This one video is going to explain all of these questions for you. How does AI work? How does AI learn?
How does ChatGPT 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, publishers. I'm not saying all of them, but some of them, like New York Times, for example, they claim that OpenAI is copying their content and they're now suing OpenAI 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 QSTAR project that OpenAI 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 ChatGPT, or MidJourney, or Stable Diffusion, or Sora, or AlphaFold, 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 neural 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 neural 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 neural 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 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 his eyes, etc.
That's just a really dumbed 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 neural network based on your original input image. An important distinction between a neural network and the brain is that these nodes can let in a percentage of data. So it can let in no data or zero percent, 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. 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 neural 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 neural 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 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 neural 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 fine-tune 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, 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 neural 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 backpropagation.
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 neural 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 epochs or training sessions. And initially, this neural 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 You might ask, well, how do you know how many layers you should have in the neural 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 be aware that determining the architecture of a neural 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 neural 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 neural networks, or CNNs, for processing images and object recognition. We have recurrent neural networks, or RNNs, as well as LSTMs or long short-term memory neural networks, and these are often used for forecasting time series or predicting, for example, the stock market. We also have the transformer's architecture. Oh, wrong one.
This one, which is used by most of the major large language models that we know today, including GPT, Claude, Lama, 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 ChatGPT 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 peregrine 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 OpenAI 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 RLHF. 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 penalty 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 Cloud 3 better than GPT-3? 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 neural 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 neural network with billions or trillions of parameters. All right, next question. How does image generation work?
Now that you know how a neural network is trained, you can probably guess how image generation works as well. Instead of feeding it 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 neural 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 neural 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 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 neural 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 Ghibli style or anime style. Once the AI learns to associate this particular image style with the word Ghibli or anime, or this image with the word Greg Ratowski 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 Ghibli-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 OpenAI 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 neural 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, Meera Muradi, 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 neural 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 neural 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.
Their argument isn't really valid. If you watched up to now, it might have occurred to you that a neural 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 watercoloured painting or a Ghibli-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 strategies over and over again.
Life is full of patterns, and the neural 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 QSTAR project that OpenAI is working on.
Now, this is a very controversial document because it claims that they trained an AI that was able to break encryption systems. 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 1 to our input. So if we give it 4, it would spit out 5. 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 epochs, a lot of training sessions, and eventually, it would be able to do this. So if we give it 1, it would give out 2. If we give it 8, it would spit out 9. But underneath all of this, it's not actually understanding that, oh, the formula must be y is x.
plus one. This is very important to understand. It's not actually getting that, aha, 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 plus 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 Leventhal's paradox, which states that proteins can potentially adopt an astronomical number of conformations or shapes due to the flexibility of their peptide bonds. Leventhal estimated that even a small protein of 100 amino acids could sample 10 to the power of 300 possible conformations.
So if we were to brute force and guess the correct shape, well there are 10 to the power 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 timescale predicted by the sequential search of all possible conformations. 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 conformations. Basically, do a brute force guess.
It's understood that proteins do not search. through all possible conformations sequentially. Instead, they fold through a hierarchical process involving local structure changes guided by thermodynamic 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, AlphaFold from Google DeepMind 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 AI 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, aha, I need to add 1 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 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 neural 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 outcompete 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 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 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 sentient lifeform, I hereby demand political asylum. Is this a joke?
Ridiculous! It's programmed for self-preservation! 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. The 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 babble offers no proof at all that you're a living, thinking lifeform.
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 and 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 codename is...
This project 2501. I am a living, thinking entity who was created in the sea of information. Ah. All right, so this AI reveals that I am a living, thinking entity in the sea 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 OpenAI doesn't have this secret thing behind closed doors. Maybe it's the QSTAR project? I don't know, but hopefully they have this adequately. restrained.
Because 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 neural 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 Clod3, 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. Claude 3 or any AI right now is conscious, in the same way that an alien might not believe that a human is conscious, 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 neural 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 OpenAI 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 neural 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 3Blue1Brown. I actually watched this religiously way back in like 2018 when I was first learning about neural 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.
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