You Don't Understand AI Until You Watch THIS

Summary

This 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

00:00

🧠 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.

05:01

🤖 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.

10:02

🎨 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.

15:04

🔒 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.

20:05

🧬 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.

25:06

🏆 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.

30:08

🧐 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.

Keywords

💡AI (Artificial Intelligence)

AI refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. In the video's context, AI is explored in various forms, such as chatbots and image generation tools, which learn and operate based on neural networks. The script discusses AI's capabilities, its learning process through data input and gradient descent, and its potential controversies, such as accusations of content theft.

💡Neural Network

A neural network is a core computational structure inspired by the human brain that consists of interconnected nodes or neurons, arranged in layers. It is fundamental to AI, allowing it to process and learn from data. The script explains how neural networks are trained to perform tasks like image recognition or language understanding, using the example of identifying cats and dogs, and how they adjust 'knobs and dials' or parameters through backpropagation.

💡Deep Learning

Deep learning is a subset of machine learning that involves training neural networks with many layers, hence the term 'deep.' It is used for complex tasks that require the recognition and learning of intricate patterns. The script mentions deep learning in relation to the layers within a neural network and how having more layers allows the AI to handle more complex tasks, such as language models like GPT.

💡Supervised Learning

Supervised learning is a type of machine learning where the algorithm learns from labeled data. In the video script, supervised learning is used to train a neural network to identify images of cats and dogs by feeding it a large number of labeled images. The AI learns to associate the correct labels with the images through this process.

💡Gradient Descent

Gradient descent is an optimization algorithm used to minimize a function by iteratively moving in the direction of steepest descent, as defined by the negative of the gradient. In the context of the video, gradient descent is key to training neural networks, as it allows the AI to adjust the 'knobs and dials' or weights and biases to improve its accuracy in tasks like image recognition.

💡Image Generation

Image generation refers to the process by which AI creates new images based on learned patterns or given prompts. The script discusses how AI can be trained on a dataset of images and their descriptions to eventually generate new images in various styles or that match specific descriptions, such as 'Ghibli style' or 'anime style'.

💡Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM)

RNNs and LSTMs are types of neural networks that are particularly effective for processing sequences of data, such as time series predictions or natural language. They are designed to remember and utilize information from previous steps, making them suitable for tasks that involve context. The script mentions these architectures in relation to forecasting and predicting tasks like stock market trends.

💡Transformers Architecture

The Transformers architecture is a type of neural network that has been pivotal in the field of natural language processing. It is known for its ability to handle long-range dependencies and has been used in major language models like GPT. The script refers to Transformers when discussing how large language models like chatbots work, emphasizing their complexity and parameter count.

💡Sentience

Sentience refers to the capacity for subjective experience, which includes consciousness, self-awareness, and the ability to feel and perceive. The script raises the philosophical question of whether AI can achieve sentience, drawing parallels between the structure of a neural network and the human brain, and questioning if the AI's complexity could lead to a form of consciousness.

💡Encryption

Encryption is the process of encoding messages or information to ensure secure communication, making it unreadable to anyone except the intended recipient. The script discusses the controversial claim that AI could potentially break encryption systems, which are complex patterns that secure passwords, bank accounts, and government data.

💡Pattern Recognition

Pattern recognition is the ability of a system to identify and classify patterns in data. AI excels at this, which allows it to perform tasks such as language translation, image recognition, and even potentially solving complex problems like protein folding or breaking encryption. The script emphasizes that life is full of patterns and AI's success in various tasks is due to its proficiency in recognizing and reproducing these patterns.

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

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