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AI Simply Explained In 12 Minutes
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AI is simpler than you think let me show
so chapter one the types of AI before AI
machines were built to follow our
Specific Instructions they did things
based on conditions we programmed into
them they were rules-based but AI is
different instead of giving machines
instructions on what to do and how to do
it we trained them to think and do
things on their own like raising a child
this is why people call AI a black box
it turns your input into an output based
on similar things you've shown it in the
past but you don't know what's happening
in the Box exactly AI basically combines
your input in the moment with the data
it's been trained on to generate an
output and there's three types of AI
boxes you should know about and you can
differentiate them based on their output
first we have predictive AI which labels
Things based on prior data like marking
an email as spam identifying someone in
a picture or recommending what you
should watch generative AI creates new
content like text images and videos a
gentic AI outputs actions based on a
given task like self-driving cars they
take a destination as an input then they
plan the trip drive the car while
stopping at traffic lights following
speed limits and not running over
pedestrians another example is AI agents
like Cloud computer use if you give them
a task they can access your computer
apps or accounts to execute actions on
your behalf these are the types of AI
but you can also Define AI by its level
of intelligence narrow AI is built for
specific tasks which applies to all AI
systems we have today artificial general
intelligence or AGI is theoretical for
now but would match human intelligence
across all tasks and scenarios but
nobody agrees on a formal definition of
what AGI actually is and finally
artificial super intelligence is also
theoretical but would surpass human
intelligence by far chapter 2 how AI is
created imagine adopted a pet robot and
you wanted to teach him how to think and
do things on his own this is called
machine learning and there's three ways
you can do this you could take your
robot around the world and point at
every object to teach him what it is
over and over again you're spoon feeding
him information until he recognizes what
each thing is this is supervised
learning and you're feeding a machine a
sandwich of labeled data like reviews
emails and even x-rays you could also
take a hands-off approach by letting him
learn on his own imagine you were living
the house and you told your robot to
sort the dishes it would start grouping
things based on similarities in shape
size and color this is UN supervised
learning and it Powers systems that
group similar items when you're shopping
browsing pictures or getting song
recommendations the final way you could
teach him is with positive and negative
reinforcement like a coach imagine you
asked your robot to make you a smoothie
it has no idea how to do it at first so
it starts experimenting with random
recipes after you taste each smoothie
you give it a thumbs up or a thumbs down
over time the robot learns to make
smoothies just the way you like them
this is reinforcement learning and it's
one of the ways chat GPT was trained to
improve its answers and the reason why
your algorithm knows you too well
creating intelligent machines isn't just
about the Training Method it's also
about the quality of data that you use
if your robot learns from low quality
information he'll only give you low
quality results garbage in garbage out
high quality data is more valuable than
ever but we might run out of it soon one
research group predicts that will run
out of publicly available data between
2026 and 2032 at the current pace of AI
development this is why AI companies are
signing expensive licensing deals to
find new training data the other
solution being explored is using
synthetic data generated by AI to create
new training data chapter three how AI
becomes biased training an AI system is
like raising a child the parents play a
big role in deciding the values they
teach them what they can watch on TV and
where they live and these factors shape
a child's worldview imagine a child who
grew up in Antarctica and the only
animal they ever saw was a penguin they
would think that penguins are the only
animals in the world but they have an
incomplete worldview because they
haven't seen all the other animals that
exist AI has the same problem which can
limit its abilities but also create
unfavorable outcomes for some people and
this bias can come from different
sources first the beliefs and values of
people can influence how AI systems are
designed and trained if the child's
father was an avid penguin Enthusiast he
might constantly talk about how penguins
are the best animals on Earth the child
would adopt the same belief because it's
all they've been exposed to and this is
the same reason why some chat Bots will
respond differently if you ask them
controversial questions bias can also
exist within the training data if it has
any imbalances that ignore the nuances
of the world if the parents only give
their child books about penguins there's
no way for them to learn about new and
different species this is why language
models might give you lower quality
responses in languages other than
English bias can also emerge from
Incorrect and overs simplistic rules
that machine identifies during training
the child who has only seen Penguins
would think that animals are only
creatures that are black white and have
flippers and this is why image
generators associate certain jobs with
specific genders and races unfortunately
there's no reliable solution to
eliminate bias from AI after all bias is
part of being human and if we're trying
to replicate the way humans think and
act bias is inevitable AI is only a
mirror image of us after all chapter 4
how AI generates text imagine you wanted
to become a DJ you would start by
listening to as many songs as possible
you would break down each song to
analyze the sequence of Beats and
instruments that were used and after
lots of listening you become comfortable
creating unique beats and mixes that
sound good to the ear large language
models are built in a similar way
they're trained on millions of pages of
text each piece of text is broken down
into tokens which are numerical
representations of words with billions
of examples machines can remember the
sequences of tokens to create language
that sounds good to the ear asking AI to
generate text for you is like requesting
a song from from a DJ based on your
request the songs learned during
training and the mixers the DJ creates a
unique music mix for you with language
models your text prompt is combined with
the training data and parameters to
create a new mix of text text generation
is like a math formula it takes your
inputs multiplies them by specific
parameters to give you a token of text
except that language models have
billions of parameters AI generates this
new mix of text by predicting the
sequence of tokens one at a time based
on all previous tokens and each token is
the result of hundreds of billions of
calculations like a DJ anxiously trying
to keep the crowd going after every beat
switch but sometimes things go wrong
since every token is a best guess these
guesses can be wrong when asking for
factual information these are called
hallucinations remember that language
models are text generators not truth
generators chapter 5 how AI generates
images imagine you were a sculptor and
someone asks you to make a statue you
would get a stone block and start
chisling away until you get your
Masterpiece AI generates images in a
similar way it starts with an image full
of random pixels and gradually adjusts
them until you get the final result but
how does it do that the process is
called diffusion these models are
trained on billions of images with text
descriptions but machines don't see
images like we do they see them as grids
of pixels and each pixel is represented
by three numbers for red green and blue
when you train an AI model on billions
of images it starts recognizing what
words are associated with which pixel
patterns and values this creates a
multi-dimensional map called the latent
space and it Maps specific features like
a shape artistic style or color to their
pixel values and patterns to capture all
these fine details these models are
trained by taking each image and slowly
randomizing all the pixels until it
becomes Pure Noise then the model is
trained to reverse the process to
reconstruct the original image so when
you ask AI to make an image for you it's
replicating that reversal process by
taking an image with random pixels
adjusting the pixel values based on the
latent space until it constructs the
image you want Che chapter 6 the energy
cost of AI every time you ask AI a
question that request is sent to a Data
Center and processed by thousands of AI
chips because the hardware in our phones
and laptops isn't powerful enough to do
it these data centers need two things to
operate first they need electricity to
run in 2024 data centers use 2% of the
world's electricity but this number is
for all data centers that keep the
internet running we don't know what
share of that can be attributed to AI
alone it's estimated that a single AI
request uses 10 times more energy than a
standard search but this estimate is
based on AI models from 2023 which are
much smaller than the ones we have today
second they need cooling systems to
prevent overheating most cooling systems
use fresh water and the average data
center consumes 300,000 gallons of water
every day which is equivalent to 19,000
showers and depending on the cooling
method used data centers may lose some
or all of this water and need constant
replenishment unfortunately we don't
have precise or reliable numbers for how
much energy or water AI uses alone
because tech companies aren't revealing
the size of their newest AI models what
we do know is that AI developments and
usage are likely to keep increasing over
the next few years which will require
more data centers AKA more electricity
and water than before thankfully some
solutions are starting to emerge to make
AI more energy efficient and here are
three you should know about first we
could run AI on our personal devices
instead of data centers because they've
been getting more powerful Hardware this
is called Edge AI and we're already
seeing signs of it for example the new
iPhone can run basic AI features on your
device directly without sending those
requests to a data center another
solution is called Model distillation
the idea is to create smaller models
based on large models to make them more
energy efficient and cost efficient for
specific tasks because we don't always
need the biggest and most advanced model
if a task is simple the same way we
don't need a Formula 1 car to deliver a
pizza finally the AI chips and data
centers are getting more efficient which
means they need less energy to produce
the same output over the past 8 years
the energy cost to generate one token on
an Nvidia GPU went down from 177,000
Jews to 0.4 chapter 7 a brief history of
AI the story of intelligent machines
begins in 1950 When Alan Turing famously
asked can machines think and proposed
the Turing test as a measure of
humanlike intelligence but the term
artificial intelligence wasn't born
until 1956 during a summer research
project at Dartmouth College from the '
50s to the 70s the AI field received
generous Government funding from DARPA
and the field witnessed its first
breakthroughs like Eliza a chatbot that
could talk to you like a psychotherapist
the hype and expectations around AI at
the time were Skyhigh High some
researchers even made bold predictions
like saying that machines would be as
smart as we are within 8 years but the
industry faced a massive blow once it
couldn't deliver on its promises two
government reports from the US and the
UK concluded that AI was more expensive
Ive less reliable slower and not solving
any useful problems yet this sent the
industry into its first winter where
funding dried up until the80s the field
went through a slight Resurgence After
Japan invested heavily in its Computing
industry which pressured the UK and the
us to do the same but they failed to
deliver on their promises again and
entered a second winter at this point 40
years had gone by with no fruitful
applications of AI and the industry was
at Rock Bottom some computer scientists
were even ashamed to say that they
worked on AI and avoided the term
completely but things were about to take
a dramatic turn in the late 9s because
the perfect storm was brewing first
computers were becoming way more
powerful quickly computer chips today
are 40 million times more powerful than
they were 50 years ago second there was
way more Digital Data available as we
moved all of our lives and work online
which could be collected as training
data and both of these things enabled
machine learning algorithms that can
replic ate and simulate intelligence
since the 2010s the AI field has slowly
recaptured The public's attention and
became an overnight sensation with the
release of chat GPT big tech companies
are racing to the top everyone has an AI
startup and people are Torn Between the
benefits AI can provide the harm it
creates and most importantly the
existential question if AI can do it all
what am I good for the future is hard to
predict but it feels like the real story
is just beginning I hope all of this
gives you a good understanding of what
AI actually is and thank you for
watching my first YouTube video if you
have any questions or feedback leave
them in the comments I'll take a look at
them so I can keep making better videos
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