>> Well, let me tell you why I think it's superior.
superior. >> Okay.
>> Okay.
>> Um, it's digital. And because it's
digital, you can have you can simulate a
neural network on one piece of hardware. >> Yeah.
>> Yeah.
>> And you can simulate exactly the same
neural network on a different piece of hardware.
hardware.
>> So you can have clones of the same intelligence.
intelligence.
Now you could get this one to go off and
look at one bit of the internet and this
other one to look at a different bit of
the internet. And while they're looking
at these different bits of the internet,
they can be syncing with each other. So
they keep their weights the same, the
connection strengths the same. Weights
are connection strengths. >> Mhm.
>> Mhm.
>> So this one might look at something on
the internet and say, "Oh, I'd like to
increase this strength of this
connection a bit." And it can convey
that information to this one. So it can
increase the strength of that connection
a bit based on this one's experience.
>> And when you say the strength of the
connection, you're talking about learning.
learning.
>> That's learning. Yes. Learning consists
of saying instead of this one giving 2.4
four votes for whether that one should
turn on. We'll have this one give 2.5
votes for whether this one should turn on.
on.
>> And that will be a little bit of learning.
learning.
>> So these two different copies of the
same neural net
are getting different experiences.
They're looking at different data, but
they're sharing what they've learned by
averaging their weights together. >> Mhm.
>> Mhm.
>> And they can do that averaging at like a
you can average a trillion weights. When
you and I transfer information, we're
limited to the amount of information in
a sentence. And the amount of
information in a sentence is maybe a 100
bits. It's very little information.
We're lucky if we're transferring like
10 bits a second.
>> These things are transferring trillions
of bits a second. So, they're billions
of times better than us at sharing information.
information.
And that's because they're digital. And
you can have two bits of hardware using
the connection strengths in exactly the
same way. We're analog and you can't do
that. Your brain's different from my
brain. And if I could see the connection
strengths between all your neurons, it
wouldn't do me any good because my
neurons work slightly differently and
they're connected up slightly differently.
differently. >> Mhm.
>> Mhm.
>> So when you die, all your knowledge dies
with you. When these things die, suppose
you take these two digital intelligences
that are clones of each other and you
destroy the hardware they run on. As
long as you've stored the connection
strength somewhere, you can just build
new hardware that executes the same
instructions. So, it'll know how to use
those connection strengths and you've
recreated that intelligence. So, they're
immortal. We've actually solved the
problem of immortality, but it's only
for digital things.
>> So, it knows it will essentially know
everything that humans know but more
because it will learn new things.
>> It will learn new things. It would also
see all sorts of analogies that people
probably never saw.
So, for example, at the point when GPT4
couldn't look on the web, I asked it,
"Why is a compost heap like an atom bomb?"
bomb?"
Off you go.
>> I have no idea.
>> Exactly. Excellent. Most that's exactly
what most people would say. It said,
"Well, the time scales are very
different and the energy scales are very
different." But then I went on to talk
about how a compost he as it gets hotter
generates heat faster and an atom bomb
as it produces more neutrons generates
neutrons faster.
>> And so they're both chain reactions but
at very different time in energy scales.
And I believe GPT4 had seen that during
its training.
It had understood the analogy between a
compost heap and an atom bomb. And the
reason I believe that is if you've only
got a trillion connections, remember you
have 100 trillion.
>> And you need to have thousands of times
more knowledge than a person, you need
to compress information into those
connections. And to compress
information, you need to see analogies
between different things. In other
words, it needs to see all the things
that are chain reactions and understand
the basic idea of a chain reaction and
code that code the ways in which they're
different. And that's just a more
efficient way of coding things than
coding each of them separately.
>> So it's seen many many analogies
probably many analogies that people have
never seen. That's why I also think that
people who say these things will never
be creative. They're going to be much
more creative than us because they're
going to see all sorts of analogies we
never saw. And a lot of creativity is
about seeing strange analogies. If you
love the D CEO brand and you watch this
channel, please do me a huge favor.
become part of the 15% of the viewers on
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