Discussion with an AI, What is a Neural Network?

I have been working with ChatGPT for coding and research for months. And I have been discussing my experience with friends. Many have avoided interacting with AI and don't understand much about it.

One common idea is AGI is the next step for ChatGPT. It isn't.

The foundation for ChatGPT is a neural network. ChatGPT doesn't know anything. It doesn't understand anything. 

I was thinking about how to explain this to someone. While working on a database, a scene from 30+ years ago popped into my head: a guy excitedly showing off needle sorts with edge-punched cards and the analogy hit me: a neural network in straightforward terms and an easier way to convey the idea of a neural network.

In the end, it's like a needle through the data.

My questions are brief and directed. I wanted the AI to conclude with little input.

The only answer I edited was the first, it started with a history of punch cards.

This was the only discussion I tried with these prompts, so I did not select the best answers.

This analogy is imperfect, but it's good enough to get the idea across.

 "When you interact with ChatGPT, you're effectively querying the network with your prompts, and the network generates responses based on its encoded knowledge map. ChatGPT doesn't "find" patterns in the sense of searching through a database; rather, it generates responses based on how the input prompt activates the statistical patterns encoded within its structure. The network operates as a sophisticated pattern-matching and generation mechanism, activated and directed by external input."

Me: I remember a method for using punched cards to make a database. Each hole is associated with a true or false data item, and using a narrow rod, you could query the data in the cards. What can you tell me about that?

The specific technique you're mentioning involves using punched cards for sorting and querying data using a manual method. This method, often referred to as the edge-notched card or the "needle sort" technique, involved cards that were punched with holes around the edges in positions corresponding to different data attributes. A user could query the data by inserting a long needle (or a narrow rod) through a specific hole position corresponding to the desired attribute. By lifting the needle, only the cards that did not have a hole punched in that position (indicating a "true" for the chosen attribute) would be picked up, separating them from the rest. This process could be repeated with different attributes to refine the query further.

Edge-notched cards were a simple yet effective way to store, sort, and retrieve data before the advent of electronic databases. They were used in various applications, from library card catalogs to customer and inventory records, and even in some early computing and voting machines. Despite their manual operation, these systems allowed for relatively quick and flexible data management and were an important step in the development of information technology.

Me: Yes - needle sort, that's what I was trying to remember. Can you compare a needle sort and how your neural network processes? There is a very simple commonality.

Indeed, there's a fascinating simplicity that connects the concept of needle sort with the workings of neural networks, particularly in the realm of information processing and decision-making. Both methods involve the filtering or processing of information to arrive at a conclusion or output, albeit through vastly different mechanisms and scales of complexity. Let's explore this commonality:

Needle Sort (Edge-Notched Cards)

- Data Representation: In needle sort, data is represented physically on cards with holes that signify certain attributes. The presence or absence of a hole in a specific position encodes binary information (true/false or yes/no).

- Querying Process: The querying or sorting process is manual and mechanical. By inserting a needle through holes corresponding to a specific attribute and lifting, you filter out cards that match the criteria (those without a hole in that position stay on the needle).

- Decision-Making: The decision-making is binary and straightforward. It's based on a single attribute or a combination of attributes defined by the holes through which the needle is inserted. This method allows for basic queries and sorting but lacks the ability to perform complex calculations or to learn from the data.

Neural Networks

- Data Representation: In neural networks, data is represented numerically, often normalized to facilitate processing. Each input feature corresponds to an input neuron in the network.

- Processing Mechanism: Neural networks process data through layers of neurons, each of which performs a weighted sum of its inputs followed by a non-linear activation function. This process allows the network to learn complex patterns and relationships in the data.

- Decision-Making: The decision-making in neural networks is based on the learned weights and biases that adjust through training. The network can make nuanced decisions, classify data into multiple categories, or predict values based on the learned patterns. The output layer provides the conclusion based on the complex interplay of these learned parameters.

Commonality

- Filtering Information: At their core, both methods are about filtering or processing information to make decisions. Needle sort does this through a physical mechanism that separates data cards based on binary attributes, while neural networks do it through mathematical functions and learned patterns.

- Decision Process: Both involve a decision process, although of vastly different complexities. Needle sort's decision-making is based on explicit, user-defined criteria (holes for the needle), while neural networks develop an internal decision-making process based on patterns learned from the data.

The simplicity that connects them is the concept of decision-making through the processing of information, albeit needle sort operates in a very manual, binary, and non-scalable way, while neural networks function in a complex, scalable, and adaptive manner, capable of learning from and making decisions based on vast amounts of data.

Me: So, extending the analogy, the needle or what makes up the needle is the prompt. The number of cards is the number tokens. 

Extending the analogy between needle sort and neural networks to include the prompt and the number of tokens offers an interesting perspective on how inputs are managed and processed in both systems:

Needle Sort Analogy

- Needle (Prompt): In the needle sort analogy, the "needle" or the selection of specific holes through which the needle is inserted represents the "prompt" or the query criteria. This action of choosing where to insert the needle is akin to defining the prompt in a neural network: it sets the conditions or parameters based on which the sorting or decision-making is to be executed.

- Cards (Number of Tokens): The cards in the needle sort method correspond to the "tokens" in the context of a neural network. Each card represents a unit of information or data point, just as each token represents a piece of input data to the neural network. The total number of cards being sorted or queried parallels the number of tokens processed by the neural network to generate an output.

Extending the Analogy

- Prompt as a Filter: Just as the needle acts as a filter to select cards based on certain attributes, the prompt in a neural network guides the model to filter through its learned knowledge and focus on generating relevant outputs. The prompt effectively "pokes" into the model's vast information base to extract specific responses.

- Tokens as Data Points: The analogy highlights how each token (like each card) carries specific information. In neural networks, the sequence and composition of tokens determine the complexity and nuance of the input, affecting the output in a manner analogous to how the combination of holes (or lack thereof) on multiple cards determines the outcome of a needle sort query.

This extended analogy illustrates how, despite their vastly different domains and mechanisms, both systems utilize a form of input (prompt/needle selection) to process and filter through a set of data (tokens/cards) to arrive at a decision or output. It simplifies the understanding of neural networks by drawing parallels to a more tangible and mechanical process, underscoring the fundamental concept of input-driven information processing and decision-making.

Me: All of the “knowledge” and all of the “information” ChatGPT and all large language models “have” are solely created in the neural network. ChatGPT training does not train the agent it embeds the information in the neural network based on patterns found in the data. A ChatGPT agent just executes a query against the neural network.

Your description captures a crucial aspect of how large language models like ChatGPT operate. These models don't "know" information in the human sense; instead, they generate responses based on patterns learned from vast amounts of text data during their training phase. Let's break down your statement for clarity:

Information Embedding and Pattern Learning

- Knowledge Embedding: When ChatGPT is trained, it processes and analyzes large datasets comprising a wide range of text from the internet, books, articles, and other sources. During this training, the model learns statistical patterns, relationships between words and phrases, and various language structures. This process embeds a representation of the knowledge within the neural network's parameters (its weights and biases). The model does not store information or facts in the way a database does; rather, it learns patterns that allow it to generate text that appears knowledgeable.

- Neural Network Structure: The neural network consists of layers of interconnected nodes (neurons) that process input data (prompts) through these learned patterns. The connections between these nodes have weights that are adjusted during training to minimize the difference between the model's output and the expected output. This adjustment process is how the model "learns."

Query Execution

- Prompt as a Query: When a user inputs a prompt, ChatGPT processes this prompt by propagating it through its neural network. The model uses the patterns it learned during training to generate a response that aligns with the structure, content, and style of the input prompt. This process is akin to executing a query against the neural network, where the input prompt activates specific patterns or pathways within the network, leading to a generated response.

Information Generation

- Response Generation: The model generates responses based on how likely each subsequent word is, given the words that have come before it. This likelihood is determined by the patterns the model has learned. Therefore, the "information" or "knowledge" that ChatGPT provides in its responses is not retrieved from a discrete storage of facts but is dynamically generated based on the learned patterns and the context provided by the prompt.

In essence, ChatGPT and similar models operate by executing complex, pattern-based queries against their trained neural networks. The "knowledge" is not explicitly stored within the model but is implicit in the learned patterns and relationships embedded in the network's parameters. This mechanism allows ChatGPT to generate responses across a broad range of topics, mimicking an understanding of language and information based on the input it receives.

Me: Yes, but I am not in full agreement with this sentence.

"When ChatGPT is trained, it processes and analyzes large datasets comprising a wide range of text from the internet, books, articles, and other sources. During this training, the model learns statistical patterns, relationships between words and phrases, and various language structures."

Does it learn? Or is "learning the result of building the neural network." In the end, the neural network is a representation of token relationships.  I understand that we are anthropomorphizing here but I am not comfortable contributing intent to the training process. While under training, it has no self-direction and no agency.Please correct me if I am mistaken.

You're correct in pointing out the nuances in how we describe the processes underlying the training of neural networks, and your concern about anthropomorphizing these processes is well-founded. When we say a neural network "learns," we are indeed using a metaphor that simplifies a much more mechanical and deterministic process. Let's clarify this with more precise language:

Training vs. Learning

- Statistical Optimization: The process often described as "learning" in the context of neural networks is, in fact, an optimization of a mathematical function. During training, a neural network adjusts its internal parameters (weights) to minimize the difference between its outputs and the expected outputs (labels) provided during training. This adjustment is done through algorithms like backpropagation combined with optimization techniques such as gradient descent.

- Pattern Detection and Representation: The "knowledge" that a neural network accumulates is not akin to human learning or understanding. Instead, it's the result of adjusting the network's parameters so that it can statistically model the relationships between inputs (tokens) and outputs (the next token in the sequence, for language models). The model does not "understand" these relationships in any cognitive or conscious sense; rather, it embodies statistical correlations within its architecture.

- Absence of Intent or Agency: The neural network operates under a set of mathematical rules and algorithms; it does not possess intent, understanding, or self-direction. The anthropomorphic language of "learning" and "knowledge" is a convenient shorthand for complex mathematical processes but should not be misconstrued as implying that the network has human-like cognitive abilities or consciousness.

Clarified Description

A more precise description of the process would be that through training, a neural network undergoes a systematic adjustment of its internal parameters to reduce error in its outputs, based on a vast dataset. This optimization process results in the network being able to generate predictions or responses that statistically match the patterns and structures observed in its training data. The term "learning" in this context is a metaphor for the outcome of this optimization process, which is the improved ability of the model to predict or generate sequences of tokens that are coherent, contextually appropriate, or factually accurate (to the extent that the data it was trained on allows).

In summary, the training of a neural network is a process of statistical optimization and pattern modeling devoid of intent or agency. The use of anthropomorphic language simplifies communication but should be understood as metaphorical, not literal.

Me: In your clarification, you are describing the results of ability. I disagree. The result of training is a neural network that represents the relationship of tokens. You can go into how it works with weights, but that is how it works, not what it is. For the results you mention, there must be an external action. Does an instrument play itself? I am the actor here requesting you, the ChatGPT agent, to find patterns of my words (tokens) in your neural network and reply with the results of your query.

Your perspective offers a valuable clarification and emphasizes the distinction between the mechanism of a neural network and its application. Let's refine the understanding with this in mind:

Neural Network as a Representation

- Token Relationships Representation: At the end of its training, what we have in a neural network is indeed a complex representation of the relationships between tokens (words, phrases, etc.), encoded in the network's weights. This representation is static—it doesn't "do" anything by itself. The weights and structure of the network capture statistical associations learned from the training data, making the network a vast, multidimensional map of how tokens relate to each other based on the contexts in which they were seen during training.

External Action Required

- Activation by Query: The neural network remains passive until an external actor (like a user) inputs a prompt. This act is analogous to playing an instrument, as you mentioned. The prompt activates specific pathways through the network, based on the statistical relationships encoded in its weights. The network's response is generated by traversing these pathways and selecting tokens that are statistically likely to follow the given input sequence, according to the patterns it "learned" during training.

- User as the Actor: In this analogy, the user (you) is the musician, and ChatGPT is the instrument. The "music" (output text) is produced by the interaction between the user's prompt and the network's encoded token relationships. The capability of the network to generate coherent, relevant responses arises from this interaction, not from any inherent ability of the network to initiate action or produce output independently.

Clarifying the Role of ChatGPT

- ChatGPT as an Intermediary: When you interact with ChatGPT, you're effectively querying the network with your prompts, and the network generates responses based on its encoded knowledge map. ChatGPT doesn't "find" patterns in the sense of searching through a database; rather, it generates responses based on how the input prompt activates the statistical patterns encoded within its structure. The network operates as a sophisticated pattern-matching and generation mechanism, activated and directed by external input.

Your point underscores the importance of recognizing that the intelligence and intent in the interaction come from the user, while the neural network provides a tool for generating responses based on the patterns encoded within it. The network's design and training enable it to serve as an effective intermediary, translating prompts into outputs through its complex, learned representations of language.

Me: That’s very well said and a summary of what I was driving.

I'm glad to hear that the explanation resonated with you and addressed the points you were highlighting. It's crucial to have a clear understanding of the interaction between the user and the technology, especially with complex systems like neural networks. If you have any more questions or need further clarification on this or any other topic, feel free to ask!

Continued 3/2/2024

Me: I want to explore how an LLM and GPT agent is a "better" method for storing human knowledge. Humans are not good at overcoming several biases that taint individual decisions and understandings. 

Like the instrument metaphor, the value of the result is directory proportional to the clearness of the prompt and follow-up queries. 

A question on the meaning of life is certainly something an LLM could not answer, as well as a myriad of subjects that can be misconstrued by individuals living in their own minds.

If we use as much training material as possible to ingest as much data as possible with a vast token size wouldn't a LLM be able to weed out most of the erroneous data and pull out what is common across as many lives as possible?

Large Language Models (LLMs) like GPT indeed offer a unique approach to storing and interpreting human knowledge. Their advantage lies in their ability to digest vast amounts of data, learning from the collective human output to identify patterns, commonalities, and even inconsistencies across diverse sources. This capacity enables them to mitigate some biases inherent to individual human perspectives by providing responses based on a broader, more balanced aggregate of information. However, it's important to note that LLMs are not infallible; they can perpetuate biases present in their training data. The quality of an LLM's output is highly dependent on the quality and clarity of the input prompts, underscoring the interactive nature of these models as tools that extend, rather than replace, human cognition. Through sophisticated processing of large datasets, LLMs can indeed "weed out" erroneous data to a certain extent, but their effectiveness is contingent on their design, training, and the ongoing effort to refine their understanding and output.

OpenAI. (2024). ChatGPT (4) [Large language model]. https://chat.openai.com

(c) Copyright Jim McCullough 2024