that's, you know, like a Timex
factor that's, you know, like a Timex watch or they're partnered with Timex,
watch or they're partnered with Timex, but they're measuring um are you
but they're measuring um are you familiar with Python?
familiar with Python? >> No.
>> No. >> Okay. So, they're ma measuring psycho
>> Okay. So, they're ma measuring psycho motor vigilance. So you know really
motor vigilance. So you know really trying to understand it's like a ENG
trying to understand it's like a ENG electron neurom modulation and they're
electron neurom modulation and they're trying to understand fatigue and and
trying to understand fatigue and and neural attentiveness
neural attentiveness in a way that is you know continuous and
in a way that is you know continuous and useful for say high-risk operations or
useful for say high-risk operations or uh training uh you whether be it in
uh training uh you whether be it in sport but what I like about it is it's
sport but what I like about it is it's actually trying to get at a higher level
actually trying to get at a higher level cognitive state from the biometrics or
cognitive state from the biometrics or the that you're measuring. And that to
the that you're measuring. And that to me is an exciting really exciting
me is an exciting really exciting direction is when you're actually doing
direction is when you're actually doing something that you could make a decision
something that you could make a decision about how I engage in my work or how I
about how I engage in my work or how I engage in my training or my life based
engage in my training or my life based on that data about my cognitive state
on that data about my cognitive state and how effective I'm going to be.
and how effective I'm going to be. >> And then I can start associating that
>> And then I can start associating that data with the other data to make better
data with the other data to make better to have better decisions, better
to have better decisions, better insights at a certain point in time. And
insights at a certain point in time. And that becomes that's really your digital
that becomes that's really your digital twin.
twin. >> It's interesting earlier you said you
>> It's interesting earlier you said you don't like the word gamification.
don't like the word gamification. >> But um one thing that I think has really
>> But um one thing that I think has really been effective in the sleep space has
been effective in the sleep space has been this notion of a sleep score where
been this notion of a sleep score where people aspire to get a high sleep score.
people aspire to get a high sleep score. >> Um and if they don't they don't see that
>> Um and if they don't they don't see that as a um a disparagement of them but
as a um a disparagement of them but rather that they need to adjust their
rather that they need to adjust their behavior. So, it's not like, oh, I'm a
behavior. So, it's not like, oh, I'm a terrible sleeper and I'll never be a
terrible sleeper and I'll never be a good sleeper. It gives them something to
good sleeper. It gives them something to aspire to on a night by basis.
aspire to on a night by basis. >> Yes.
>> Yes. >> And I feel like that's been pretty
>> And I feel like that's been pretty effective. When I say gamification, I
effective. When I say gamification, I don't necessarily mean competitive uh
don't necessarily mean competitive uh with others, but I mean um encouraging
with others, but I mean um encouraging of oneself, right? So I could imagine uh
of oneself, right? So I could imagine uh this showing up in other domains too um
this showing up in other domains too um for wakeful states like you know like I
for wakeful states like you know like I spend the I had very few highly
spend the I had very few highly distracted you know work bouts or
distracted you know work bouts or something like that like I'd love to
something like that like I'd love to know at the end of my day I had three
know at the end of my day I had three really solid work bouts
really solid work bouts >> um of an hour each at least um that
>> um of an hour each at least um that would feel good like that was day well
would feel good like that was day well spent even if you know I didn't
spent even if you know I didn't accomplish what I wanted to in its
accomplish what I wanted to in its entirety like I I put in some really
entirety like I I put in some really good solid work. Right now, it's all
good solid work. Right now, it's all very subjective. Uh we know that
very subjective. Uh we know that gamification of steps was very effective
gamification of steps was very effective as a public messaging. You know, 10,000
as a public messaging. You know, 10,000 steps a day. We now know you want to get
steps a day. We now know you want to get somewhere exceeding 7,000 as a
somewhere exceeding 7,000 as a threshold. But if you think about it, we
threshold. But if you think about it, we could have just as easily said, hey, you
could have just as easily said, hey, you want to walk at a at a reasonable pace
want to walk at a at a reasonable pace for you for 30 minutes per day. But
for you for 30 minutes per day. But somehow the counting steps thing was
somehow the counting steps thing was more effective because people I know who
more effective because people I know who are not fanatic about exercise at all
are not fanatic about exercise at all will tell me I make sure I get my 11,000
will tell me I make sure I get my 11,000 steps per day. Like people tell me this.
steps per day. Like people tell me this. I'm like oh okay. Like so apparently
I'm like oh okay. Like so apparently it's a meaningful thing for people. Um
it's a meaningful thing for people. Um so I think quantification of performance
so I think quantification of performance um creates this aspirational state. Mhm.
um creates this aspirational state. Mhm. >> Um so I think that can be very useful
>> Um so I think that can be very useful >> data and and
>> data and and h understanding the quantification that
h understanding the quantification that you're working towards is really
you're working towards is really important. Those are, you know, summary
important. Those are, you know, summary summary statistics effectively that
summary statistics effectively that maybe they're good on some level to aim
maybe they're good on some level to aim for. If it means that people move more,
for. If it means that people move more, >> all for it, right? And it's something
>> all for it, right? And it's something that if I didn't move as much before and
that if I didn't move as much before and I didn't get up and I didn't do
I didn't get up and I didn't do something, then you know, and this is
something, then you know, and this is making me do it. That's awesome or
making me do it. That's awesome or that's great. But it's also great when
that's great. But it's also great when now through like a computer vision app I
now through like a computer vision app I can understand it's not just 10,000
can understand it's not just 10,000 steps but maybe there's you know an you
steps but maybe there's you know an you know a small battery of things I'm
know a small battery of things I'm trying to perform against that are
trying to perform against that are helping shape me neurally with the
helping shape me neurally with the feedback and the targets that I'm
feedback and the targets that I'm getting so that there's a little more
getting so that there's a little more there's more nuance towards achieving
there's more nuance towards achieving the goal I'm aiming for which is what
the goal I'm aiming for which is what I'm all about from a neuroplasticity
I'm all about from a neuroplasticity perspective. So I just don't like the
perspective. So I just don't like the word gamification. I believe everything
word gamification. I believe everything should be fun or everything training can
should be fun or everything training can be fun and gamified in some ways. Um,
be fun and gamified in some ways. Um, you know, again, like my life has been
you know, again, like my life has been predominantly in industry, but I've
predominantly in industry, but I've always, you know, I love teaching and
always, you know, I love teaching and I've always been at Stanford to, you
I've always been at Stanford to, you know, really there I try to it's it's
know, really there I try to it's it's how do I use technology and and merge it
how do I use technology and and merge it with the human system in a way that does
with the human system in a way that does help optimize learning in and training
help optimize learning in and training in a way that is from a sort of neural
in a way that is from a sort of neural circuit first perspective. you know, how
circuit first perspective. you know, how do we think about the neural system and
do we think about the neural system and use, you know, this more enjoyable,
use, you know, this more enjoyable, understandable target to to engage with
understandable target to to engage with it. One of my favorite examples though
it. One of my favorite examples though is there was a a period it was right
is there was a a period it was right around 2018 2020 and from 2018 to 2020
around 2018 2020 and from 2018 to 2020 20 and into the pandemic where you know
20 and into the pandemic where you know there became uh the students I I noticed
there became uh the students I I noticed had a much more uh
had a much more uh there there were a lot of projects their
there there were a lot of projects their final project they can build whatever
final project they can build whatever they want um and you know they've had to
they want um and you know they've had to do projects where they build neural
do projects where they build neural brain computer interfaces they've had to
brain computer interfaces they've had to build projects in VR they've had to
build projects in VR they've had to build AR projects they've had to build
build AR projects they've had to build projects that you know use um any sort
projects that you know use um any sort of input device you know they have to
of input device you know they have to use different sensor driven input
use different sensor driven input devices and that's all part of what they
devices and that's all part of what they develop and around 2018 2020 I started
develop and around 2018 2020 I started to see almost every project had a
to see almost every project had a wellness component to it which I loved I
wellness component to it which I loved I thought that was and it was a very
thought that was and it was a very notable shift in like the student body
notable shift in like the student body and maybe you've seen that too but I
and maybe you've seen that too but I still got this like one of my favorite
still got this like one of my favorite games today it was this VR game where
games today it was this VR game where I'm you in a morg. I wake up. I've got
I'm you in a morg. I wake up. I've got to solve an escape room. I've got
to solve an escape room. I've got zombies that are coming out of me and
zombies that are coming out of me and they're climbing out of the morg and
they're climbing out of the morg and they're getting closer and there's
they're getting closer and there's people breathing on my neck and you know
people breathing on my neck and you know and everything. And it's a wellness app.
and everything. And it's a wellness app. Go figure.
Go figure. It was their idea of look, this is what
It was their idea of look, this is what I feel like. I've got to because I'm
I feel like. I've got to because I'm also measuring my breath and heart rate
also measuring my breath and heart rate and I've got to keep those biological
and I've got to keep those biological signatures. like everything about how
signatures. like everything about how the zombies in solving my escape room
the zombies in solving my escape room problems, they're going to get closer to
problems, they're going to get closer to me if my breath rate goes up, if my
me if my breath rate goes up, if my heart rate goes up. I've got to keep
heart rate goes up. I've got to keep >> So, it was about stress control
>> So, it was about stress control basically.
basically. >> Exactly. Yes. But it was in that
>> Exactly. Yes. But it was in that environment and it was, you know,
environment and it was, you know, realized for them how they felt, but
realized for them how they felt, but Yeah. And you can do it in much simpler
Yeah. And you can do it in much simpler ways, but at least I I'm a huge fan of
ways, but at least I I'm a huge fan of how do we use the right quantification
how do we use the right quantification to develop the right habits, the right
to develop the right habits, the right skills, the right acuity or resolution
skills, the right acuity or resolution in a domain we might not or an area
in a domain we might not or an area where we might not be able to break it
where we might not be able to break it into the pieces we need, but it's going
into the pieces we need, but it's going to help us get there because my brain
to help us get there because my brain actually needs to now learn to uh
actually needs to now learn to uh understand that different, you know,
understand that different, you know, that sophistication. Yeah, it's clear to
that sophistication. Yeah, it's clear to me that in the health space, giving
me that in the health space, giving people information that scares them is
people information that scares them is great for getting them to not do things,
great for getting them to not do things, but it's very difficult to scare people
but it's very difficult to scare people into doing the right things. You need to
into doing the right things. You need to incentivize people do the right things
incentivize people do the right things by making it engaging and fun and
by making it engaging and fun and quantifiable and yeah. Um, you know, I
quantifiable and yeah. Um, you know, I like the example of the zombie game. Um,
like the example of the zombie game. Um, okay. So, fortunately, we won't have to
okay. So, fortunately, we won't have to wear uh dozens of sensors. Um, they'll
wear uh dozens of sensors. Um, they'll be more integrated over time. I'm I'm
be more integrated over time. I'm I'm happy to walk through a cheat sheet
happy to walk through a cheat sheet later after you know for building out
later after you know for building out like a computer vision app if if you
like a computer vision app if if you know for quantifying some of you you
know for quantifying some of you you know some of these more personalized
know some of these more personalized domain related things that people might
domain related things that people might want to do if
want to do if >> that would be awesome. Yeah. Yeah. And
>> that would be awesome. Yeah. Yeah. And then we can we can post a link to it in
then we can we can post a link to it in the show not captions because I think
the show not captions because I think that the example you gave of of you know
that the example you gave of of you know creating an app that can analyze
creating an app that can analyze swimming performance running gate focus
swimming performance running gate focus what you know focused work bouts I think
what you know focused work bouts I think that's really intriguing to a lot of
that's really intriguing to a lot of people but I think there's a at least
people but I think there's a at least for me there's a a gap there between
for me there's a a gap there between hearing about it thinking it's really
hearing about it thinking it's really cool and and how to implement. So I
cool and and how to implement. So I would certainly appreciate it. I know
would certainly appreciate it. I know the audience would too.
the audience would too. >> I mean just in
>> I mean just in >> that's very generous of you. Thank you.
>> that's very generous of you. Thank you. >> Yes. Absolutely. and and you know we're
>> Yes. Absolutely. and and you know we're in an era where everyone all you hear
in an era where everyone all you hear about is AI and AI tools and there are
about is AI and AI tools and there are tools that absolutely accelerate our
tools that absolutely accelerate our capabilities as humans but you know we
capabilities as humans but you know we we gave the examples of talking about
we gave the examples of talking about some you know some of the LLMs I mean I
some you know some of the LLMs I mean I I sat next to for we we went to Cal I
I sat next to for we we went to Cal I sat next I was at a a film premiere and
sat next I was at a a film premiere and I was sitting it there I was sitting
I was sitting it there I was sitting next to a few students who happened to
next to a few students who happened to be from Berkeley and they said to me you
be from Berkeley and they said to me you know they were computer science students
know they were computer science students and double engineering and one of them
and double engineering and one of them when he knew what I talk about or care
when he knew what I talk about or care about he's like you know I'm really
about he's like you know I'm really worried my my peer group like my peers
worried my my peer group like my peers can't start a paper without chat GPT
can't start a paper without chat GPT and you know it was a truth but it was
and you know it was a truth but it was also a concern so they understand the
also a concern so they understand the implications of what's happening and you
implications of what's happening and you know that's on one level we're in an era
know that's on one level we're in an era of agents everywhere and you know I
of agents everywhere and you know I think Reed has said that there's you
think Reed has said that there's you know a number of people have said you we
know a number of people have said you we won't we'll be using agents AI agents
won't we'll be using agents AI agents for everything at work in in the next
for everything at work in in the next five years and um some of those things
five years and um some of those things we need to use agents will accelerate um
we need to use agents will accelerate um they will accelerate capability they
they will accelerate capability they will accelerate short-term revenue but
will accelerate short-term revenue but they also will diminish workforce capab
they also will diminish workforce capab you know cognitive uh cognitive skill
you know cognitive uh cognitive skill and as a user of agents in any
and as a user of agents in any environment as a you know an owner of
environment as a you know an owner of companies employing agents you have to
companies employing agents you have to think hard about whi what the near-term
think hard about whi what the near-term and long-term ramifications. Doesn't
and long-term ramifications. Doesn't mean you don't use your agents in places
mean you don't use your agents in places where you need to, but you need to
where you need to, but you need to without the gerine cognitive load. There
without the gerine cognitive load. There there is a different dependence now that
there is a different dependence now that you have to have down the road. But also
you have to have down the road. But also you have to think about how do you how
you have to think about how do you how do you engage with the right competence
do you engage with the right competence to keep your humans that are in you know
to keep your humans that are in you know engaged with you know developing their
engaged with you know developing their cognitive skills and their gerine cognit
cognitive skills and their gerine cognit their their mental schemas to be able to
their their mental schemas to be able to support your systems down the road.
support your systems down the road. >> Let's talk more about digital twins.
>> Let's talk more about digital twins. >> Sure. Um, I don't think this concept has
>> Sure. Um, I don't think this concept has really landed uh squarely in people's
really landed uh squarely in people's minds as as like a specific thing. I
minds as as like a specific thing. I think people hear AI, they know what AI
think people hear AI, they know what AI is more or less. They hear about a
is more or less. They hear about a smartphone, they obviously know what a
smartphone, they obviously know what a smartphone is. Everyone uses one, it
smartphone is. Everyone uses one, it seems, but um, what is a digital twin? I
seems, but um, what is a digital twin? I think when people hear the word twin,
think when people hear the word twin, they think it's a twin of us. Earlier
they think it's a twin of us. Earlier you pointed out that's not necessarily
you pointed out that's not necessarily the case. It can be a useful tool for
the case. It can be a useful tool for some area of our life but it's not a
some area of our life but it's not a replica of us. Correct.
replica of us. Correct. >> Not at all in the ways that I think are
>> Not at all in the ways that I think are most relevant. Maybe you know there are
most relevant. Maybe you know there are some you know side cases that think
some you know side cases that think about that. And so like first two things
about that. And so like first two things to think about. One when I talk about
to think about. One when I talk about digital twins to companies and such I I
digital twins to companies and such I I like to frame it on um how it's being
like to frame it on um how it's being used how the immediiacy of the data from
used how the immediiacy of the data from the digital twin. So, let's go back 50
the digital twin. So, let's go back 50 years. An example of a digital twin that
years. An example of a digital twin that we still use, air traffic controllers.
we still use, air traffic controllers. When an air traffic controller sit down
When an air traffic controller sit down sits down and looks at, you know, a
sits down and looks at, you know, a screen, they're not looking at a
screen, they're not looking at a spreadsheet. They're looking at a
spreadsheet. They're looking at a digitization of information about
digitization of information about physical objects. That is meant to give
physical objects. That is meant to give them fast reaction times, make them
them fast reaction times, make them understand the landscape as effectively
understand the landscape as effectively as possible. We would call that
as possible. We would call that situational awareness. I've got to take
situational awareness. I've got to take in data about the environment around me
in data about the environment around me and I've got to be able to action on it
and I've got to be able to action on it as rapidly as quickly as possible to
as rapidly as quickly as possible to make the right decisions that mitigate
make the right decisions that mitigate any potential you know things that I you
any potential you know things that I you know are determined to be pro you know
know are determined to be pro you know problems or risks right and so that's
problems or risks right and so that's what you're trying to engage a human
what you're trying to engage a human system you know the visualization of
system you know the visualization of that data is important or doesn't have
that data is important or doesn't have to be visualization the interpretation
to be visualization the interpretation of it right and it's not the raw data
of it right and it's not the raw data it's again it's how is that data you
it's again it's how is that data you know represented you want the key
know represented you want the key information in a way that the salient
information in a way that the salient most important information in this case
most important information in this case you know about
you know about planes h is able to be acted on by that
planes h is able to be acted on by that human or even autonomous system right
human or even autonomous system right >> could you give me an example where in
>> could you give me an example where in like a more typical home environment
like a more typical home environment >> we're both into uh reefing and um you
>> we're both into uh reefing and um you know I built a aquacultured reef in my
know I built a aquacultured reef in my kitchen partly because I have a a child
kitchen partly because I have a a child and I wanted her to understand I I love
and I wanted her to understand I I love I I of it myself. So don't get that
I I of it myself. So don't get that wrong. It wasn't just all but to
wrong. It wasn't just all but to understand sort of the fragility of the
understand sort of the fragility of the ecosystems that happen in the ocean and
ecosystems that happen in the ocean and things we need to to worry about, care
things we need to to worry about, care about and and and all. And um you know
about and and and all. And um you know initially when I started and maybe you
initially when I started and maybe you know this was is not something you
know this was is not something you encountered, but when you build aqua a
encountered, but when you build aqua a reef or a reef tank and and do saltwater
reef or a reef tank and and do saltwater fish, you're uh a couple things. you're
fish, you're uh a couple things. you're doing chemical measurements by hand
doing chemical measurements by hand usually um you know weekly bi-weekly uh
usually um you know weekly bi-weekly uh there's a whole you know like 10
there's a whole you know like 10 different chemicals that you're
different chemicals that you're measuring and I would have my daughter
measuring and I would have my daughter doing that so that she would do the
doing that so that she would do the science part of it and you're trying to
science part of it and you're trying to you know you know the ranges the
you know you know the ranges the tolerances you have and you're also
tolerances you have and you're also observing this ecosystem and looking for
observing this ecosystem and looking for problems and by the time you see a
problems and by the time you see a problem you're reacting to that problem
problem you're reacting to that problem and I can tell you it was very
and I can tell you it was very unsuccessful. I mean there's lots of
unsuccessful. I mean there's lots of error and noise and human measurements.
error and noise and human measurements. There's you don't have the right
There's you don't have the right resolution of measurements. When
resolution of measurements. When resolution I mean I I'm every other you
resolution I mean I I'm every other you know every few days is not enough to
know every few days is not enough to track a problem. Uh you also have the
track a problem. Uh you also have the issue of you know you're reactive
issue of you know you're reactive instead of being proactive. It's just
instead of being proactive. It's just you're not sensing things that where
you're not sensing things that where you're the point at which it's visible
you're the point at which it's visible to you. It's probably too late to do
to you. It's probably too late to do anything about it. So if you look at my
anything about it. So if you look at my fish tank right now or my reef tank
fish tank right now or my reef tank right now um I have a number of digital
right now um I have a number of digital sensors in it. I have dashboards. I can
sensors in it. I have dashboards. I can track a huge chemical assay that is
track a huge chemical assay that is tracked in real time so that I can go
tracked in real time so that I can go back and look at the data. I can
back and look at the data. I can understand I can see oh there was a
understand I can see oh there was a water change there. Oh the the rod tank
water change there. Oh the the rod tank you my my I can tell what's happening by
you my my I can tell what's happening by looking at the data. I have you know and
looking at the data. I have you know and you know this you've got your spe the
you know this you've got your spe the spectrum of your lights is on a cycle of
spectrum of your lights is on a cycle of effect that's representative of the
effect that's representative of the environment that the corals you're
environment that the corals you're aquaculturing would you know that their
aquaculturing would you know that their their systems their deterministic
their systems their deterministic systems are looking for right and so
systems are looking for right and so you've built this ecosystem that when I
you've built this ecosystem that when I look at my dashboards I have a digital
look at my dashboards I have a digital twin of that system and it it my tank is
twin of that system and it it my tank is very stable my tank knows what's wrong
very stable my tank knows what's wrong what's happening I can look at the data
what's happening I can look at the data and understand that import an event
and understand that import an event happens somewhere that could have been
happens somewhere that could have been mitigated or some I can understand that
mitigated or some I can understand that something's wrong quickly before it even
something's wrong quickly before it even shows up.
shows up. >> It's amazing. I mean I think for people
>> It's amazing. I mean I think for people who aren't into reefing um might ask
who aren't into reefing um might ask like you know I know people that are and
like you know I know people that are and multiple people in my life are soon to
multiple people in my life are soon to have kids. Um most everybody nowadays
have kids. Um most everybody nowadays has a has a camera on the the sleeping
has a has a camera on the the sleeping environment of their kids so that if
environment of their kids so that if their kid wakes up in the middle of the
their kid wakes up in the middle of the night they can see it, they can hear it.
night they can see it, they can hear it. Um so camera and microphone do you think
Um so camera and microphone do you think we're either have now or soon we'll have
we're either have now or soon we'll have AI tools that will help us um better
AI tools that will help us um better understand the health status of infants
understand the health status of infants like parents learn intuitively over time
like parents learn intuitively over time based on um diaper changes based on um
based on um diaper changes based on um all sorts of things cries frequency of
all sorts of things cries frequency of illnesses etc and their kids how well
illnesses etc and their kids how well their kids are doing before they kids
their kids are doing before they kids can communicate that do you think AI can
can communicate that do you think AI can help parents be better parents by giving
help parents be better parents by giving real-time feedback on the health
real-time feedback on the health information of their kids. Not just if
information of their kids. Not just if they're awake or asleep or if they're in
they're awake or asleep or if they're in some sort of uh trouble, but really help
some sort of uh trouble, but really help us adjust our care of our young like
us adjust our care of our young like what's more important for our species
what's more important for our species than, you know, supporting the the
than, you know, supporting the the growth of our uh next generation.
growth of our uh next generation. >> No, absolutely. But I' I'd even more on
>> No, absolutely. But I' I'd even more on the biological side. I mean, so think
the biological side. I mean, so think about digital twins. There's and I'll
about digital twins. There's and I'll get to babies in a moment, but just
get to babies in a moment, but just you if you've ever bought a plane
you if you've ever bought a plane ticket, which any of us have today,
ticket, which any of us have today, that's a very sophisticated digital
that's a very sophisticated digital twin. Not the, you know, not the air
twin. Not the, you know, not the air traffic controllers looking at planes,
traffic controllers looking at planes, but the pricing models for what data is
but the pricing models for what data is going into driving that price in uh real
going into driving that price in uh real time, right? you you might be trying to
time, right? you you might be trying to buy a ticket and you go back an hour
buy a ticket and you go back an hour later or half hour later and it's like
later or half hour later and it's like double or maybe it's gone up in you and
double or maybe it's gone up in you and that's because it's using constant data
that's because it's using constant data from environments from things happening
from environments from things happening in the world from geopolitical issues
in the world from geopolitical issues from things happening in the that's
from things happening in the that's driving that price and that is very much
driving that price and that is very much an AIdriven digital twin that's driving
an AIdriven digital twin that's driving you know the sort of value of that that
you know the sort of value of that that ticket and so there there are places
ticket and so there there are places where we use digital twin so that would
where we use digital twin so that would sort of the example of something that's
sort of the example of something that's affecting our lives, but we don't think
affecting our lives, but we don't think about it as a digital twin, but it is a
about it as a digital twin, but it is a digital twin.
digital twin. >> And then you think about a different
>> And then you think about a different example where you've got a whole sandbox
example where you've got a whole sandbox model. The NFL might have a a digital
model. The NFL might have a a digital twin of every player that's in the NFL,
twin of every player that's in the NFL, right? They're they know data. They they
right? They're they know data. They they they're tracking that information. They
they're tracking that information. They know how people are going to perform
know how people are going to perform many times. What do they care about?
many times. What do they care about? They want to anticipate if someone might
They want to anticipate if someone might be, you know, high risk for an injury so
be, you know, high risk for an injury so that they, you know, can mitigate it.
that they, you know, can mitigate it. >> They're using those kind of data.
>> They're using those kind of data. >> Absolutely. Yeah.
>> Absolutely. Yeah. >> Interesting. I think the word twin is
>> Interesting. I think the word twin is the misleading part. I feel like digital
the misleading part. I feel like digital twin I feel like
twin I feel like >> soon that nomenclature needs to be
>> soon that nomenclature needs to be replaced because people hear twin they
replaced because people hear twin they think a duplicate of yourself.
think a duplicate of yourself. >> Yes.
>> Yes. >> I I feel like these are are um
>> I I feel like these are are um >> well it's a duplicate of relevant data
>> well it's a duplicate of relevant data and information about yourself but not
and information about yourself but not just trying to like what's the purpose
just trying to like what's the purpose in emulating myself? It's to emulate
in emulating myself? It's to emulate key. So imagine me as a physical system.
key. So imagine me as a physical system. I'm going to digitize some of that data,
I'm going to digitize some of that data, right? And whatever, you know, data I
right? And whatever, you know, data I have, I'm it's how that data I interact
have, I'm it's how that data I interact with it to make intelligent insights and
with it to make intelligent insights and feedback loops in the digital
feedback loops in the digital environment about how that physical
environment about how that physical system is going to behave. Right.
system is going to behave. Right. >> Okay. So, it's a digital representative.
>> Okay. So, it's a digital representative. >> Yes.
>> Yes. >> More than a digital twin. Yes. I think
>> More than a digital twin. Yes. I think I'm I'm not trying to
I'm I'm not trying to >> There are many digital twins in any
>> There are many digital twins in any digital twin. So like even you know
digital twin. So like even you know you've got data you live with lots of
you've got data you live with lots of digital what I would I think the world
digital what I would I think the world would the digital twin whatever
would the digital twin whatever nomenclature would say is a digital twin
nomenclature would say is a digital twin but I like a digital representative and
but I like a digital representative and it's it's informing some aspect of
it's it's informing some aspect of decision- making and it's many feedback
decision- making and it's many feedback so I'm digitizing different things I'm
so I'm digitizing different things I'm you know and and in that situational
you know and and in that situational awareness model like just can I give a
awareness model like just can I give a quick example so imagine I so I I can
quick example so imagine I so I I can digitize an environment right I can
digitize an environment right I can digitize are the the space we're in
digitize are the the space we're in right now and would that be a digital
right now and would that be a digital twin? So first there in situational
twin? So first there in situational awareness there's the state of okay so
awareness there's the state of okay so what's the sort of sensor
what's the sort of sensor you know limitations the acuity of the
you know limitations the acuity of the data I've actually brought in okay so
data I've actually brought in okay so that's like perception same with our
that's like perception same with our sensory systems and then there's
sensory systems and then there's comprehension so comprehension would be
comprehension so comprehension would be like okay that's a table that's a chair
like okay that's a table that's a chair that's a person now I'm in those sort of
that's a person now I'm in those sort of semantic units of relevance that the
semantic units of relevance that the digitization takes then there's the
digitization takes then there's the insight so what's happening in that
insight so what's happening in that environment. What do I do with that?
environment. What do I do with that? What is, you know, and and that's that's
What is, you know, and and that's that's where things get interesting and that's
where things get interesting and that's where a lot of, you know, I think the
where a lot of, you know, I think the future of AI products is because then
future of AI products is because then it's the feedback loops of what's
it's the feedback loops of what's happening with those, you know, that
happening with those, you know, that input and that data. And it it becomes
input and that data. And it it becomes interesting and important when you start
interesting and important when you start having multiple layers of relevant data
having multiple layers of relevant data that are interacting that can give you
that are interacting that can give you the right insights about what's
the right insights about what's happening, what to anticipate and you
happening, what to anticipate and you know in that space. But that's all about
know in that space. But that's all about our situational awareness and
our situational awareness and intelligence in that environment.
intelligence in that environment. >> Yeah, I I can see where uh these
>> Yeah, I I can see where uh these technologies could take us. I think for
technologies could take us. I think for the general public right now,
the general public right now, AI is super scary because we hear most
AI is super scary because we hear most about AI developing its own forms of
about AI developing its own forms of intelligence that turn on us.
intelligence that turn on us. >> I think people are gradually getting on
>> I think people are gradually getting on board the idea that AI can be very
board the idea that AI can be very useful. We have digital representatives
useful. We have digital representatives already out there for for us in these
already out there for for us in these different domains.
different domains. >> Absolutely. And
>> Absolutely. And >> I think being able to customize them for
>> I think being able to customize them for our unique challenges and to and our
our unique challenges and to and our unique goals is really what's most
unique goals is really what's most exciting to me.
exciting to me. >> I love that because I I mean I think
>> I love that because I I mean I think what I was trying to say is exactly what
what I was trying to say is exactly what you said. Look, there they are out there
you said. Look, there they are out there and these are effectively digital twins.
and these are effectively digital twins. Every company that's you're interacting
Every company that's you're interacting with social media has an effectively a
with social media has an effectively a digital twin of you in some place. It's
digital twin of you in some place. It's not to emulate your body but it's to
not to emulate your body but it's to emulate your behaviors. So to you know
emulate your behaviors. So to you know in those spaces or you're using tools
in those spaces or you're using tools that are optim you know have digital
that are optim you know have digital twins you for things you do in your
twins you for things you do in your daily life. So the question is how do we
daily life. So the question is how do we harness that for our success for
harness that for our success for individual success for understanding and
individual success for understanding and agency of what that can mean for you? If
agency of what that can mean for you? If the NFL is using it for a player, you
the NFL is using it for a player, you can use it as an athlete, meaning as an
can use it as an athlete, meaning as an athlete at any level, right? And it's
athlete at any level, right? And it's that digitization of information that
that digitization of information that can feed you. For my baby, you can
can feed you. For my baby, you can better understand a great deal about how
better understand a great deal about how they're successful or what isn't
they're successful or what isn't successful about them. and you know some
successful about them. and you know some of not not your baby's always successful
of not not your baby's always successful I don't want to say but what is maybe
I don't want to say but what is maybe not you know working well for them you
not you know working well for them you know the things that but um I would tend
know the things that but um I would tend to say uh the the exciting places about
to say uh the the exciting places about digital twins come in and really once
digital twins come in and really once you start integrating the data from
you start integrating the data from different places that tell us about the
different places that tell us about the success of our systems and those are
success of our systems and those are anchored with actual successes right I
anchored with actual successes right I think You used an example of your
think You used an example of your mattress and sleep and or even like you
mattress and sleep and or even like you one I liked was I had three good very
one I liked was I had three good very focused work sessions. You may have used
focused work sessions. You may have used different words Andy but the idea is
different words Andy but the idea is okay you've had those but it's when you
okay you've had those but it's when you can correlate it with other systems and
can correlate it with other systems and other outputs that it becomes powerful.
other outputs that it becomes powerful. That's the way a digital representative
That's the way a digital representative or a digital twin becomes more useful is
or a digital twin becomes more useful is thinking about not you know the
thinking about not you know the resolution of the data where the data
resolution of the data where the data source where the data is coming from
source where the data is coming from meaning whether is it biometric data is
meaning whether is it biometric data is it environmental data you know is it the
it environmental data you know is it the context of the state of what else was
context of the state of what else was happening during those work sessions and
happening during those work sessions and how is that something that I don't have
how is that something that I don't have to think about but AI can help me
to think about but AI can help me understand where I'm successful and what
understand where I'm successful and what else drove that success or what drove
else drove that success or what drove that state because it's not just my
that state because it's not just my success, it's intelligence. It's I like
success, it's intelligence. It's I like to call it situational intelligence is
to call it situational intelligence is sort of the overarching goal that we
sort of the overarching goal that we want to have and that involves you know
want to have and that involves you know my body and systems having situational
my body and systems having situational awareness but it's really you know a lot
awareness but it's really you know a lot of um integration of data that you know
of um integration of data that you know AI is very powerful for thinking about
AI is very powerful for thinking about how does it optimize and give us the the
how does it optimize and give us the the insights it doesn't have to do just have
insights it doesn't have to do just have systems behave but it can give us the
systems behave but it can give us the insights of how effectively we can act
insights of how effectively we can act in those environments
in those environments >> yeah I think of AI as being able to see
>> yeah I think of AI as being able to see what we can't see. Yes. So, for
what we can't see. Yes. So, for instance, if I had some sort of AI
instance, if I had some sort of AI representative that, you know, paid
representative that, you know, paid attention to my work environment and to
attention to my work environment and to my ability to focus as I'm trying to do
my ability to focus as I'm trying to do focused work.
focused work. >> And it turned out, obviously I'm making
>> And it turned out, obviously I'm making this up, but it turned out that every
this up, but it turned out that every time my um my air conditioner clicked
time my um my air conditioner clicked over to silent or back to on that it
over to silent or back to on that it would break my focus for the next 10
would break my focus for the next 10 minutes. Yes.
minutes. Yes. >> And I wasn't aware of that. And by the
>> And I wasn't aware of that. And by the way, this for people listening, this is
way, this for people listening, this is entirely plausible because so many of
entirely plausible because so many of our states of mind are triggered by cues
our states of mind are triggered by cues that we're just fundamentally unaware of
that we're just fundamentally unaware of >> or that it's always at the 35 minute
>> or that it's always at the 35 minute mark that my eyes start to have to
mark that my eyes start to have to reread words or lines um because somehow
reread words or lines um because somehow my attention is drifting um or that it's
my attention is drifting um or that it's paragraphs of longer than a certain
paragraphs of longer than a certain length. It's a near infinite space for
length. It's a near infinite space for us to explore on our own, but for AI to
us to explore on our own, but for AI to explore it, it's straightforward. And so
explore it, it's straightforward. And so it it can see through our literal our
it it can see through our literal our cognitive blind spots and our functional
cognitive blind spots and our functional blind spots. I and I think of where
blind spots. I and I think of where people pay a lot of money right now to
people pay a lot of money right now to get information to get around their
get information to get around their blind spots are things like um when you
blind spots are things like um when you have a pain and you don't know what it
have a pain and you don't know what it is, you go to this thing called a
is, you go to this thing called a doctor.
doctor. >> Or when you have um a uh a problem and
>> Or when you have um a uh a problem and you don't know how to sort it out, you
you don't know how to sort it out, you might talk to a therapist, right? People
might talk to a therapist, right? People pay a lot of money for that. I'm not
pay a lot of money for that. I'm not saying AI should replace all of that,
saying AI should replace all of that, but I do think AI can see things that we
but I do think AI can see things that we can't see.
can't see. >> Two examples to your point, which I I
>> Two examples to your point, which I I love the, you know, the reading
love the, you know, the reading potentially you're, you know, there's a
potentially you're, you know, there's a point at which you're experiencing
point at which you're experiencing fatigue and you want, you know, you
fatigue and you want, you know, you ideally, much like the fish tank, you
ideally, much like the fish tank, you want to be not reactive. You want to be
want to be not reactive. You want to be proactive. You want to mitigate it. you
proactive. You want to mitigate it. you know stop or you could have your devices
know stop or you could have your devices can have that integration of data and
can have that integration of data and respond to give you feedback when your
respond to give you feedback when your either your mental acuity your vigilance
either your mental acuity your vigilance or your just effectiveness has waned
or your just effectiveness has waned right but also on the level of health uh
right but also on the level of health uh a we know AI is you know huge for uh
a we know AI is you know huge for uh identifying a lot of different
identifying a lot of different pathologies out of you know data that as
pathologies out of you know data that as humans we're just not that good at at
humans we're just not that good at at discerning you know our voice in the
discerning you know our voice in the last 10 years we've become come much
last 10 years we've become come much more aware of the different pathologies
more aware of the different pathologies that are um can be discerned from AI
that are um can be discerned from AI app, you know, assessments of our speech
app, you know, assessments of our speech and not what we say, but how we say it.
and not what we say, but how we say it. >> Yeah, there's a lab up in University of
>> Yeah, there's a lab up in University of Washington, um I think it's Sam Golden's
Washington, um I think it's Sam Golden's lab who um
lab who um uh working on some really impressive
uh working on some really impressive algorithms to analyze speech patterns as
algorithms to analyze speech patterns as a way to predict suicidality.
a way to predict suicidality. >> Oh, interesting. and to great success
>> Oh, interesting. and to great success where people don't realize that they're
where people don't realize that they're drifting in that direction.
drifting in that direction. >> Um and phones can potentially warn
>> Um and phones can potentially warn people,
people, >> warn them themselves, right? Um that
>> warn them themselves, right? Um that they're drifting in a particular
they're drifting in a particular direction. People who have um cycles of
direction. People who have um cycles of depression or mania can know whether or
depression or mania can know whether or not they're drifting into that. That can
not they're drifting into that. That can be extremely useful. Um they can discern
be extremely useful. Um they can discern who else gets that information. Um I
who else gets that information. Um I think it and it's all based on tonality
think it and it's all based on tonality uh at different times of day stuff that
uh at different times of day stuff that even in a close close relationship with
even in a close close relationship with a therapist over many years they might
a therapist over many years they might not be able to detect if the person
not be able to detect if the person becomes reclusive or something of that
becomes reclusive or something of that sort.
sort. >> Absolutely. I mean um neural
>> Absolutely. I mean um neural degeneration it shows up and you know
degeneration it shows up and you know short assessment of how people speak uh
short assessment of how people speak uh they've definitely been able to show
they've definitely been able to show potential likelihood of psychosis.
potential likelihood of psychosis. um you know and and that's with uh
um you know and and that's with uh syntactic completion and and how people
syntactic completion and and how people read read read paragraphs. Um neural
read read read paragraphs. Um neural degeneration though things like
degeneration though things like Alzheimer's show up in speech because of
Alzheimer's show up in speech because of the you know linguistic cues can show up
the you know linguistic cues can show up but you know sometimes 10 years before a
but you know sometimes 10 years before a typical clinical uh uh symptom would
typical clinical uh uh symptom would show up that would be identified and and
show up that would be identified and and what I what I think is important for
what I what I think is important for people to realize is it's not someone
people to realize is it's not someone saying I don't remember. It's nothing
saying I don't remember. It's nothing like that. It's not those cues that you
like that. It's not those cues that you think are actually relevant. It's more
think are actually relevant. It's more like an individual says something
like an individual says something like that. What I just did, which was I
like that. What I just did, which was I purposely stuttered. I started a word
purposely stuttered. I started a word again, right? And it's, you know, what
again, right? And it's, you know, what we might call a stutter in how we're
we might call a stutter in how we're speaking. Sometimes duration of spaces
speaking. Sometimes duration of spaces between starting one sentence to the
between starting one sentence to the next. These are things that as humans
next. These are things that as humans we've adapted to not p not pick up on
we've adapted to not p not pick up on because it makes us you know it makes us
because it makes us you know it makes us ineffective in communication or and and
ineffective in communication or and and but an algorithm can do so very well. Um
but an algorithm can do so very well. Um diabetes, heart disease both show up in
diabetes, heart disease both show up in voice. diabetes shows up because uh you
voice. diabetes shows up because uh you can pick up on uh dehydration uh in the
can pick up on uh dehydration uh in the in the voice uh you much again I'm I'm a
in the voice uh you much again I'm I'm a sound person in my heart in my past and
sound person in my heart in my past and if you look at the spectrum of sound
if you look at the spectrum of sound you're going to see changes that show up
you're going to see changes that show up you know there are very consistent
you know there are very consistent things in a voice that show up with
things in a voice that show up with dehydration in the spectral you know
dehydration in the spectral you know salance as well as with heart disease
salance as well as with heart disease you get sort of flutter that shows up
you get sort of flutter that shows up it's a proxy for things happening inside
it's a proxy for things happening inside your body you know with problems
your body you know with problems cardiovascular issues, but you're going
cardiovascular issues, but you're going to see them as certain like modulatory
to see them as certain like modulatory fluctuations in certain frequency bands.
fluctuations in certain frequency bands. And again, we don't walk around as as,
And again, we don't walk around as as, you know, a partner or a spouse or a or
you know, a partner or a spouse or a or a child, you know, you you caretaking
a child, you know, you you caretaking our parents and listening for, you know,
our parents and listening for, you know, like the the 4 kHz modulation, but an
like the the 4 kHz modulation, but an algorithm can. And you know, all of
algorithm can. And you know, all of these are places where you can identify
these are places where you can identify something that is potentially, you know,
something that is potentially, you know, mitigate something proactively before
mitigate something proactively before there's, you know, a problem. And
there's, you know, a problem. And especially with like neural
especially with like neural degeneration, we're really just getting
degeneration, we're really just getting to a place where there's
to a place where there's pharmacological, you know, opportunities
pharmacological, you know, opportunities to slow something down. And you want to
to slow something down. And you want to find that as quick as possible. So where
find that as quick as possible. So where do you you want to you want to have that
do you you want to you want to have that input so that you can do something about
input so that you can do something about it. You asked me about the babies, you
it. You asked me about the babies, you know, like before we
know, like before we the type of coughs we have tell us a lot
the type of coughs we have tell us a lot about different pathologies. So for a
about different pathologies. So for a baby their cry their you know if I'm
baby their cry their you know if I'm thinking you asked me about a digital
thinking you asked me about a digital tomb where would I be most interested in
tomb where would I be most interested in using that information if I had you know
using that information if I had you know children or I mean I do have a child but
children or I mean I do have a child but from you know in the sort of lowest
from you know in the sort of lowest touch most opportunity it's to identify
touch most opportunity it's to identify potential you know pathologies or issues
potential you know pathologies or issues early based on you know the the natural
early based on you know the the natural sounds and the natural utterances and
sounds and the natural utterances and call you know that are happening to
call you know that are happening to understand if there is something that
understand if there is something that you know there's a way it could be
you know there's a way it could be helped. It could be you know need you
helped. It could be you know need you could proactively
could proactively um make something much better.
um make something much better. >> Let's talk about you.
>> Let's talk about you. >> Oh boy.
>> Oh boy. >> And how you got into all of this stuff
>> And how you got into all of this stuff because you're highly unusual in the
because you're highly unusual in the neuroscience space. I recall when we
neuroscience space. I recall when we were graduate students who when you were
were graduate students who when you were working on auditory perception and
working on auditory perception and physiology and then years later uh now
physiology and then years later uh now you're involved with in AI
you're involved with in AI neuroplasticity you were at Dolby. the
neuroplasticity you were at Dolby. the what is to you the most of interesting
what is to you the most of interesting question that's driving all of this like
question that's driving all of this like what what guides your choices about what
what what guides your choices about what to work on
to work on >> human technology intersection and
>> human technology intersection and perception is my core right I say
perception is my core right I say perception but the world is data and you
perception but the world is data and you know how our brains take in the data
know how our brains take in the data that we consume to optimize how we
that we consume to optimize how we experience the world is is what I care
experience the world is is what I care about across all of what I've spent my
about across all of what I've spent my time doing and for me technology is such
time doing and for me technology is such a huge part of that
a huge part of that >> that it is you know I I like to innovate
>> that it is you know I I like to innovate I like to build things but I also like
I like to build things but I also like to think about how do we improve human
to think about how do we improve human performance core to improving human
performance core to improving human performance is understanding how we're
performance is understanding how we're different not just how similar but you
different not just how similar but you know the nuances of how our brains are
know the nuances of how our brains are shaped and how they're influenced and
shaped and how they're influenced and thus why I care you know I've spent so
thus why I care you know I've spent so much time in neuroplasticity and it is
much time in neuroplasticity and it is at the intersection of everything is how
at the intersection of everything is how are we changing and how do we harness
are we changing and how do we harness that how Do we make it something that we
that how Do we make it something that we have agency over? Whether it's from the
have agency over? Whether it's from the technologies we build and we innovate to
technologies we build and we innovate to the point of I want to feel better. I
the point of I want to feel better. I want to be successful. I don't want that
want to be successful. I don't want that to be something left to surprise me.
to be something left to surprise me. Right?
Right? >> So you asked me how do I get there? One
>> So you asked me how do I get there? One thing that so I was violinist back in
thing that so I was violinist back in the day. I'm still a violinist and
the day. I'm still a violinist and music's a part of my life. But I was
music's a part of my life. But I was studying viol music and engineering a uh
studying viol music and engineering a uh when I was in undergrad and I think we
when I was in undergrad and I think we alluded to the fact I have uh absolute
alluded to the fact I have uh absolute pitch and absolute pitches for anyone
pitch and absolute pitches for anyone that doesn't know it's not it it's not
that doesn't know it's not it it's not anything that means I always sing in
anything that means I always sing in tune. What it means is I hear the world
tune. What it means is I hear the world uh like I hear sound like people see
uh like I hear sound like people see color. Okay. Um and I can't turn it off
color. Okay. Um and I can't turn it off really. I can kind of push it back.
really. I can kind of push it back. >> Wait, sorry. Don't we all hear sound
>> Wait, sorry. Don't we all hear sound like we see? I mean, I hear sounds and I
like we see? I mean, I hear sounds and I see colors. Could you clarify what you
see colors. Could you clarify what you mean?
mean? >> When you Okay. So, when you walk down
>> When you Okay. So, when you walk down the street, your brain is going, "Oh,
the street, your brain is going, "Oh, that's red, that's black, that's blue,
that's red, that's black, that's blue, that's green." My brain's going, "That's
that's green." My brain's going, "That's an A, that's a B, that's a G, that's an
an A, that's a B, that's a G, that's an F."
F." >> I see. You're cate your You're your
>> I see. You're cate your You're your category.
category. >> There's a categorical perception about
>> There's a categorical perception about it. And because of the nature of I think
it. And because of the nature of I think my exposure to sound in my life, I also
my exposure to sound in my life, I also know what frequency it is, right? You
know what frequency it is, right? You know, so I can say that's, you know, 350
know, so I can say that's, you know, 350 Hz or that's 400 Hz or that's 442 hertz.
Hz or that's 400 Hz or that's 442 hertz. And um it has different applications. I
And um it has different applications. I mean, I can transcribe a jazz solo when
mean, I can transcribe a jazz solo when I listen to it. That's a great party
I listen to it. That's a great party trick. But but it doesn't mean that it's
trick. But but it doesn't mean that it's not necessarily a good thing for a
not necessarily a good thing for a musician, right? you know as well as I
musician, right? you know as well as I do that um you know categorical
do that um you know categorical perception is we all have different
perception is we all have different forms of it usually for speech and
forms of it usually for speech and language like the units of vowels or
language like the units of vowels or phonetic units will especially vowels
phonetic units will especially vowels will you can hear many different
will you can hear many different versions of a an e and still hear it as
versions of a an e and still hear it as an e and that's what we would call
an e and that's what we would call categorical perception and I my brain
categorical perception and I my brain does the same thing for you a sort of
does the same thing for you a sort of set of frequencies to hear it as an a
set of frequencies to hear it as an a and um that's that that can be good at
and um that's that that can be good at times, but when you're actually a
times, but when you're actually a musician, there's a lot more subtlety
musician, there's a lot more subtlety that goes into how you play with other
that goes into how you play with other people. And um what what key you're in
people. And um what what key you're in or what you know the the details like if
or what you know the the details like if you ask me to sing happy birthday, I'm
you ask me to sing happy birthday, I'm always going to sing it in the key of G
always going to sing it in the key of G if I am left to my own devices and I
if I am left to my own devices and I will I will get you there somehow if we
will I will get you there somehow if we start somewhere else. M so what happened
start somewhere else. M so what happened to me when I was in music school when I
to me when I was in music school when I was in conservatory and also engineering
was in conservatory and also engineering school is um I was taking two things
school is um I was taking two things happened. I knew that I had to override
happened. I knew that I had to override my brain because it was not allowing me
my brain because it was not allowing me the subtlety I wanted to play my shots
the subtlety I wanted to play my shots or play my chamber music in the ways
or play my chamber music in the ways that were that I was having to work too
that were that I was having to work too hard to override what you know these
hard to override what you know these these sort of categories of sounds I was
these sort of categories of sounds I was hearing. So I started playing early
hearing. So I started playing early music. Early music, Baroque music for
music. Early music, Baroque music for anyone. I I said I think I said earlier
anyone. I I said I think I said earlier A has is a social construct. Today we
A has is a social construct. Today we typically as a set as a standard A is
typically as a set as a standard A is 440 hertz. Um if you go back to like the
440 hertz. Um if you go back to like the 1700s, A was uh 415 hertz in the Baroque
1700s, A was uh 415 hertz in the Baroque era and 415 hertz is effectively a G
era and 415 hertz is effectively a G sharp. So it's the difference between H
sharp. So it's the difference between H and H. Okay. And um what would happen to
and H. Okay. And um what would happen to me when I was trying to override this is
me when I was trying to override this is I was playing in an early music ensemble
I was playing in an early music ensemble and I would tune my violin up and I
and I would tune my violin up and I would see a on the page and I'd hear
would see a on the page and I'd hear G#arp in my brain and it was completely
G#arp in my brain and it was completely it it was it was I was terrible. I was
it it was it was I was terrible. I was like always it was really hard for my
like always it was really hard for my brain to override and uh I mean wind
brain to override and uh I mean wind brass and wind players do this all the
brass and wind players do this all the time. It's like transposition and they
time. It's like transposition and they modulate to the key that they're in and
modulate to the key that they're in and they doesn't their brains have evolved,
they doesn't their brains have evolved, you know, through their training and
you know, through their training and neuroplasticity to be able to not have
neuroplasticity to be able to not have the same sort of experience I had.
the same sort of experience I had. Anyhow,
Anyhow, long story long, I uh was also taking a
long story long, I uh was also taking a neuroscience course. This neuroscience
neuroscience course. This neuroscience course, we were reading papers about
course, we were reading papers about sort of different mapmaking and
sort of different mapmaking and neuroplasticity. And I read this paper
neuroplasticity. And I read this paper by a professor at Stanford named Eric
by a professor at Stanford named Eric Kudson. And Eric Kudson did these
Kudson. And Eric Kudson did these amazing well he did a lot of seminal
amazing well he did a lot of seminal work for how we understand the auditory
work for how we understand the auditory pathways as well as how we form
pathways as well as how we form multiensory objects and and the way the
multiensory objects and and the way the brain integrates um you know cells data
brain integrates um you know cells data from across our modalities meaning you
from across our modalities meaning you know sight and sound. Um but in this
know sight and sound. Um but in this paper what he was doing was he had
paper what he was doing was he had identified cells in the brain that
identified cells in the brain that optimally responded their receptive
optimally responded their receptive fields. You know receptive field being
fields. You know receptive field being that sort of like in all of that giant
that sort of like in all of that giant data set of the world it's that you know
data set of the world it's that you know it's the the set of data that optimally
it's the the set of data that optimally causes that cell to respond. And for
causes that cell to respond. And for these cells, they cared about a
these cells, they cared about a particular location in auditory and
particular location in auditory and visual space, which you know, frankly,
visual space, which you know, frankly, for mammals, we don't have the same sort
for mammals, we don't have the same sort of like cells because we can move our
of like cells because we can move our eyes back and forth in our sockets
eyes back and forth in our sockets unlike owls. And he studied owls. And
unlike owls. And he studied owls. And owls have a very hardwired map of
owls have a very hardwired map of auditory visual space.
auditory visual space. >> On the other hand, if I hear click off
>> On the other hand, if I hear click off to my right, I turn my head to the
to my right, I turn my head to the right.
right. >> You turn your head it triggers a
>> You turn your head it triggers a different, you know, vestibular ocular
different, you know, vestibular ocular response that moves, you know, all of
response that moves, you know, all of that. Yes. But in this case, he had
that. Yes. But in this case, he had these beautiful hardwired maps of
these beautiful hardwired maps of auditory visual space. And then he would
auditory visual space. And then he would rear and raise these owls with prism
rear and raise these owls with prism glasses that effectively shifted their
glasses that effectively shifted their their visual system by 15 degrees. And
their visual system by 15 degrees. And um then he would put them key to
um then he would put them key to developing neuroplasticity. He would put
developing neuroplasticity. He would put them in high, you know, important, you
them in high, you know, important, you know, high not stress, but let's say
know, high not stress, but let's say situations where they had to do
situations where they had to do something critical to their, you know,
something critical to their, you know, their survival or their their
their survival or their their well-being. And so they would hunt and
well-being. And so they would hunt and they would feed and do things like that
they would feed and do things like that with with the um this 15 degree shift,
with with the um this 15 degree shift, you know. And consequently, he saw the
you know. And consequently, he saw the cells, the auditory neurons, he saw
cells, the auditory neurons, he saw their their denderrites realigned to the
their their denderrites realigned to the now 15 degree visually shifted cells.
now 15 degree visually shifted cells. And and it was this realization that
And and it was this realization that they developed a secondary map that was
they developed a secondary map that was now aligned with the 15 degree shift of
now aligned with the 15 degree shift of the prism glasses as well as their
the prism glasses as well as their original map was was super interesting
original map was was super interesting for understanding how our brains
for understanding how our brains integrate data and the feedback and
integrate data and the feedback and neuroplasticity.
neuroplasticity. So I go back to my Baroque violin where
So I go back to my Baroque violin where I'm always out of tune and I'm tuning up
I'm always out of tune and I'm tuning up with, you know, tuning up my vi my
with, you know, tuning up my vi my baroque violin and I realize I had
baroque violin and I realize I had developed absolute pitch at A415. So I
developed absolute pitch at A415. So I developed a secondary absolute pitch map
developed a secondary absolute pitch map and then I would go play Shastikovich
and then I would go play Shastikovich right after it A440 and I had that map
right after it A440 and I had that map and I have nothing in between but I
and I have nothing in between but I could modulate between the two and
could modulate between the two and that's like the point at which I said I
that's like the point at which I said I I I think I just you know my brain is a
I I think I just you know my brain is a little weird and I just did something
little weird and I just did something that I need to go better understand. So
that I need to go better understand. So that's how I like ended up here as a
that's how I like ended up here as a neuroscientist.
neuroscientist. >> I know Eric's work really well. Um our
>> I know Eric's work really well. Um our labs were next door. Yes, our offices
labs were next door. Yes, our offices were next door. He's retired now, but um
were next door. He's retired now, but um >> I I've he knows I told him the story.
>> I I've he knows I told him the story. >> He's he's wonderful. I I think one of my
>> He's he's wonderful. I I think one of my favorite things about those studies I
favorite things about those studies I think people will find interesting is
think people will find interesting is that um
that um if
if an animal, human or owl, you know, has a
an animal, human or owl, you know, has a displacement in the world, something's
displacement in the world, something's different, something changes and you
different, something changes and you need to adjust to it. could be like new
need to adjust to it. could be like new information coming to you that you need
information coming to you that you need to learn in order to perform your sport
to learn in order to perform your sport correctly or to perform well in class or
correctly or to perform well in class or or an emotionally challenging situation
or an emotionally challenging situation that you need to adjust to. All of that
that you need to adjust to. All of that >> can happen,
>> can happen, but it happens much much faster if your
but it happens much much faster if your life depends on it.
life depends on it. >> Yes.
>> Yes. >> And we kind of intuitively know this,
>> And we kind of intuitively know this, but one of my favorite things about his
but one of my favorite things about his work is where he said, "Okay, well,
work is where he said, "Okay, well, yeah, these owls can adjust to the prism
yeah, these owls can adjust to the prism shift. their maps in the brain can
shift. their maps in the brain can change, but they sure as heck form much
change, but they sure as heck form much faster if you say, "Hey, in order to
faster if you say, "Hey, in order to eat, in other words, in order to
eat, in other words, in order to survive, these maps have to change." You
survive, these maps have to change." You know, and I I like that study so much
know, and I I like that study so much because, you know, we hear all the time,
because, you know, we hear all the time, you know, it takes 29 days to form a new
you know, it takes 29 days to form a new habit or it takes 50 days to form a new
habit or it takes 50 days to form a new habit or whatever it is. Actually, you
habit or whatever it is. Actually, you can form a new habit as quickly as is
can form a new habit as quickly as is necessary to form that new habit. And so
necessary to form that new habit. And so the limits on neuroplasticity are really
the limits on neuroplasticity are really set by how critical it is.
set by how critical it is. >> Yeah.
>> Yeah. >> And you know, of course, if you put a
>> And you know, of course, if you put a gun to my head right now and you said,
gun to my head right now and you said, "Okay, remap your your auditory world."
"Okay, remap your your auditory world." I mean, there are limits at the at the
I mean, there are limits at the at the other end, too. I mean, I can't do that
other end, too. I mean, I can't do that quickly. But I I think um
quickly. But I I think um it's a reminder to me anyway, and thank
it's a reminder to me anyway, and thank you for bringing up Eric's work. It's a
you for bringing up Eric's work. It's a reminder to me that neuroplasticity is
reminder to me that neuroplasticity is always in reach.
always in reach. If the incentives are high enough, we
If the incentives are high enough, we can do it. Yeah.
can do it. Yeah. >> And so I think with AI it's going to be
>> And so I think with AI it's going to be very interesting or with technology
very interesting or with technology generally. You know our ability to form
generally. You know our ability to form these new maps of experience at least
these new maps of experience at least with smartphones has been pretty
with smartphones has been pretty gradual. I really see 2010 as kind of
gradual. I really see 2010 as kind of the beginning of the smartphone and then
the beginning of the smartphone and then now by 2025
now by 2025 >> we're in a place where most everyone
>> we're in a place where most everyone young and old has integrated this new
young and old has integrated this new technology. I think AI is coming at us
technology. I think AI is coming at us very fast and it's not unclear what form
very fast and it's not unclear what form it's coming at us and and where and as
it's coming at us and and where and as you said it's already here. And I think
you said it's already here. And I think um we will adapt
um we will adapt >> for sure. We'll form the necessary maps.
>> for sure. We'll form the necessary maps. I think uh being very conscious of which
I think uh being very conscious of which maps we're are changing is so key. I
maps we're are changing is so key. I mean I think we're still doing a lot of
mean I think we're still doing a lot of cleanup
cleanup >> of the kind of detrimental aspects of
>> of the kind of detrimental aspects of smartphones. Short wavelength light late
smartphones. Short wavelength light late at night.
at night. >> Um you know being in contact with so
>> Um you know being in contact with so many people all the time maybe not so
many people all the time maybe not so good. I mean, I think what scares
good. I mean, I think what scares people, certainly me, is the idea that,
people, certainly me, is the idea that, you know, we're going to be doing a lot
you know, we're going to be doing a lot of error correction over the next 30
of error correction over the next 30 years because we're going so fast with
years because we're going so fast with technology because maps can change
technology because maps can change really, really fast.
really, really fast. >> Well, they they do change. Sam Alman had
>> Well, they they do change. Sam Alman had um I I I saw him
um I I I saw him say this and I actually thought was
say this and I actually thought was really good description. It's like, you
really good description. It's like, you know, PE Gen X or you know, there's a
know, PE Gen X or you know, there's a group that is using AI as a tool that's
group that is using AI as a tool that's sort of novel, interesting. Then you you
sort of novel, interesting. Then you you know you've got a different millennials
know you've got a different millennials or are using it as you know a search
or are using it as you know a search algorithm and maybe that's even Genex
algorithm and maybe that's even Genex but you know it's it's a little more
but you know it's it's a little more deeply integrated but then you go back
deeply integrated but then you go back you know to to younger generations and
you know to to younger generations and it's an operating system and it already
it's an operating system and it already is and that has major changes in neural
is and that has major changes in neural structure for how not just you know maps
structure for how not just you know maps but also neural processes for how we
but also neural processes for how we deal with information how we learn. uh
deal with information how we learn. uh you know the idea that we are very
you know the idea that we are very plastic under pressure. Absolutely. And
plastic under pressure. Absolutely. And that's where it gets interesting to talk
that's where it gets interesting to talk about different species too. I mean
about different species too. I mean we're talking about owls and that was
we're talking about owls and that was under pressure but you know what is
under pressure but you know what is successful human performance in training
successful human performance in training and all of these things. It's to make
and all of these things. It's to make those probabilistic situations more
those probabilistic situations more deterministic. Right? That's when you
deterministic. Right? That's when you are, if you're training as an athlete,
are, if you're training as an athlete, you're really trying to not have to
you're really trying to not have to think and to have the fastest reaction
think and to have the fastest reaction time to very complex behaviors given
time to very complex behaviors given complex stimula, complex situations and
complex stimula, complex situations and contexts, but you're you know that
contexts, but you're you know that situational awareness or physical
situational awareness or physical behavior in those environments. You you
behavior in those environments. You you want that as fast as possible with as
want that as fast as possible with as little cognitive, you know, load as
little cognitive, you know, load as possible. And you know, it's like that
possible. And you know, it's like that execution is critical. You love looking
execution is critical. You love looking across species. So do I. and looking for
across species. So do I. and looking for these ways where you know we we are a
these ways where you know we we are a brain is changing or you've got a
brain is changing or you've got a species that can do something that is
species that can do something that is absolutely not what you would predict or
absolutely not what you would predict or it's incredible in its you know how it
it's incredible in its you know how it can evade a predator how it can find a
can evade a predator how it can find a target you find a a mate and you know
target you find a a mate and you know it's doing things that are critical to
it's doing things that are critical to it being able to survive much as you
it being able to survive much as you said like I if I make it something that
said like I if I make it something that is absolutely necessary for success.
is absolutely necessary for success. It's going to do it. You know, one of my
It's going to do it. You know, one of my favorite examples is a particular moth
favorite examples is a particular moth that bats predate on um echolocating
that bats predate on um echolocating bats and and you know, frankly,
bats and and you know, frankly, echolocating bats are sort of nature's
echolocating bats are sort of nature's engineered amazing predatory species.
engineered amazing predatory species. You know, their their brains when you
You know, their their brains when you look at them, you know, are are just
look at them, you know, are are just incredible. They have huge amounts of
incredible. They have huge amounts of their their brain just dedicated to
their their brain just dedicated to what's called a FM constant frequency FM
what's called a FM constant frequency FM sort of sweep that some of the bats you
sort of sweep that some of the bats you know elicit a call that's sort of likeoo
know elicit a call that's sort of likeoo but really high
but really high >> so we so we can't hear it what does that
>> so we so we can't hear it what does that do for them
do for them >> it's doing two things one that constant
>> it's doing two things one that constant frequency portion is allowing them to
frequency portion is allowing them to sort of track the Doppler in a moving
sort of track the Doppler in a moving object so and and they're they're even
object so and and they're they're even so uh I mean It's such clever and
so uh I mean It's such clever and sophisticated. They're not changing um
sophisticated. They're not changing um they're changing subtly how what
they're changing subtly how what frequencies they elicit the call at so
frequencies they elicit the call at so that it always comes back in the same
that it always comes back in the same frequency range because that's where
frequency range because that's where their heightened sensitivity is.
their heightened sensitivity is. >> So otherwise you you know so they're
>> So otherwise you you know so they're modifying their vocal cords to make sure
modifying their vocal cords to make sure that the call comes back in the same
that the call comes back in the same range and then they're tracking how much
range and then they're tracking how much they've had to modify their their the
they've had to modify their their the call
call >> just so that people are on board. Yeah.
>> just so that people are on board. Yeah. Bats echoloccate. They're sending out
Bats echoloccate. They're sending out sound and they can measure distance and
sound and they can measure distance and sh they can essentially
sh they can essentially >> see in their mind's eye. They can sense
>> see in their mind's eye. They can sense distance. They can uh sense speed of
distance. They can uh sense speed of objects. They can sense shape of objects
objects. They can sense shape of objects by virtue of sounds being sent out and
by virtue of sounds being sent out and coming back. Absolutely.
coming back. Absolutely. >> And they're shaping those the sounds
>> And they're shaping those the sounds going out differently so that they can
going out differently so that they can look at multiple objects simultaneously.
look at multiple objects simultaneously. >> But also so they're shaping the sounds
>> But also so they're shaping the sounds they send out so that whatever comes
they send out so that whatever comes back is in their optimal neural like
back is in their optimal neural like range. so that they don't have to go
range. so that they don't have to go through more neuroplasticity that they
through more neuroplasticity that they already have like circuits that are
already have like circuits that are really dedicated to these certain
really dedicated to these certain frequency ranges. And so they send it
frequency ranges. And so they send it out and then they're keeping track of
out and then they're keeping track of the deltas. They're keeping track of how
the deltas. They're keeping track of how much they've had to change it and that's
much they've had to change it and that's what's in, you know, tells them the
what's in, you know, tells them the speed. So that constant frequency is a
speed. So that constant frequency is a lot like you know the ambulance sound
lot like you know the ambulance sound going by. That's the compression of
going by. That's the compression of sound waves that you hear as a when when
sound waves that you hear as a when when things move past you at speed. That's
things move past you at speed. That's the Doppler effect. And then there also
the Doppler effect. And then there also it has usually a really fast FM
it has usually a really fast FM frequency modulated sweep and that lets
frequency modulated sweep and that lets me take kind of a an imprint of you know
me take kind of a an imprint of you know so one's telling me the speed of the
so one's telling me the speed of the object another one's telling me sort of
object another one's telling me sort of what the surface structure looks like
what the surface structure looks like right that FM sweep lets me get uh you
right that FM sweep lets me get uh you know a sonic imprint of what's there so
know a sonic imprint of what's there so I can tell topography I can tell if
I can tell topography I can tell if there's a you know a moth on a a hard
there's a you know a moth on a a hard surface right so what's beautiful about
surface right so what's beautiful about other species is you've got a little
other species is you've got a little moth and you've got nature's predatory
moth and you've got nature's predatory marvel and 80% of the time about that
marvel and 80% of the time about that moth gets away
moth gets away >> how
>> how >> multiple things I call it almost an
>> multiple things I call it almost an acoustic arms race that's happening
acoustic arms race that's happening between the two and there's a lot of
between the two and there's a lot of acoustic sub subtrifuge between the moth
acoustic sub subtrifuge between the moth you know but there's also beautiful
you know but there's also beautiful deterministic responses that they have
deterministic responses that they have and um so first uh deterministic
and um so first uh deterministic behaviors again be it an athlete be it
behaviors again be it an athlete be it you know effectiveness being fast, quick
you know effectiveness being fast, quick in making good decisions that get you
in making good decisions that get you the right answer are always important.
the right answer are always important. So, you know, moss have just a few
So, you know, moss have just a few neurons. When that echolocating bat is
neurons. When that echolocating bat is flying, you know, at a certain point,
flying, you know, at a certain point, uh, when those neurons start firing,
uh, when those neurons start firing, they will start, you know, they'll start
they will start, you know, they'll start flying in more of a random pattern.
flying in more of a random pattern. You'll see the same thing with seals
You'll see the same thing with seals when there are great white sharks
when there are great white sharks around, right? It's decreasing the
around, right? It's decreasing the probability that, you know, it's easy
probability that, you know, it's easy for them to continue to track you. So
for them to continue to track you. So they'll f fly in a random pattern and
they'll f fly in a random pattern and then when their neurons saturate you
then when their neurons saturate you when when the when it gets those calls
when when the when it gets those calls get close enough the moth will drop to
get close enough the moth will drop to the ground with the idea that yeah in
the ground with the idea that yeah in assuming we don't live in cities in a
assuming we don't live in cities in a natural world the ground is you know
natural world the ground is you know wheat grass it's a difficult environment
wheat grass it's a difficult environment for an echo locating back to locate you
for an echo locating back to locate you right so that is just a deterministic
right so that is just a deterministic behavior that will happen regardless but
behavior that will happen regardless but then the interesting part is their body
then the interesting part is their body is reflecting metarlectors effectively
is reflecting metarlectors effectively so that the bat may put out its call and
so that the bat may put out its call and it deflects the you know the energy of
it deflects the you know the energy of the call away from its body. So you're
the call away from its body. So you're deflecting it away from critical
deflecting it away from critical critical areas and you know this is all
critical areas and you know this is all like happening and that's the the
like happening and that's the the changes in the physi physical body are
changes in the physi physical body are interesting but then it's the behavioral
interesting but then it's the behavioral differences they're really key right
differences they're really key right it's how fast does that moth react if it
it's how fast does that moth react if it has to question you know or if it were
has to question you know or if it were cognitively responsive instead of being
cognitively responsive instead of being deterministic in its behavior it
deterministic in its behavior it wouldn't escape right but it gets
wouldn't escape right but it gets Yeah, I've never thought about bats and
Yeah, I've never thought about bats and and moths. I I um I never got the insect
and moths. I I um I never got the insect I was about to say I never got the
I was about to say I never got the insect bug that that then no pun
insect bug that that then no pun intended. I I never got the insect bug
intended. I I never got the insect bug because um I I don't think of things in
because um I I don't think of things in the auditory domain. I think of things
the auditory domain. I think of things in the visual domain. And some insects
in the visual domain. And some insects are very visual. But um it's it's it's
are very visual. But um it's it's it's good for me to think about that. You
good for me to think about that. You know, one of my favorite people,
know, one of my favorite people, although I never met him, was Oliver
although I never met him, was Oliver Saxs, like the neurologist and writer.
Saxs, like the neurologist and writer. And he claimed to have spent a lot of
And he claimed to have spent a lot of time imagining, just sitting in a chair
time imagining, just sitting in a chair and trying to imagine what life would be
and trying to imagine what life would be like as a bat as a way to enhance his um
like as a bat as a way to enhance his um clinical abilities with patients
clinical abilities with patients suffering from different neurologic
suffering from different neurologic disorders.
disorders. >> Huh. So when he would interact with
>> Huh. So when he would interact with somebody with Parkinson's or with severe
somebody with Parkinson's or with severe autism or with lockin syndrome or uh any
autism or with lockin syndrome or uh any number of different deficits of the of
number of different deficits of the of the nervous system, he would um
the nervous system, he would um he felt that he could go into their mind
he felt that he could go into their mind a bit to understand what their
a bit to understand what their experience was like. He could empathize
experience was like. He could empathize with them and that would make him more
with them and that would make him more effective at treating them. And he
effective at treating them. And he certainly was very effective at storing
certainly was very effective at storing out their um their experience in ways
out their um their experience in ways that brought about a lot of compassion
that brought about a lot of compassion and understanding. Like he never
and understanding. Like he never presented a a neural condition in a way
presented a a neural condition in a way that made you feel sorry for the person.
that made you feel sorry for the person. It was always the opposite.
It was always the opposite. >> Um and I should point out, not trying to
>> Um and I should point out, not trying to be politically correct here, but when I
be politically correct here, but when I say autistic, I meant the patients he
say autistic, I meant the patients he worked with were severely autistic to
worked with were severely autistic to the point of, you know, never being able
the point of, you know, never being able to take care of themselves. This is
to take care of themselves. This is we're not talking about along a
we're not talking about along a spectrum. We're talking about the far
spectrum. We're talking about the far end of the spectrum of uh needing
end of the spectrum of uh needing assisted living their entire lives and
assisted living their entire lives and being sensory very uh from a sensory
being sensory very uh from a sensory standpoint extremely sensitive, couldn't
standpoint extremely sensitive, couldn't go out in public, that kind of thing.
go out in public, that kind of thing. That we're not talking about people that
That we're not talking about people that are uh functioning uh with autism. So um
are uh functioning uh with autism. So um apparently thinking in the auditory
apparently thinking in the auditory domain was useful for him. So I should
domain was useful for him. So I should probably do that. So I have one final
probably do that. So I have one final question for you. Uh which is what's
question for you. Uh which is what's really two questions. First question,
really two questions. First question, why did you sing to spiders? And second,
why did you sing to spiders? And second, what does that tell us about spiderw
what does that tell us about spiderw webs? Because uh I confess I know the
webs? Because uh I confess I know the answers to these questions, but I was
answers to these questions, but I was absolutely blown away to learn what
absolutely blown away to learn what spiderw webs are actually for. Um and
spiderw webs are actually for. Um and you singing to spiders reveals what
you singing to spiders reveals what they're for. So why did you sing to
they're for. So why did you sing to spiders?
spiders? >> Two things. And um you can watch me sing
>> Two things. And um you can watch me sing to a spider on a TED talk I gave a few
to a spider on a TED talk I gave a few years ago. We'll put it
years ago. We'll put it >> here back. Okay. And um no uh so maybe
>> here back. Okay. And um no uh so maybe this comes back to I have absolute pitch
this comes back to I have absolute pitch so I know what frequencies I'm singing
so I know what frequencies I'm singing but I also recognize by having absolute
but I also recognize by having absolute pitch I know my brain is just a little
pitch I know my brain is just a little different. Again what you ask me what
different. Again what you ask me what threads drive me. It's always been we we
threads drive me. It's always been we we do experience the world differently. And
do experience the world differently. And I believe that our success, everyone's
I believe that our success, everyone's success and the success of our growth as
success and the success of our growth as humans is is partly dependent on how we
humans is is partly dependent on how we use technology to help you know improve
use technology to help you know improve and optimize each of us with you know
and optimize each of us with you know the different variables we need. Right?
the different variables we need. Right? So different species and how they
So different species and how they respond to sound is very interesting to
respond to sound is very interesting to me. And as much as you I know Andy you
me. And as much as you I know Andy you look at how different species respond to
look at how different species respond to color and to information in the world be
color and to information in the world be it cuttlefish or such I have jellyfish
it cuttlefish or such I have jellyfish too and I can see how they you know
too and I can see how they you know their pulsing rates change with their
their pulsing rates change with their photo receptors when they uh you know
photo receptors when they uh you know with different light colors it's very
with different light colors it's very obvious that some clearly make you know
obvious that some clearly make you know that they are under when they're under
that they are under when they're under stress versus when they're in a a more
stress versus when they're in a a more calming state. And so it's like
calming state. And so it's like understanding the stimula in our world
understanding the stimula in our world that shape us, those changes is a huge
that shape us, those changes is a huge part of being human. In my perspective,
part of being human. In my perspective, in this case, this happens to be an orb
in this case, this happens to be an orb spider, the one I sing to. And when I
spider, the one I sing to. And when I hit about 880 hertz, uh you will see the
hit about 880 hertz, uh you will see the spider kind of dances. But what this
spider kind of dances. But what this particular species and not all spiders
particular species and not all spiders will do this is predated on by
will do this is predated on by echolocating bats and birds which makes
echolocating bats and birds which makes sense that then you know it tunes its
sense that then you know it tunes its web effectively and and the orb weavers
web effectively and and the orb weavers are all over California. It's what they
are all over California. It's what they they show up a lot in around uh
they show up a lot in around uh Thanksgiving if you are October,
Thanksgiving if you are October, November for anyone that's on the you
November for anyone that's on the you know out here on the west coast. Um
know out here on the west coast. Um they're not bad spiders. They they are
they're not bad spiders. They they are not spiders you need to get rid of.
not spiders you need to get rid of. They're totally happy spiders. or some,
They're totally happy spiders. or some, you know, that maybe you're should worry
you know, that maybe you're should worry about more. Anyhow, they tune their webs
about more. Anyhow, they tune their webs to resonate like a violin. And when, you
to resonate like a violin. And when, you know, you'll see it as I hit a certain
know, you'll see it as I hit a certain frequency, it'll effectively tell me to
frequency, it'll effectively tell me to to to go away. And uh it's it's it's a
to to go away. And uh it's it's it's a pretty interesting sort of deterministic
pretty interesting sort of deterministic response. Other insects do different
response. Other insects do different things. Uh the one kind of uh funny for
things. Uh the one kind of uh funny for that was when my daughter was I think at
that was when my daughter was I think at the time she was about two and a half or
the time she was about two and a half or three and she kind of adopted
three and she kind of adopted uh asking me when we would see spiders
uh asking me when we would see spiders if it was the kind we would we should
if it was the kind we would we should sing to or the kind we shouldn't touch.
sing to or the kind we shouldn't touch. >> Those were the two classes.
>> Those were the two classes. >> So uh amazing. So if I understand
>> So uh amazing. So if I understand correctly, these orb spiders use their
correctly, these orb spiders use their web.
web. >> Yes.
>> Yes. more or less as an instrument to detect
more or less as an instrument to detect certain sound frequencies in their
certain sound frequencies in their environment.
environment. >> Resonances absolutely
>> Resonances absolutely >> so that they can respond appropriately.
>> so that they can respond appropriately. Yeah.
Yeah. >> Either by raising their legs to protect
>> Either by raising their legs to protect themselves or to attack or whatever it
themselves or to attack or whatever it is that that the spiderweb is a
is that that the spiderweb is a functional thing not just for catching
functional thing not just for catching prey. It's it's a detection device also.
prey. It's it's a detection device also. And we know that because when prey are
And we know that because when prey are in caught in a spiderweb, they wiggle
in caught in a spiderweb, they wiggle and then the spider goes over to it and
and then the spider goes over to it and wraps it and and eats it. But um but the
wraps it and and eats it. But um but the idea that it would be tuned to
idea that it would be tuned to particular frequencies is really wild.
particular frequencies is really wild. >> Yeah. Not just any vibration, right? You
>> Yeah. Not just any vibration, right? You know, there's the idea that there's any
know, there's the idea that there's any vibration, I know I've got, you know,
vibration, I know I've got, you know, food somewhere, I should go to that food
food somewhere, I should go to that food source, but instead it's something that
source, but instead it's something that if I experience a threat or something,
if I experience a threat or something, I'm going to behave. And that is a more
I'm going to behave. And that is a more selective, you know, response that I've
selective, you know, response that I've tuned it towards.
tuned it towards. >> It's so interesting because if I just
>> It's so interesting because if I just transfer it to the visual domain, it's
transfer it to the visual domain, it's like, yeah, of course, like if an
like, yeah, of course, like if an animal, including us, sees something
animal, including us, sees something like a looming object coming at us.
like a looming object coming at us. >> Yeah. uh closer to dark, we our
>> Yeah. uh closer to dark, we our immediate response is to either freeze
immediate response is to either freeze or flee. Like that's just what we do.
or flee. Like that's just what we do. The looming response is one of the most
The looming response is one of the most fundamental responses, but that's in the
fundamental responses, but that's in the visual domain. So the fact that there
visual domain. So the fact that there would be auditory cues that would bring
would be auditory cues that would bring about what you said the sort of
about what you said the sort of deterministic responses seems very real.
deterministic responses seems very real. I feel like that there the whale of a of
I feel like that there the whale of a of somebody in pain.
somebody in pain. >> Yes.
>> Yes. >> Evokes a certain response. the um
>> Evokes a certain response. the um yesterday there was a lot of noise
yesterday there was a lot of noise outside my window at night and I there
outside my window at night and I there was a moment where I couldn't tell were
was a moment where I couldn't tell were these um shouts of glee or shouts of
these um shouts of glee or shouts of fear
fear >> and I like I can't do and then I heard
>> and I like I can't do and then I heard this like kind of like uh highpitch
this like kind of like uh highpitch um fluttering that came after the scream
um fluttering that came after the scream and I realized these were kids playing
and I realized these were kids playing in the in the alley outside my house and
in the in the alley outside my house and I went and looked I was like oh yeah
I went and looked I was like oh yeah they're they're definitely playing but I
they're they're definitely playing but I knew even before I went and looked based
knew even before I went and looked based on the kind of the the flutter of sound
on the kind of the the flutter of sound that came after the like the the shriek.
that came after the like the the shriek. It was like and then it was it was like
It was like and then it was it was like I can't I can't reproduce the sound at
I can't I can't reproduce the sound at that high frequency.
that high frequency. >> That's that's um
>> That's that's um >> so the idea that this would be true all
>> so the idea that this would be true all the time is uh is super interesting. We
the time is uh is super interesting. We just don't tend to focus just on our
just don't tend to focus just on our hearing unless of course somebody's
hearing unless of course somebody's blind in which case they have to rely on
blind in which case they have to rely on it much more.
it much more. >> So two interesting things to go with
>> So two interesting things to go with that. So like crickets for example,
that. So like crickets for example, crickets um have biodal neurons that
crickets um have biodal neurons that have sort of peaks in two different
have sort of peaks in two different frequency ranges for the same neuron.
frequency ranges for the same neuron. And each frequency range will elicit a
And each frequency range will elicit a completely different behavior to when
completely different behavior to when when so you've got a peak at 6k and
when so you've got a peak at 6k and you've got a peak at 40k and cricket and
you've got a peak at 40k and cricket and this is the same neuron. cricket hears
this is the same neuron. cricket hears 40k from a speaker, run over to it
40k from a speaker, run over to it because that's got to be my bait or some
because that's got to be my bait or some you know that and you hear 40k and they
you know that and you hear 40k and they run away and you know it's very
run away and you know it's very predictive behavior. Uh I spend a lot of
predictive behavior. Uh I spend a lot of well I spent a good period of time
well I spent a good period of time working with non- primate non-human
working with non- primate non-human primate species marmicetses. Marmicetses
primate species marmicetses. Marmicetses are very interesting when you get to a
are very interesting when you get to a more sophistic you know you know a more
more sophistic you know you know a more sophisticated neural system. Um, but
sophisticated neural system. Um, but they're you marmicetses are very social.
they're you marmicetses are very social. You know, it's critical to their
You know, it's critical to their happiness. If you ever see a single
happiness. If you ever see a single marmicet in the zoo or something, that's
marmicet in the zoo or something, that's a very unhappy uh animal. But they're
a very unhappy uh animal. But they're they're native to the Amazon. You know,
they're native to the Amazon. You know, new world monkeys native to Brazil and
new world monkeys native to Brazil and the Amazon, but they're aroreal. They
the Amazon, but they're aroreal. They live in trees and they're very social.
live in trees and they're very social. So that kind of can, you know, be in
So that kind of can, you know, be in conflict with each other because you're,
conflict with each other because you're, you know, in dense foliage, but yet you
you know, in dense foliage, but yet you need to communicate. So they've evolved
need to communicate. So they've evolved very interesting systems to be able to
very interesting systems to be able to you know achieve what they needed to
you know achieve what they needed to which one um they if you ever see a
which one um they if you ever see a marmicetses they're very stoic unlike
marmicetses they're very stoic unlike macac monkeys that you know often have a
macac monkeys that you know often have a lot of visual you know expression of how
lot of visual you know expression of how they're feeling. Armicetses always look
they're feeling. Armicetses always look about the same and um but their their
about the same and um but their their vocalizations are almost like bird song
vocalizations are almost like bird song and they're very rich in the information
and they're very rich in the information that they're you know communicating.
that they're you know communicating. They also have a f pherommonal system
They also have a f pherommonal system like you know they um thought you can
like you know they um thought you can have a dominant female in the colony who
have a dominant female in the colony who may not be because you have to have ways
may not be because you have to have ways of communic when one sense is
of communic when one sense is compromised the other senses sort of
compromised the other senses sort of rise up to help assure that the success
rise up to help assure that the success of what that s you know that that
of what that s you know that that species or system needs is going to be
species or system needs is going to be you know thrive. And in the case of
you know thrive. And in the case of marmicetses, you can have the dominant
marmicetses, you can have the dominant female effectively causes the ovulation
female effectively causes the ovulation of like the biology to change of all the
of like the biology to change of all the other females and you can have a female
other females and you can have a female that you put just in the same proximity
that you put just in the same proximity but now as part of a different group and
but now as part of a different group and her biology will change. I mean it's
her biology will change. I mean it's very powerful the pherommonal
very powerful the pherommonal interactions that happen in the because
interactions that happen in the because those are things that can travel even
those are things that can travel even when I can't see you. One thing when I
when I can't see you. One thing when I was working with them, you know, that I
was working with them, you know, that I thought was and and I never I like
thought was and and I never I like writing pads more than publishing
writing pads more than publishing papers. So, but these things are real
papers. So, but these things are real because I was studying pupilometry is is
because I was studying pupilometry is is understanding the power of the you know
understanding the power of the you know their sacads. I could know what they
their sacads. I could know what they were hearing based on their eye
were hearing based on their eye movements, right? So, if I play
movements, right? So, if I play marmicetses have, you know, call some of
marmicetses have, you know, call some of their calls are really antipinal.
their calls are really antipinal. They're to see, hey, are you out there?
They're to see, hey, are you out there? Am I alone? Who else is around?
Am I alone? Who else is around? >> Texting for humans. Yeah.
>> Texting for humans. Yeah. >> Yeah. And sometimes it's light or
>> Yeah. And sometimes it's light or sometimes it might be like oh you know
sometimes it might be like oh you know from be careful there's you know there's
from be careful there's you know there's somebody you know around that we got to
somebody you know around that we got to watch out for maybe there's a leopard on
watch out for maybe there's a leopard on the ground or somebody something right
the ground or somebody something right and then sometimes it's like you're in
and then sometimes it's like you're in my face get out of here now right and
my face get out of here now right and those are three different things and I
those are three different things and I can play that to you and I can tell you
can play that to you and I can tell you without hearing it and I know exactly
without hearing it and I know exactly what's being heard in the case of the
what's being heard in the case of the antipol hey are you out there you see
antipol hey are you out there you see like the the eye will just start
like the the eye will just start scanning back and forth right because
scanning back and forth right because that's the right movement I'm looking
that's the right movement I'm looking for where's this coming from?
for where's this coming from? >> Yeah. They paired the right eye movement
>> Yeah. They paired the right eye movement with the right sound.
with the right sound. >> Exactly. In the case of, you know, look,
>> Exactly. In the case of, you know, look, it's um you know, there's something to
it's um you know, there's something to be scar threatened of. You're going to
be scar threatened of. You're going to see dilation and you're also going to
see dilation and you're also going to see some scanning, but it's not as slow.
see some scanning, but it's not as slow. It's a lot faster because there's a
It's a lot faster because there's a threat to me. I my you know, my
threat to me. I my you know, my autonomic system and my cognitive system
autonomic system and my cognitive system are like be reacting differently. And in
are like be reacting differently. And in the case of you're in my face, it's
the case of you're in my face, it's going to be, you know, without even so
going to be, you know, without even so without seeing you, if I hear another,
without seeing you, if I hear another, you know, sort of aggressive sound, I'm
you know, sort of aggressive sound, I'm going to react. I'm going to be, you
going to react. I'm going to be, you know, I'm not scanning anywhere, but I
know, I'm not scanning anywhere, but I my dilation is going to be fast and, you
my dilation is going to be fast and, you know, my and I'm also going to be much
know, my and I'm also going to be much more on top of things. But we do this
more on top of things. But we do this as, you know, humans too, right? And
as, you know, humans too, right? And it's like I you can you walk into a
it's like I you can you walk into a business meeting, you walk into a
business meeting, you walk into a conference room and you know it's these
conference room and you know it's these subtle cues that are con you we can't
subtle cues that are con you we can't don't always suppress them. We show them
don't always suppress them. We show them whether we think we do or we don't. But
whether we think we do or we don't. But you know when you look at species like
you know when you look at species like that it's very much like okay you know
that it's very much like okay you know there's there's a lot of you know
there's there's a lot of you know sophistication in and how their bodies
sophistication in and how their bodies are helping them be successful even in a
are helping them be successful even in a world or an environment that has a lot
world or an environment that has a lot of things that that could maybe you know
of things that that could maybe you know come after them. So interesting to think
come after them. So interesting to think about that in terms of um our own human
about that in terms of um our own human behavior and what we're optimizing for,
behavior and what we're optimizing for, especially as all these technologies
especially as all these technologies come on board and are sure to come on
come on board and are sure to come on board even more quickly. Um Poppy, thank
board even more quickly. Um Poppy, thank you so much for coming here today to
you so much for coming here today to educate us about what you've done,
educate us about what you've done, what's here now, what's to come. We
what's here now, what's to come. We covered a lot of different territories
covered a lot of different territories and I I'm glad we did because um you
and I I'm glad we did because um you have expertise in a lot of areas and I
have expertise in a lot of areas and I love that you are constantly thinking
love that you are constantly thinking about technology development and I you
about technology development and I you know I drew a little diagram for myself
know I drew a little diagram for myself that I'll just describe for people
that I'll just describe for people because um if I understood correctly one
because um if I understood correctly one of the reasons you got into neuroscience
of the reasons you got into neuroscience and research at all is about this um
and research at all is about this um interface between inputs and us and what
interface between inputs and us and what sits in between those two things is this
sits in between those two things is this incredible feature of our nervous
incredible feature of our nervous systems which is neuroplasticity
systems which is neuroplasticity or what I sometimes like to refer to as
or what I sometimes like to refer to as self-directed plasticity because unlike
self-directed plasticity because unlike other species
other species we can decide what we want to change and
we can decide what we want to change and make the effort to adopt a second
make the effort to adopt a second map of the auditory world or visual
map of the auditory world or visual world or or take on a new uh a new set
world or or take on a new uh a new set of learnings in any domain and we can do
of learnings in any domain and we can do it if we put our mind to it if the
it if we put our mind to it if the incentives are high enough we can do it
incentives are high enough we can do it and at the same time neuroplasticity is
and at the same time neuroplasticity is always occurring based on the things
always occurring based on the things we're bombarded with new technology. So,
we're bombarded with new technology. So, we have to be aware of how we are
we have to be aware of how we are changing and we need to intervene at
changing and we need to intervene at times and and leverage those things for
times and and leverage those things for our health. So, thank you so much for
our health. So, thank you so much for doing the work that you do. Thank you
doing the work that you do. Thank you for coming here to educate us on them
for coming here to educate us on them and um keep us posted. We'll provide
and um keep us posted. We'll provide links to you singing to uh spiders and
links to you singing to uh spiders and and all the rest. My mind's blown. Thank
and all the rest. My mind's blown. Thank you so much.
you so much. >> Thank you, Eddie. Great to be here.
>> Thank you, Eddie. Great to be here. >> Thank you for joining me for today's
>> Thank you for joining me for today's discussion with Dr. Poppyrcum. To learn
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