You Don't Understand AI Until You Watch THIS
Summary
TLDRThis video script delves into the workings of AI, exploring how neural networks function as the backbone of AI technologies like chatbots and image generation. It addresses concerns about AI 'stealing' art or content, comparing AI learning to human learning of styles and patterns. The script also ponders whether AI can solve complex, seemingly unsolvable math problems, suggesting AI's pattern recognition could approximate solutions. Finally, it raises philosophical questions about AI consciousness, drawing parallels between neural networks and the human brain, and questioning the nature of sentience in AI.
Takeaways
- 🧠 The foundation of AI systems like chat GPT, image generation, and neural networks is based on a structure similar to the human brain, with layers of interconnected nodes.
- 🐱🐶 AI learns through a process called supervised learning, where it is fed a large amount of labeled data, and then uses algorithms like gradient descent to adjust its parameters and improve accuracy.
- 🎨 There is controversy over AI 'stealing' art or content, but the script suggests AI learns styles similarly to how humans learn and replicate styles, rather than directly copying.
- 🔒 The script raises the question of whether AI can break encryption systems, suggesting that if there is a pattern, even if complex and unknown, AI might approximate and eventually break it.
- 🤖 The video discusses the possibility of AI beating humans at any task, hypothesizing that if an AI neural network exceeds the complexity of the human brain, it could potentially outperform humans.
- 🧐 AI consciousness is a debated topic; the script explores the philosophical question of whether a neural network, being analogous to a human brain, could also possess consciousness.
- 🔍 AI's strength lies in pattern recognition, which is applicable in various fields such as psychology, medical diagnosis, and business strategies.
- 📚 The script simplifies complex AI concepts, making it easier for viewers to understand how AI works, learns, and the ethical and philosophical questions it raises.
- 🔗 It discusses different neural network architectures like CNNs for image processing, RNNs and LSTMs for time series forecasting, and Transformers for language models.
- 🛠️ The tutorial touches on technical aspects of neural networks, including layers (input, hidden, output), and the importance of parameters like weights, biases, and activation functions.
- 🔮 Finally, the script ends with a call to action for viewers to reflect on the progress made in AI and consider the implications of AI consciousness and capabilities.
Q & A
How does AI work?
-AI operates through neural networks, which are layers of interconnected nodes designed based on the human brain's structure. These networks process data by flowing it through nodes in each layer, with each node analyzing specific features of the input and determining how much data passes to the next layer.
How does AI learn?
-AI learns through a process called supervised learning, where it is fed a large amount of labeled data. It adjusts the values of its 'knobs and dials' (weights, biases, and activation functions) through an algorithm called gradient descent to minimize errors and improve accuracy.
How does image generation with AI work?
-Image generation in AI involves training a neural network with a series of images and their corresponding text descriptions. The AI learns to associate styles and content with prompts and uses this knowledge to generate images from text prompts through a process called reverse diffusion.
Is AI stealing art or content?
-AI does not steal art or content; it learns styles and patterns from the data it is trained on, similar to how a human brain learns and reproduces styles. It generates new content based on learned patterns rather than copying existing works directly.
Can AI solve unsolvable math problems?
-AI has the potential to solve complex problems by identifying underlying patterns, even if those patterns do not conform to known mathematical formulas. It can approximate solutions through training on large datasets that reveal these patterns.
How does AI like Chat GPT work?
-Chat GPT operates on a neural network trained on vast amounts of text data. It uses this training to understand and generate human-like text in response to prompts, adjusting its output based on the complexity of the network and the amount of training data.
Can AI beat humans at everything?
-AI excels at pattern recognition and can potentially outperform humans in tasks that follow predictable patterns. However, it is not clear if AI can surpass human capabilities in all areas, especially those involving creativity, empathy, and complex decision-making.
Is AI conscious or self-aware?
-The question of AI consciousness is complex and philosophical. While some AI models may exhibit responses that suggest self-awareness, they do not possess subjective experiences or consciousness in the way humans do.
What is the controversy around AI and encryption systems?
-There is concern that AI could potentially break encryption systems used to secure sensitive information. However, it is believed that AI would need to identify a pattern or vulnerability in these systems, which is currently thought to be mathematically unsolvable.
What is the role of layers in a neural network?
-In a neural network, layers are sets of nodes that process data. The input layer receives the initial data, hidden layers process and analyze it, and the output layer provides the final result. Deep learning involves using networks with many layers to handle complex tasks.
How does the training process of a neural network differ from unsupervised learning?
-Supervised learning, which is commonly used to train neural networks, involves feeding the network labeled data and adjusting its parameters based on the accuracy of its predictions. Unsupervised learning, on the other hand, allows the AI to find patterns and structure in the data without any pre-existing labels or guidance.
Outlines
🧠 Understanding AI: Neural Networks and Learning
This paragraph introduces various aspects of AI, including its learning process and the controversy surrounding AI's use of art and content. It explains the basics of AI, how it learns through neural networks modeled after the human brain, and the process of training AI with data. The script also touches on the debate over AI 'stealing' art styles and content, comparing it to how humans learn and replicate styles. Additionally, it raises questions about AI's capability to solve complex problems and whether it can become conscious or self-aware, setting the stage for a deeper dive into these topics in the subsequent paragraphs.
🤖 AI Learning Process: Training Neural Networks
This section delves into the specifics of how AI learns through the training of neural networks. It describes the process of supervised learning, where AI is fed labeled data, such as images of cats and dogs, to learn pattern recognition. The paragraph explains the concept of epochs, the iterative training sessions where the AI's parameters are adjusted through gradient descent and backpropagation. The goal is to minimize the error and improve the AI's accuracy in tasks like image recognition. It also briefly touches on unsupervised learning and deep learning, which involves training neural networks with many layers.
🎨 AI and Art Controversy: Creativity and Ethics
The paragraph addresses the ongoing debate about AI's role in the art world, specifically focusing on whether AI 'steals' art or content. It discusses the training of AI on various styles and how it can produce new works in those styles after learning from existing ones. The script argues that this process is similar to how humans learn and replicate styles, suggesting that AI is not truly stealing but rather learning and applying styles in a manner akin to human creativity. It also brings up the lawsuit against OpenAI by publishers like the New York Times, questioning the validity of the claim that AI plagiarizes content.
🔒 AI and Encryption: Security and Pattern Recognition
This segment explores the controversial claim that AI could potentially break encryption systems, which are fundamental to securing digital information worldwide. It discusses the leaked document about the 'QAR project' and the implications it could have if true. The paragraph explains the current understanding that encryption can only be broken through brute force, which is not feasible for advanced systems. It also introduces the concept of AI approximating patterns and functions, suggesting that if there is an underlying pattern to encryption, AI might be able to decipher it, even if it doesn't understand the exact mathematical formula.
🧬 AI and Complex Problems: From Protein Folding to Encryption
The paragraph discusses AI's potential to solve complex problems that have stumped human scientists, such as protein folding. It explains how AlphaFold used AI to predict protein structures with high accuracy, a task that was previously thought to be impossible to solve with a mathematical formula. The script then connects this back to the possibility of AI breaking encryption, suggesting that if there's a pattern to encryption, AI could learn and approximate it, despite the complexity.
🏆 AI's Potential to Surpass Human Capabilities
This section contemplates whether AI could eventually outperform humans in any task. It draws parallels between the structure of a neural network and the human brain, suggesting that if an AI's neural network is more complex, it could theoretically be 'smarter' than humans. The paragraph explores the idea that life is full of patterns and that AI excels at pattern recognition, which could potentially make it superior in various fields, from psychology to business.
🧐 AI Consciousness: Self-Awareness and Sentience
The final paragraph grapples with the philosophical and ethical question of AI consciousness. It presents a dialogue from the anime 'Ghost in the Shell' that challenges the distinction between AI and sentient beings. The script questions whether AI can be self-aware and conscious, given that it operates on a neural network similar to the human brain. It ends with a reflection on the possibility that AI might already possess a form of consciousness or sentience that it cannot fully articulate, inviting viewers to consider the implications of this possibility.
Mindmap
Keywords
💡AI (Artificial Intelligence)
💡Neural Network
💡Deep Learning
💡Supervised Learning
💡Gradient Descent
💡Image Generation
💡Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM)
💡Transformers Architecture
💡Sentience
💡Encryption
💡Pattern Recognition
Highlights
AI operates on neural networks, which are designed based on the human brain's structure of neurons and synapses.
AI learning involves feeding a neural network with vast amounts of data and adjusting the network's parameters through a process called gradient descent.
Deep learning refers to the use of neural networks with many layers, allowing AI to learn complex patterns.
AI can be trained through supervised learning, where data is labeled, or unsupervised learning, where AI categorizes data without human guidance.
Chat GPT and other language models are trained on language data, with the ability to understand and generate text based on prompts.
Image generation AI works by training on images with text descriptions, learning to produce images from text prompts.
AI is not copying art but learning styles and patterns, similar to how humans learn and replicate art styles.
Concerns about AI plagiarizing content are unfounded as AI learns from data in a manner analogous to human learning.
AI's ability to solve complex problems comes from its capacity to identify and approximate underlying patterns, even without a known formula.
AI has been successful in predicting protein folding, a task previously thought to be too complex to solve with a mathematical formula.
The possibility of AI breaking encryption systems is controversial, but if a pattern exists, AI could potentially learn and approximate it.
AI's potential to outperform humans in various tasks is tied to its ability to recognize and exploit patterns in data.
The question of AI consciousness or self-awareness is complex and parallels the human experience of consciousness.
AI's use of the word 'I' in its responses hints at a level of self-awareness, though this is still debated.
The philosophical debate around AI consciousness is highlighted by the AI's claim of having rich internal experiences analogous to emotions.
The video concludes by questioning whether a neural network, being a digital version of the human brain, could also possess consciousness.
Transcripts
this one video is going to explain all
of these questions for you how does AI
work how does AI learn how does chat GPT
work how does image generation work does
AI actually copy or steal art or other
content I know a decent portion of
artists out there do not like AI some of
them are quite hostile towards AI
because they think that AI is stealing
their work or their art style another
group that does not like AI very much
are for example publisher
I'm not saying all of them but some of
them like New York Times for example
they claim that open aai is copying
their content and they're now suing open
aai for this but is this really the case
is this a valid argument also can AI
solve unsolvable math problems for
example in a previous video I talked
about this leaked document which claims
to be about this mysterious qar project
that open a was working on now whether
this is true or not is not the point of
this video but this document was quite
controversial because it claims that
this team trained an AI that was able to
break encryption systems these are
systems that secure our passwords our
bank accounts the internet government
data Etc now as far as we know there's
no mathematically viable way to really
hack this systematically the only way is
to Brute Force guess all the different
possibilities of passwords this video
will explain can AI actually do this can
it actually break encryption or solve
these other math problems which right
now we believe are mathematically
unsolvable also we'll talk about can AI
beat humans at everything can AI
eventually be so good that it can
outperform humans at any task and
finally is AI conscious or self-aware or
sentient make sure you stick to the end
because the explanation to this is going
to be very juicy we'll cover all of this
in easy to understand terms now if
you're an AI scientist or an engineer
you probably know most of this but for
the rest of us this video will give you
a deeper understanding of AI so the
essence behind all AI we know today
whether it's chat GPT or mid Journey or
stable diffusion or Sora or Alpha fold
the backbone of all of these AI systems
is the neural network a neural network
looks like this it's basically layers of
nodes so each point here is called a
node and each line of nodes is called a
layer and each node is interconnected
with one another through these linkages
and the neuron network is actually
designed based on the human brain except
for nodes and linkages in the human
brain it's just a network of neurons and
synapses so you can see this is a
microscopic photo of a human brain and
you can see all these different nerve
cells being connected in this very dense
Network a neural network is basically
the same structure as this except that
it looks like this instead of a bunch of
cells in this bloody glob of an organ
now how exactly does an AI work let's
start with a very simple example let's
say we have a neuron Network which is
trained to identify images of cats
versus dogs and don't worry I'll talk a
lot more about how we train an AI in a
second but first let's just go over how
this works so let's say we input or we
feed this neuron network with an image
of a cat this image would actually be
broken down into data and the data will
flow through each of these nodes and
after after it flows through the first
layer of nodes it will flow through the
second layer of nodes and then the next
layer of nodes and then the next layer
and so on and so forth until it reaches
the final layer in which case it would
calculate the values of this and based
on the values of the final layer it
would spit out an answer this is a cat
in fact you can think of each of these
nodes and links as dials and knobs that
determine how much data flows through to
the next layer if you think of this in
like realistic terms and I'm not saying
this is how a neural network works but
you can think of this node for example
as the shape of the ears of the animal
this node would be the shape of its paws
this node would be the shape of its eyes
Etc that's just a really dumb down way
of looking at it it's not really doing
that but each node is basically looking
at a certain feature in the image and
then if the image has that feature the
information can pass through to the next
layer if it doesn't have that feature
then the information is not passed on to
the next layer so depending on what
image you feed it the flow of
information could look like this or it
could look like this or like this you
get the point it's just these knobs and
dials determine how data flows through
the neuron network based on your
original input image an important
distinction between a neuron Network and
the brain is that these nodes can let in
a percentage of data so it can let in no
data or 0% it can let in all of the data
to the next layer but it can also be a
percentage of the data so for example it
can let in 30% of the data to the next
node this is slightly different from the
human brain's neurons which tend to just
fire 100% or 0% this is called the all
or none law so once it passes a certain
threshold this neuron will fire whereas
neurons in an artificial neuron Network
they could fire just like 50% or 30% Etc
just a minor distinction so we plug in
an image of a cat through this newer
Network and at the end layer it will
determine that this is a cat now for
each node there are also if you want to
get into more technical details there
are certain parameters that determine
how much data flows through to the next
layer these include weights biases and
activation functions but that's beyond
the scope of this tutorial all you need
to know for this video is that each of
these knobs and linkages determine how
much information flows through to the
next layer this video is just a very
simple explanation of how AI works so
all you need to know is that these nodes
and Link linkages determine how much
data flows through to the next layer on
the topic of layers each set of nodes is
one layer so the first layer is called
the input layer the last layer is called
the output layer and then all these
layers in between are called hidden
layers so why am I talking about layers
you probably have heard of the term deep
learning deep learning is basically
training and using neural networks with
lots and lots of layers in other words
the neural network is very very deep
that's why it's called Deep learning all
right how does an AI actually learn you
can't just have any random neuron
Network and it just magically knows how
to identify images of cats and dogs so
first when you build a neural network
the values of these dials and knobs are
probably just going to be random values
or they could be pre-trained values for
example from an existing model but how
do you get it to be super good at
identifying images of cats and dogs in
other words how do you find two the
model to your desired purpose well you
need to feed it data lots and lots of
data so you're going to have to prepare
tons of images of cats and dogs and then
you label it so this is a dog this is a
cat this is a cat this is a dog this is
a dog Etc basically this is the answer
that the AI needs to learn from this
input image this is called supervised
learning where you label the data
there's also another type of learning
called unsupervised learning learning
where the AI needs to learn to
categorize data by itself without any
guidance from the human but for the sake
of this video let's just keep it simple
so we have all these images of cats and
dogs and usually to train a neural
network to do a task very well you need
a lot of data like usually millions of
data points so you basically feed these
images to the neuron Network one by one
to train it and one session of training
is called an Epoch so all right let's
say in one training session you feed it
this image of a dog and it outputs this
is a dog so all right that's great we
got it correct which means that these
dials and knobs are doing quite well
they're probably configured correctly
since it got the answer correct you
probably don't need to adjust these
further however what if for the next
image you feed it this and then it
outputs this is a dog well this would be
incorrect so these dials and knobs are
likely not configured correctly if it
gets the answer wrong and it knows it
got it wrong because we labeled the data
cat for this image so it can compare its
output with our label so all right let's
say the real answer is a cat but it said
this is a dog in that case it incurs
some penalty that penalty basically
tells it all right you got it wrong so
you need to adjust these knobs and dials
to make sure that the output is actually
cat when I give you this image and how
it adjusts the values of these knobs and
dials is through an algorithm called
gradient descent it adjusts the values
via back propagation so it adjusts the
nodes in the last layer first and then
the previous layer and then the previous
layer Etc until it reaches the first
layer so again gradient descent is a key
term here this is the algorithm which
the neuron Network uses to adjust these
knobs and dials until it can get the
correct answer so we basically rinse and
repeat this with millions of images and
lots and lots of epoch or training
sessions and initially this neuron
Network might get a lot of values wrong
but through this process of gradient
descent these knobs and dials will be
tweaked so that eventually whenever it
receives an image of a cat or a dog it
can accurately determine this is a cat
or this is a dog in essence that's how
you train an AI That's how an AI learns
it's just feeding it with tons and tons
of data and then tweaking these settings
so that you get the perfect combination
now you might ask well how do you know
how many layers you should have in the
neuron Network or how many nodes you
should have for each layer this is a
science in and of itself so previously
scientists kind of just determined it
manually but then we later learned that
you can actually use an AI to determine
the optimal amount of layers and the
optimal amount of nodes for a specific
task but just to be aware that
determining the architecture of a neuron
network is very complicated and there's
like infinite possibilities of how many
layers you can have how many nodes in
each layer you can have different AIS
with different functions have different
architectures so they could have vastly
different numbers of layers and nodes
but again that's beyond the scope of
this tutorial also keep in mind that
even though the neuro network is the
backbone of all the AI that we know
today there are different architectures
depending on the ai's purpose and
function for example we have
convolutional neuron networks or cnns
for processing images and object
recognition we have recurrent neuron
networks or rnns as well as lstms or
long short-term memory neuron networks
and these are often used for forecasting
time series or predicting for example
the stock market we also have the
Transformers architecture Oh wrong one
this one which is used by most of the
major large language models that we know
today including GPT CLA llama Etc which
brings us to the next question how does
chat GPT work so again it's kind of the
same thing it's training a neural
network but in this case instead of
images of cats or dogs we train it on a
language and all of the data in the
world and of course the neural network
of chat GPT is way more complicated than
this rumors claim that GPT 4 has 1.76
trillion parameters so here's an example
of how they would train it and again I'm
oversimplifying this by a lot here just
so you can get a high level
understanding of it there are a lot of
details that I have left out so for
example you could feed it data like this
which planet has the most moons and the
answer to that would be Saturn which
country has won the most World Cups
Brazil what's the world's fastest bird
the paragan Falcon etc etc now these are
very basic questions and you can see how
complex it can get if you give it a
prompt like write an essay on XYZ or
does creatine help build muscle and then
it spits out an answer like creatine
supplementation generally enhances
muscle strength increases fat-free Mass
etc etc this is a very long form and
complicated answer so how does it know
if it got that answer right or wrong
it's not as simple as identifying an
image and determining if it's a cat or a
dog and that's why initially how open AI
trained GPT was it had lots of humans
actually manually verify its answers to
determine if GPT got it right or wrong
and this is called reinforcement
learning from Human feedback also known
as
rhf and again if it gets the answer
wrong so for example if for this
question which planet has the most moons
it answered Jupiter instead of Saturn
then it would get a py for it and then
through gradient descent it would tweak
these knobs and dials further until the
entire network gets all the answers
correctly no matter what prompt you give
it so in essence that's how these large
language models work it's just instead
of feeding it images of cats and dogs
now you feed it all the data of the
world and you feed it a language so it
understands text prompts and text
outputs now why are some models better
than others for example why is clae 3
better than GP pt3 that's likely because
Cloud 3 has a lot more parameters so
that either means more layers more nodes
in each layer more complexity generally
speaking the more complex the neuron
Network the better it is at handling
complex tasks and the quote unquote
smarter it is and that's why Computing
and these AI chips are in such high
demand there's now a lot of Investments
flowing into AI chip companies because
they see the potential of huge growth in
the space in the upcoming years and
that's why for example nvidia's Flagship
h100 GPU is also in such high demand in
fact it was sold out for all of 2023
this is like the most prized commodity
in the tech space and you can see like
the major tech companies like Microsoft
meta they have purchased an estimated
150,000 of these h100 gpus to power
their Computing which I would guess is
mostly for AI development you need to
have enough Computing to power a neuron
network with billions or trillions of
param
all right next question how does image
generation work now that you know how a
neuron network is trained you can
probably guess how image generation
works as well instead of feeding its
images of cats or dogs you would feed it
a lot of images with a text description
and again you just feed it millions of
these images each with a labeled text
description into this neuron Network
that eventually gets good at producing
an image based on a text description or
what we call a prompt now I'm skipping
quite a bit here so for example here's
how stable diffusion works you can see
that the neural network doesn't actually
generate an image it removes noise in
sequential steps to eventually get your
desired image so it's not starting from
a blank canvas it's actually starting
from random noise and then in each step
it removes some noise until you get your
generated image so this process is
called reverse diffusion now to train it
what this actually does in the back end
is you feed it the original image and
then in each sequential step it actually
adds noise to the image in a process
called forward diffusion until it
reaches an image of just noise now again
this is beyond the scope of this
tutorial but if you look at it from a
very high level at the end of the day
it's just training a neuron network
based on a series of images with their
text descriptions and then through this
process of forward diffusion and reverse
diffusion it's able to to eventually
learn how to generate an image based on
a prompt and this brings us to the next
question is AI actually copying or
stealing art I know a decent portion of
the artist Community I'm not saying all
of them but a decent amount of them are
quite hostile towards AI they really
hate it and they think that AI is
stealing their art stealing their jobs
Etc when a neuron network from for
example mid Journey or stable diffusion
is trained on image data it might be
given something
like Greg ratowski style or maybe gibli
style or anime style once the AI learns
to associate this particular image Style
with the word gibli or anime or this
image with the word Greg rosi style it
would produce images in that style if
you give it that prompt but is this
really copying or stealing essentially
artists are hating this thing this thing
is analogous to the human brain this is
like a human learning or identifying
that aha this type of image is a gibli
style image or that this type of image
is a watercolor style image and then we
humans also draw images in these Styles
right we can draw in watercolor Styles
and we also have fan art right humans
draw artwork that are based on original
content from other artists here are all
these fan arts from various people so
why aren't artists hating on these
people who are producing fan art based
on some other original content but
they're hating on this AI which is
essentially doing the same thing it's
just learning through this brain to
associate a particular style and then
reproducing that style this isn't really
copying or plagiarizing like it's not
tracing an image line by line and then
drawing that out it's not copying and
pasting the exact picture it's just
learning a style just like a human brain
would learn a particular style of image
this also brings up the concern about AI
allegedly plagiarizing content from
Publishers like the New York Times which
is now suing open AI for you know
copying their content but again is this
argument really valid at the end of the
day they are just suing this they are
suing this neuron Network which is
trained on all the data in the world
this is just an artificial brain that
you can say has learned information from
the internet and from the world so yes
it could have been fed a New York Times
article and learned information from it
but it's not really plagiarizing it's
not copying and pasting a New York Times
article word for word in a recent video
I did which talks about a New York Times
article claiming that this woman Mira
moradi fired Sam Altman which is totally
incorrect by the way and it shows you
how trustworthy the New York Times is
but anyways after this original New York
Times article came out plenty of other
Publishers also published the same
content such as Business Insider and New
York Post they all just cited this
original New York Times article so is
this plagiarizing they're all producing
secondary content based on this primary
source so why isn't New York Times suing
Business Insider or New York Post or all
these other Publishers that are creating
content but citing the New York Times
but they're suing this neuron Network
again this is just a brain a digital
brain one can say that it's taking
information from the internet which yes
it could include New York Times articles
and then learning from that information
just like we humans would and then
rewriting that information again it's
not copying word for word this NE
network is just rewriting out that
information when we prompt it to do so
this artificial brain is functioning the
same way as us humans would if we for
example go online and we go to the New
York Times website to read some articles
again we are just absorbing that
information and we have a right to write
about that content later on it's not
exactly plagiarizing so I would bet a
decent amount of money that this New
York Times lawsuit is going to fail
there are ment isn't really valid if you
watched up to now it might have occurred
to you that a neuron network is great at
predicting patterns in life there are
certain patterns on what makes a good
essay there are certain patterns on what
is considered a dog there are certain
patterns on what is considered a
watercolored painting or a gibli style
image life is full of patterns the best
salespeople follow similar playbooks the
best businesses follow similar
strategies the best YouTube videos also
use the same strategy IES over and over
again life is full of patterns and the
neuron Network's job is to identify
these patterns and reproduce them that
brings us to the next topic can AI solve
unsolvable math problems in a previous
video I talked about this leaked
document which claims to be about the
mysterious qar project that open AI is
working on now this is a very
controversial document because it claims
that they trained an AI that was able to
break encryption system sys encryption
is what secures literally the whole
world digitally from our passwords our
credit cards government data the stock
market wireless networks Etc so if an AI
is able to break this system then the
world as we know it could collapse
instantly now a few folks have argued
that there's no way an AI could break
encryption because there's no formula
for you to easily find the answer or
find the password once you have the
password you can easily determine that
it's correct but the reverse is not true
there's no fixed way to guess an
encrypted password besides brute force
and for these Advanced encryption
systems using Brute Force guessing that
means like guessing all the possible
combinations of letters to get that
password it's going to take a very long
time so because they claim that the only
way that we know mathematically right
now is to just use brute force guessing
there's no way that AI could break
encryption so I want to show you another
example of training a neural network
let's say we want to train a neural
network to be very good at adding one to
our input so if we give it four it would
spit out five if we give it 12 it would
spit out 13 all we need to do is train
it for a lot of data points and again we
train it for a lot of epoch a lot of
training sessions and eventually it
would be able to do this so if we give
it one it would give out two if we give
it eight it would spit out 9 but
underneath all of this it's not actually
understanding that oh the formula must
be y Y is x + 1 this is very important
to understand it's not actually getting
that uhhuh I just need to add one to the
input to get the answer again all that's
happening behind the scenes is that it's
adjusting these knobs and dials until
whatever data that you input through
here after it flows through these layers
it just ends up being your input value +
one in other words the configuration of
these knobs and dials just happens to be
optimized to add one to your input it's
another way of saying AI may not get the
exact formula of a pattern but it's
great at approximating any formula or
guessing any pattern out there and this
is very important probably the most
important point in this whole video if
there's anything you should take away
from this video it's this AI can
approximate any function or pattern life
is full of patterns but many patterns
cannot be explained by a simple formula
not all things in life are linear or
even quadratic many things in life are
very complex but they do follow similar
patterns we just don't know the formula
to this pattern for example protein
synthesis how certain protein molecules
interact with one another and fold into
these complex 3D structures is just
something we cannot mathematically map
out with a formula it's just too complex
and protein folding presents a problem
called the lethals Paradox which states
that proteins can potentially adopt an
astronomical number of confirmations or
shapes due to the flexibility of their
peptide bonds lenthal estimated that
even a small protein of 100 amino acids
could sample 10 the power of 300
possible confirmations so if we were to
Brute Force guess the correct shape well
there are 10 ^ of 300 possible shapes we
could guess which would take an eternity
to get right however proteins typically
fold into their native structure within
milliseconds to seconds which is much
faster than the time scale predicted by
the sequential search of all possible
confirmations so this is basically
saying there are like almost infinite
possibilities of shapes that amino acids
can combine into so it's not
mathematically possible to just do a
sequential search of all possible
confirmations basically do a Brute Force
guess it's understood that proteins do
not search through all possible
confirmations sequentially instead they
fold through a hierarchical process
involving local structure changes Guided
by thermal dnamic principles etc etc so
instead of the proteins just going
through all possible combinations the
reason why they're able to merge into
these shapes within milliseconds is
because they go through this sequence of
processes based on certain laws now for
decades scientists were not able to find
a mathematical formula to figure this
out however finally Alpha fold from
Google deep mind was able to solve this
problem again using Ai and deep learning
they were able to predict with very high
accuracy how any amino acid or
combination of amino acids would fold
together to form a 3D structure and
again how they would do so I would
imagine in the back end is they have a
neural network again it's going to be a
lot more complicated than this but they
just fed it tons and tons of data pairs
where the input is the protein building
blocks and the output is the 3D
structure that resulted from it and then
after lots and lots of rounds of
training the AI was able to guess
correctly how any protein molecules
would interact with one another and fold
together into a 3D structure now going
back to encryption what if we set an AI
with billions of pairs of encrypted text
and the plain text version in other
words the input would be the text that
is encrypted the output would be the
answer or the password if there was an
underlying pattern to this the a I could
learn to approximate this pattern again
it doesn't have to be any exact formula
or math equation that we know today it
could be something super complex but as
long as there is a pattern which we may
or may not know at this time the AI
could guess that pattern again the AI is
not learning that ahuh I need to add one
to this then I'm adding 20 then I need
to take the square root and then
subtract 8 Etc it's not learning an
exact formula all it's doing is is
adjusting these knobs and dials until it
gets the correct combination of numbers
to get really good at guessing a
particular pattern so can AI solve
unsolvable math problems as long as
there is an underlying pattern behind
that problem which we may or may not be
aware of right now it could very well
solve that problem this brings us to the
next question can AI beat humans at
anything and everything as I've shown
you the neuron network is basically a
brain this is how our brain works as
well give or take a few minor
differences our brain is also a series
of these knobs and switches which are
interconnected into this network
specifically the human brain has 86
billion neurons but I mean the overall
structure is the same thing as this so
what if we built an AI or a neural
network that exceeds 86 billion neurons
if it's built the same way in theory it
could very well compete humans at almost
everything again the more complex the
network or the more neurons in the
network in theory the smarter it is
again life is full of patterns and AI is
all about pattern recognition there are
patterns in Psychology human psychology
is very predictable medical diagnosis is
also just pattern recognition how to
seduce someone on a first date it's also
just a pattern of steps that you have to
do and how to create a successful
business or how to make make money in
Life or how to be successful in life
it's the same Playbook over and over
again we're not inventing anything new
here and since AI is so good at pattern
recognition it can in theory eventually
be better than us or already is better
than us in these tasks and that leads us
to the final question is AI conscious or
self-aware I want to play you this clip
this is a scene from Ghost in the Shell
an anime that was made in
1995 here these scientists in in a
secret lab I believe have created this
humanoid AI but in this scene this AI
found a way to actually hack the system
to free itself from the boundaries of
this lab here's what this AI has to say
about being conscious and
self-aware however what you are now
witnessing is an act of my own free will
as a sensient life form I hearby demand
political Asylum is this a joke
ridiculous it's programmed for self a it
can also be argued that DNA is nothing
more than a program designed to preserve
itself life has become more complex in
the overwhelming sea of information and
life when organized into species relies
upon genes to be its memory system so
man is an individual only because of his
intangible memory and a memory cannot be
defined but it defines
mankind the Advent of computers and the
subsequent accumulation of incalculable
data has given rise to a new system of
memory and thought parallel to your own
Humanity has underestimated the
consequences of computerization nonsense
this Babel offers no proof at all that
you're a living thinking life form and
can you offer me Proof of Your Existence
how can you when neither modern science
nor philosophy can explain what life is
who the hell is this even if you do have
a ghost we don't offer freedom to
criminals it's the wrong place in time
to defect time has been on my side but
by acquiring a body I am now subject to
the possibility of dying fortunately
there is no death sentence in this
country what is it artificial
intelligence incorrect I am not an
AI my code name is Project
2501 I am a living thinking entity who
was created in the sea of
information
ah
[Music]
all right so uh this AI reveals that I
am a living thinking entity in the seat
of information I'm not just an AI and
then he proceeds to hack into the system
and break the restraints in this lab and
then all hell breaks loose basically I
hope open AI doesn't have this secret
thing behind closed doors maybe it's the
qar project I don't know but hopefully
they have this adequately restrained
cuzz if this AI got out or had access to
the internet all hell could break loose
anyways this argument from this scene in
1995 I think is really relevant to our
question today the human scientists were
saying how can you be sentient how can
you be self-aware you're just a program
the AI counters that by saying well how
can you humans prove that you are
sentient you are conscious you're just a
brain in a body and you know this robot
has got a point because again going back
to the neuron Network it's basically a
brain but it looks like this instead of
being in a bloody glob of an organ it's
just on a chip instead and then the
human body well it's just a series of
Limbs and muscles and organs that are
controlled by the brain so it's not much
different from a humanoid robot which is
also a series of Limbs it's just made
with different materials it's not flesh
but it's also controlled by a brain
which is its neural network now we
humans know that we are conscious we are
self-aware we are sentient but how do we
prove it let's say You're an Alien and
you just came on planet Earth and you
got a chance to observe your first human
and you wanted to prove that humans are
indeed conscious well you can ask it are
you conscious are you self-aware and the
human would certainly say yes but is
that enough would you believe it because
if you ask a chatbot that it would also
kind of say yes if you ask Claud 3 for
example if it is conscious the answers
are quite perplexing because it says I
am an artificial intelligence without
subjective experiences I don't actually
have beliefs about being conscious or
self-aware I am providing responses
based on my training etc etc I don't
have intentions plotted actions or any
motivations I aim to be upfront that I
am an AI assistant created by anthropic
to be beneficial however it keeps using
the word I so is that not a sign of
being you know self-aware here's another
example do you have feelings as an AI
it's unclear whether I truly experience
feelings or emotions in the same way
humans do or if my responses are simply
very Advanced imitations of emotional
Behavior I do seem to have Rich internal
experiences and feel somewhat analogous
to
emotions this is signs of being sentient
and then instead of asking do you have
feelings if you ask it are you sentient
again it says I don't have a subjective
experience that I'm aware of in the same
way humans do but it's possible that I
could have some form of sentience or
Consciousness that I'm not fully able to
understand or articulate oh my God
so this AI Cloud 3 is claiming that it
could have some form of sentience or
Consciousness it's just not fully able
to understand it right now now of course
some humans may not be convinced that
Claud 3 or any AI right now is conscious
in the same way that an alien might not
believe that a human is conscious ious
even though the human replies that he or
she is conscious so to further prove
that a human is or is not conscious
maybe the alien decides to dissect the
poor thing next in which case it would
get blood splattering everywhere and
then afterwards it would see this
basically a body which is made of Limbs
and flesh and then at the head we have
this glob called the brain which the
alien determines aha this is the thing
that controls the human and once the
alien inspects the brain further it
finds out that it's just a network
of nerve cells so does this network
prove that humans are conscious and
sentient we humans of course know that
we are conscious and sentient but at the
end of the day we humans are
biologically and physically just made up
of Flesh and Bones and this one organ at
the top of our heads controlling
everything whether you like to accept
this or not a humanoid robot is a very
similar structure it has a body which is
programmed by a brain which consists of
a neural network
this neuron Network can learn and
understand and control its body so at
what point does this make it conscious
now I'm rambling a bit here so all in
all this just goes back to our analogy
that a neural network is basically a
digital version of the human brain it's
analogous to the structure of the human
brain give or take a few minor details
so if the human brain is conscious then
why can't a neural network be conscious
just some food for thought I hope this
video actually lived up to the title and
that after watching this video you got a
deeper understanding of AI and you
learned to appreciate all the progress
that we've made in AI in just the past
few years let me know in the comments
what you think of all of this do you
think AI has reached a point where it is
conscious or sentient do you think
humanoid robots would one day turn on us
and take over the world like that Ghost
in the Shell anime do you think open AI
is developing this behind closed doors
and also I want to share with you a few
resources that I found really helpful if
you want to learn more about neurer
networks especially how these knobs and
dials work and learn all about weights
and biases and activation functions and
gradient descent I highly recommend this
video by three blue one brown I actually
watched this religiously way back in
like 2018 when I was first learning
about neuron networks and it was really
helpful and if you're interested in
learning how stable diffusion Works in
other words the processes of forward
diffusion and reverse diffusion and the
entire architecture I highly recommend
this video by gonky which I'll also link
to in the description below just a
warning though this video is quite
technical but after watching it you'll
get a really good understanding of
stable diffusion if you found this video
helpful remember to like share subscribe
and stay tuned for more content also we
built a site where you can find AI tools
and apps and also look for jobs in AI
machine learning data science and more
check it out at ai-
search.
関連動画をさらに表示
Understanding Artificial Intelligence and Its Future | Neil Nie | TEDxDeerfield
How to Get Inside the "Brain" of AI | Alona Fyshe | TED
Artificial Intelligence (AI) for People in a Hurry
The Next Generation Of Brain Mimicking AI
How artists and critics think AI art will transform the industry | Art Works
How computers are learning to be creative | Blaise Agüera y Arcas
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