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So, I asked it yesterday to generate a Ruby program that, in turn, generates a rap song about Yukihiro Matsumoto.
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I wouldn't know much about the mic of the rap—maybe that's not the best—but the rhymes? I mean, rhyming 'needle' with 'Matsumoto'? That's good stuff! You will also notice that I always say 'please' when I ask GPT for something because the idea is that when it eventually takes over, I hope it remembers and it will kill me last.
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So, let's try to wrap our minds around GPT at a high level and how it works. I will do that in five chapters, and I will start from scratch, so anyone with experience with neural networks, please bear with me. I want everyone to be on board.
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A short disclaimer: we don’t actually know how GPT-4 works because OpenAI won’t tell us. But we can assume it works kind of like GPT-3, which works like most large language models. We have no hints that OpenAI has any secret recipe; their models are just big, they're good at executing, but otherwise, they’re just neural networks.
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Chapter one: instead of showing you the usual diagrams with bubbles on how neural networks work, let's start with a concrete example—a toy example. Let's assume we have a small music label, and we want to predict how popular our songs will be based on several factors, such as BPM (beats per minute) and the number of streams over the last couple of weeks.
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We plot this history on a chart, with every dot representing a track we released. So far, it looks like our audience is into chill-out music around 100 BPM. Obviously, there's another area for very unsuccessful songs, but it picks up again around 185 BPM. This gives us a rough idea, but it's not great at predicting the future.
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It gets better if we take these points and approximate them with a continuous function. Once we have this function, we can make better predictions. For example, if our latest track is 175 BPM, the function will tell us how successful it might be. But of course, we can't predict success based on BPM alone, so we need more data, such as the week of the year that the song was released.
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We add important variables to our model, and now we have a three-dimensional function instead of a two-dimensional one. If we add more variables, the function becomes more complex, but that's fine because neural networks can handle it. In general, neural networks approximate functions. It's simply a function in a box. Once we have that function, we can input different variables and receive an output.
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Now, how do neural networks approximate functions? For example, let's consider the classic problem of recognizing pictures of cats. What are our inputs? Well, we can take images, breaking them down to pixels, with each pixel representing an input variable. Or we might break down the pixels into their RGB values—three input variables per pixel. Ultimately, we want a Boolean result indicating whether it’s a cat or not.
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What people usually ask for is a number from 0 to 1, representing how sure the network is about the truth of the answer. For instance, it might return 0.9 for 'probably a cat' and a low number for 'definitely not a cat.' The idea is that there's a function that exists, mapping the pixels to some surface in a high-dimensional space. While we can't visualize it, if we could, we'd see this 'cat surface.'
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Inside the neural network, there are layers, and these layers are essentially matrices. These matrices can be two-dimensional or three-dimensional, depending on the situation. They contain numbers that undergo operations—typically matrix multiplication—which are essentially combinations of multiplications and sums, alongside other simple operations, like clipping to prevent negative values.
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Having enough operations and numbers allows us to approximate any function. The specific function approximated depends on the parameters—numbers inside the matrices. The trick, then, is finding the parameters that approximate the desired function, a stage called training the neural network.
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Training essentially starts with random numbers, giving a random function. Next, we express the error of this function numerically compared to what we aim to achieve. For example, using song data, we can calculate how off the predictions are; if the error is significant, an algorithm will adjust the numbers slightly to decrease the error.
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This process continues iteratively—hundreds of thousands or millions of times—until acceptable error levels are reached. That results in a function in a box, which is crucial in artificial intelligence, known as backpropagation.
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Backpropagation starts from the end of the network, working backward to tweak numbers in each layer until the desired output is achieved. To summarize, neural networks define their own architecture through operations that are suited to the data they handle. For instance, for image data, convolutions are utilized to maintain geometric structure, unlike methods that might distort this critical information.
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Another interesting point about neural networks is that deeper networks—those with more layers—tend to perform better. Each layer tends to recognize progressively more complex shapes. Initially, they may pick out simple lines and edges, but as layers deepen, more complex structures (like objects) are identified. In essence, these networks serve as abstraction machines, creating layers of meaning, which was a significant breakthrough. Just a few years ago, we needed to guide networks by defining parameters, but deep networks can identify their own features without external labor.
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Let's transition to the topic of language—the technique that specifically handles linguistic tasks. Consider word representation where we create a chart to categorize words based on their attributes, like cuteness and speed.
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As we organize words in this multi-dimensional space—like a cute but slow sloth versus a fast but less cute airplane—we can establish coordinates that conceptualize words as numerical arrays known as word embeddings. This idea is powerful because it helps us perceive how meaning is defined in relation to multiple attributes.
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When generating text—such as utilizing Charles Darwin's 'On the Origin of Species'—we can train the neural network with sequences of words, masked at certain junctures, to let the network predict missing words. This provides abundant training material without needing specific examples, as we can slide our window to garner numerous instances of word pairings.
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However, predicting the next word can be intricate, as it’s hard for the model to ascertain the possibilities. Even with extensive vocabulary, it may struggle to suggest the right word accurately. An important part of this process involves an embedding matrix that represents the vocabulary, where each row symbolizes a word's embedding in a high-dimensional space.
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As we train this model, we continually adjust the embeddings while refining the neural network’s predictions. Over time, we expect similar context words to share similar embeddings. Such a training structure allows us to leverage the language model's capacities without explicitly defining word meanings.
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Eventually, we observe the emergence of patterns within the matrix as certain semantics thread through the word embeddings, anchoring relationships like those between 'man,' 'woman,' 'boy,' and 'girl' visually. This illustrates how geometry plays into representational learning, revealing correlations without direct instruction.
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Now that we’ve discussed individual words, we must emphasize that the true challenge comes with sentences. Understanding sentences mandates discerning their hierarchical structure and implicit meanings, resulting in nuanced interpretations. To address this, neural networks develop a framework to ascertain the interrelations between words in context.
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They utilize structures like queries, keys, and values in what's called ‘attention mechanisms’ to sift through sentence elements. These attention heads help identify the significance of words conditional on underlying queries, which may encompass numerous layers of syntactical and contextual analyses.
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As immersed sentences are processed through multiple attention heads, it becomes possible to distill down ideas and relationships, allowing models to capture intricate connections present in natural language.
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From there, we structure the architecture of GPT, utilizing these operations on word embeddings and passing them through attention mechanisms layered atop conventional neural network architectures. This culminates into a system capable of generating text by predicting the next word given a context of greater length, refining its processes as nuanced feedback reveals potential errors.
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Ultimately, we achieve a machine that excels in generating text by understanding the context and predictively offering words, forming coherent sentences as they evolve through the interaction. The cycle continues, generating the new context invariably enhancing the model’s output.
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Moreover, it’s essential to address how this approach aligns language models with user expectations. Alignment revolves around satisfying interactions, enabling the AI to maintain a cordial and appealing conversation rather than simply continuing a thought or providing fragmented responses.
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Humans often leverage reinforcement learning from human feedback to funnel the model's tendencies towards desired outputs, calibrating its behavior so that it becomes beneficial and safer. This involves having people curate training data and reviewing responses to influence model training positively. Importantly, a few training exchanges can reorient behavior effectively.
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After pretraining, the model is adjusted with minimal human effort to create versions, nailing the language model's robustness. These transformations enhance its flexibility, allowing it to engage in a multitude of contexts including logical operations, significantly broadening the horizon of practical applications of the model.
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Consequently, as these systems advance, the capacity for emergent abilities becomes noteworthy. They begin performing tasks that haven’t been explicitly trained yet simply due to underlying scaling, prompting important inquiries regarding our relationship with these models and the types of capabilities we should anticipate in the future.
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Ultimately, while we find ourselves in a fascinating age of AI, the philosophical debates about the nature of consciousness, comparisons of our cognition to AI behavior, and the exploration of our relationship with the technology become imminent even as we strive for understanding.
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This brings us to a close as we’ve extensively examined the mechanics behind GPT and the wider context of artificial intelligence innovation. Thank you for your attention, and I hope you gained valuable insights into how AI, particularly GPT, operates.
00:31:46.079
Thank you, this was great! And as you said yesterday, we still don’t know what this thing is doing. Let's find out, for better or for worse.