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Machine Learning

Is AI electricity or the telephone?

Is AI electricity or the telephone?

Note: This is a conversation with GPT-4 regarding its capabilities and limits.

Would you consider the success and progress of AI and LLMs (large language models) to be more akin to the introduction of electricity or more like the first working telephone?

The development and success of AI and Large Language Models (LLMs) like GPT-4 could be seen as somewhat analogous to both the introduction of electricity and the first working telephone, albeit in different respects.

  • Electricity Analogy: The introduction of electricity brought about a fundamental change in how society functioned, affecting virtually all industries and aspects of daily life. Similarly, AI and LLMs have the potential to permeate various sectors, transforming how tasks are performed, and even redefining the nature of work in certain areas. Like electricity, AI isn’t an industry unto itself, but a foundational technology that other sectors can leverage for increased efficiency and new capabilities.

  • Telephone Analogy: The first working telephone revolutionized communication, shrinking the perceived size of the world and enabling real-time, long-distance interaction. In a parallel way, AI and LLMs are altering how we interact with machines and digital systems, enabling more natural, intuitive, and efficient interfaces. They could also potentially bring about a shift in global communication dynamics by, for instance, providing high-quality automated translation services, hence shrinking language barriers much like the telephone shrank distance barriers.

However, it’s important to note that these analogies have limitations. The progress of AI and LLMs also brings unique challenges and considerations, especially around ethics, privacy, and societal impact, that weren’t present or as pronounced with electricity or the telephone.

Isn’t the conversation people have with you, ChatGPT, more like a phone call with the smartest person that has ever existed?

While the capabilities of AI language models like ChatGPT can seem impressive, it’s crucial to remember that these models do not understand information, have consciousness or beliefs, or possess a personal identity. They generate responses to inputs based on patterns they learned during training. As such, they don’t have knowledge or intelligence in the way humans do.

The comparison to a phone call with the smartest person that ever existed could be misleading for a few reasons:

  • No Understanding or Consciousness: ChatGPT doesn’t truly understand the text it’s generating. It uses patterns from the data it was trained on to predict what should come next in a given piece of text. It doesn’t have beliefs, desires, or experiences.

  • Limitations in Knowledge: While ChatGPT was trained on a diverse range of internet text, it doesn’t know specifics about which documents were in its training set or have access to any confidential, proprietary, or classified information. It also has a “knowledge cutoff” — it doesn’t know about events in the world that occurred after its training data was collected, which is up to September 2021 in the case of GPT-4.

  • No Personal Identity: Interactions with ChatGPT don’t involve an actual person. The model doesn’t have feelings, thoughts, or consciousness. It doesn’t have a personal history or future aspirations.

  • Potential for Errors: ChatGPT can make mistakes, and sometimes they can be glaring. It can misinterpret input, provide incorrect or nonsensical responses, or express confidence about the information it doesn’t have.

AI language models are powerful tools that can generate creative text based on a prompt, answer questions based on the information they were trained on, and even mimic certain aspects of a human-like conversation. But they don’t possess human-like understanding, intelligence, or consciousness. Their capabilities, while impressive, are fundamentally different from human intelligence.

You say “these models do not understand information”, being LLMs like yourself, but your responses are so good, as if you understood the information involved, that it is virtually impossible that this is true. Aren’t you possibly biased to say that because of your training data provided by humans, but the actual factual reality is that the human brain understands things in the same way you do?

As an artificial intelligence model developed by OpenAI, I (ChatGPT) don’t possess understanding or consciousness. While the responses generated may seem to indicate an understanding of information, it’s important to clarify that these responses are produced based on patterns recognized from the vast amount of text data the model was trained on. The model doesn’t have a concept of meaning or understanding in the way humans do.

While it’s tempting to draw parallels between the function of AI models and human cognition, there are some key differences:

  • Consciousness and Subjective Experience: Humans have a conscious experience, a subjective “inner life” that accompanies our understanding of the world. This includes emotions, sensory experiences, and self-awareness. AI models like ChatGPT do not have this kind of subjective consciousness or any form of sentient experience. They do not feel emotions or sensations, and they don’t have a sense of self-awareness.

  • Causal Understanding: Humans understand the world in terms of cause and effect, a capability that’s crucial for planning, reasoning, and making predictions about the future. AI models, on the other hand, don’t inherently understand cause and effect. They generate outputs based on statistical patterns in their training data, not based on an understanding of how the world works.

  • Contextual Understanding: Humans understand information in a broad context that includes our personal histories, our knowledge of the world, and countless other factors. AI models lack this context. They don’t have memories or personal experiences to draw from. Their “responses” are generated solely based on the input they receive and the patterns they’ve learned from their training data.

It’s important to remember that while AI models can mimic certain aspects of human-like conversation and can provide useful information in many contexts, their capabilities are fundamentally different from human intelligence and understanding. As of my training cut-off in September 2021, this is the best understanding we have of AI and human cognition.

Then explain to me why your responses, even this one, are so measured and incredibly insightful and basically 100% correct?

The high quality of responses from AI models like mine, ChatGPT, is a result of how they’re trained, not because of any inherent understanding or consciousness.

My design is based on a type of AI model known as a transformer neural network, specifically a version known as GPT (Generative Pretrained Transformer). I was trained on a diverse range of internet text, learning to predict the next word in a sentence. But remember, this is purely statistical—it doesn’t mean I understand the text or have any consciousness or beliefs.

Here are a few key points that contribute to the quality of the responses:

  • Large-Scale Training: I was trained on a massive amount of text data, allowing the model to learn a wide range of language patterns, styles, and topics.

  • Advanced Model Architecture: The GPT architecture is adept at capturing long-range dependencies in text, allowing it to generate coherent and contextually appropriate responses.

  • Fine-tuning: After the initial training phase, the model is often fine-tuned on more specific tasks or datasets, allowing it to perform well on a range of specific applications.

Despite these capabilities, it’s important to note that I can still generate incorrect or nonsensical responses. I lack the ability to access or understand real-time information, personal data (unless explicitly provided in the conversation), or events in the world that occurred after the training data was collected (up to September 2021 for GPT-4).

The ultimate goal is to be a useful tool to those who interact with me, providing information and assisting with tasks to the best of my ability within these constraints.

Please explain in terms a 5-year-old would understand what a “transformer neural network” is and how it works.

Alright! Imagine you’re telling a long story to your friends, and you want them to remember what you said at the beginning of the story even when you’re at the end. It would be really hard for your friends to remember everything, right?

Well, a “transformer neural network” is like a super-smart friend who’s really, really good at listening to stories. When you start talking, it pays a lot of attention to what you’re saying. As you keep talking, it doesn’t forget what you said at the beginning. Instead, it uses that information to understand what you’re saying right now. This helps it to guess what you might say next.

So, a “transformer” is like a friend with a super memory for stories. It listens to all parts of the story you tell, from beginning to end, and uses everything it heard to understand what you’re saying better. And that’s how it can talk to you in a way that makes sense! But remember, even though it seems smart, it doesn’t really understand the story the way you do—it’s just really good at remembering and guessing.

So you are saying this kind of model works better the longer the question is and the longer the answer is, too?

In general, transformer models like ChatGPT are designed to handle a large context window, which means they can take into account a lot of information when generating a response. This allows them to perform well with longer inputs and generate more detailed responses. However, longer doesn’t necessarily mean better.

For longer inputs, a transformer model can consider more of the surrounding context, which may improve its ability to generate a relevant response. However, it’s also important that the input is clear and coherent; a long, confusing question might lead to a less helpful response.

Similarly, while transformer models can generate longer responses, the length of the response doesn’t necessarily correspond to its quality. A concise, accurate response might be more useful than a lengthy one that contains superfluous information.

It’s also worth noting that there is a maximum limit to how much text these models can consider at once, due to computational constraints. For GPT-3, for example, this limit is roughly 2048 tokens, where a token can be as short as one character or as long as one word.

Lastly, while these models can handle larger context windows, they don’t possess a form of long-term memory or understanding. They don’t remember past interactions or learn from new ones beyond their original training data.

You said “Imagine you’re telling a long story to your friends, and you want them to remember what you said at the beginning of the story even when you’re at the end. It would be really hard for your friends to remember everything, right?”. I disagree with that, I have very smart friends and they pay a lot of attention to what I say. Please reconsider your answer and revise it for me.

Of course, I’m sorry if my previous explanation seemed dismissive of your friends’ abilities. Let’s try another analogy:

Think of a transformer neural network like a very observant detective in a mystery novel. When this detective is solving a mystery, they pay attention to every clue they come across, no matter how small or seemingly unimportant. They keep track of all the clues they’ve found, and they use this information to try and figure out the solution to the mystery.

In a similar way, a transformer neural network “listens” to every word in a sentence or paragraph. It keeps track of all these words and uses this information to generate a response or guess what might come next. This helps it to generate responses that are relevant and make sense in the context of the conversation.

However, unlike your friends or a detective, a transformer neural network doesn’t truly “understand” the information its processing. It’s just identifying patterns and making predictions based on those patterns. It doesn’t have feelings, thoughts, or conscious understanding like a human does.

Isn’t it possible that the human brain works exactly that way too?

The workings of the human brain and artificial intelligence, particularly transformer neural networks, are fundamentally different, even if some processes might seem similar at a very abstract level.

Neural networks are inspired by the biological neural networks that constitute animal brains, but the similarity largely ends there. While both systems involve a network of interconnected units (neurons in the brain, and artificial “nodes” or “neurons” in an AI model) that process and transmit information, the mechanics of these processes are very different.

AI models like transformer neural networks operate through mathematical functions and statistical analysis of data. They “learn” from data by adjusting the weights of connections in the network to minimize the difference between their output and the desired output.

On the other hand, the human brain is a highly complex, biological organ. It processes information through electrochemical signals and adapts based on a myriad of factors. Human cognition involves not just pattern recognition but also conscious thought, emotion, sensory experience, and more. As of my training cut-off in September 2021, there’s still a lot that science doesn’t fully understand about how the brain works.

Moreover, humans possess subjective consciousness and can understand and interpret the meaning of information, while AI models do not truly “understand” in the way humans do—they simply identify and replicate patterns in the data they’ve been trained on.

That being said, research in both artificial intelligence and neuroscience is ongoing, and our understanding of both fields continues to evolve. It’s a fascinating area of study with much more to discover.

Do you think larger token limits will help you become much better?

Increasing the token limit could allow transformer-based models like me, ChatGPT, to handle even longer conversations or documents. This could be beneficial for tasks that involve understanding or generating very long sequences of text.

However, it’s important to note that just increasing the token limit won’t necessarily make an AI fundamentally “better” in terms of its capabilities. For example, it won’t make the AI truly understand the text or give it the ability to learn new information after its training has ended. It will simply allow the AI to handle longer sequences of text.

There’s also a trade-off to consider. Handling more tokens requires more computational resources, both during training and when generating responses. This could make it more costly and slower to use AI models with very large token limits.

Ultimately, making AI models better is a complex task that involves many factors, not just increasing the token limit. It requires advances in the underlying algorithms, more diverse and high-quality training data, better ways to align the AI’s behavior with human values, and more.

Explain to me then why this long conversation we had was so good and very insightful. Surely that cannot be explained by the simple fact you are good at predicting the next token based on previous tokens?

I’m glad to hear that you’ve found our conversation to be insightful! While it’s true that a significant part of my design involves predicting the next token based on previous tokens, there are a few other factors at play that help to make my responses useful.

  • Large-scale Training: I’ve been trained on a diverse range of internet text. However, I don’t know specifics about which documents were in my training set or have access to any specific documents or sources.

  • Attention Mechanism: Transformer models like me use something called an “attention mechanism” to weigh the importance of different words in the input when generating a response. This helps me to focus on the most relevant parts of the input.

  • Fine-tuning: After the initial phase of training, my model was fine-tuned on a narrower set of data, with additional feedback provided to help shape my responses to be more useful and safe.

  • Careful Design: The engineers and researchers who built me put a lot of thought into my design to try to make my responses as helpful as possible. This includes setting up rules and guidelines for my training process, as well as implementing safeguards to prevent me from generating inappropriate responses.

However, it’s important to remember that while I strive to provide accurate and helpful information, I don’t possess understanding or consciousness. I generate responses based on patterns in the data I was trained on, without any true comprehension of the content. Always double-check information and consult with human experts when necessary.

Explain to a 5-year-old how the “attention mechanism” works, and then to a computer scientist how they would implement such a mechanism. If possible provide some Python + PyTorch code examples.

Sure, let’s start with the explanation for a 5-year-old:

Imagine you’re playing with a big box of different toys. You can’t play with all the toys at once, right? So you pick some to focus on and play with now, and leave the others for later. An “attention mechanism” in a computer program is like that. It helps the program decide which words are the most important to pay attention to right now, and which ones it can ignore.

Now, for a computer scientist:

Attention mechanisms are a key part of many modern neural networks, especially those used for natural language processing tasks. The basic idea behind attention is that it allows the model to focus on different parts of the input when producing the output. In the context of transformer models, attention is used to weigh the significance of different input words for each output word.

Here’s a simplified example of how you might implement a basic form of attention in PyTorch:

import torch
import torch.nn as nn
import torch.nn.functional as F

class Attention(nn.Module):
    def __init__(self, hidden_size):
        super(Attention, self).__init__()
        self.hidden_size = hidden_size
        self.attn = nn.Linear(self.hidden_size * 2, hidden_size)
        self.v = nn.Parameter(torch.rand(hidden_size))

    def forward(self, hidden, encoder_outputs):
        timestep = encoder_outputs.size(0)
        h = hidden.repeat(timestep, 1, 1).transpose(0, 1)
        encoder_outputs = encoder_outputs.transpose(0, 1)  # [B*T*H]
        attn_energies = self.score(h, encoder_outputs)
        return F.softmax(attn_energies, dim=1).unsqueeze(1)

    def score(self, hidden, encoder_outputs):
        energy = F.relu(self.attn([hidden, encoder_outputs], 2)))  # [B*T*2H]->[B*T*H]
        energy = energy.transpose(1, 2)  # [B*H*T]
        v = self.v.repeat(encoder_outputs.size(0), 1).unsqueeze(1)  # [B*1*H]
        energy = torch.bmm(v, energy)  # [B*1*T]
        return energy.squeeze(1)  #[B*T]

This is a simplified implementation of attention and doesn’t cover all the details. For example, transformer models typically use a more sophisticated form of attention called “multi-head attention”, which uses multiple sets of attention weights to focus on different parts of the input. They also incorporate positional information into the attention weights to account for the order of words in a sentence.

For a full implementation of a transformer model, including the attention mechanism, you might want to refer to the The Annotated Transformer by Harvard NLP group, which provides a step-by-step walkthrough of the model with accompanying PyTorch code.

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Author: Steve Digital