What is “advanced reasoning” and why isn’t this doing it? If you made a chinese room to output coherent chains of reasoning, it would functionally be equally useful to an actual reasoner, with or without the capacity for sentience or whatever.
Basically, if you had a model that could reason, it should be able to figure out new infromation. I.e lets say you map some bytes of the output to an api for creating a tcp socket and communicating over it. The model should be able figure out how to go out on the internet and search for information, all by itself, without any explicit training on how to do that.
So without prior information, it should be able to esentially start out with random sequences in those bytes, and seeing what the output is, then eventually identify and remember patterns that come out. Which means there has to be some internal reward function for someting that differentiates good results from bad results, and some memory that the model has to remember what good results are, and eventually a map of how to get information that it needs (the model would probably stumble across Google or ChatGPT at some point and time after figuring out http protocol, and remember it as a very good way to get info)
Philosophically, I don't even know if this is solvable. It could be that we just through enough compute at all iterations of architectures in some form of genetic algorithm, and one of the results ends up being good.
In artificial intelligence, reasoning is the cognitive process of drawing conclusions, making inferences, and solving problems based on available information. It involves:
Logical Deduction: Applying rules and logic to derive new information from known facts.
Problem-Solving: Breaking down complex problems into smaller, manageable parts.
Generalization: Applying learned knowledge to new, unseen situations.
Abstract Thinking: Understanding concepts that are not tied to specific instances.
AI researchers often distinguish between two types of reasoning:
System 1 Reasoning (Intuitive): Fast, automatic, and subconscious thinking, often based on pattern recognition.
System 2 Reasoning (Analytical): Slow, deliberate, and logical thinking that involves conscious problem-solving steps.
Testing for Reasoning in Models:
To determine if a model exhibits reasoning, AI scientists look for the following:
Novel Problem-Solving: Can the model solve problems it hasn't explicitly been trained on?
Step-by-Step Logical Progression: Does the model follow logical steps to reach a conclusion?
Adaptability: Can the model apply known concepts to new contexts?
Explanation of Thought Process: Does the model provide coherent reasoning for its answers?
Analysis of the Cipher Example:
In the cipher example, the model is presented with an encoded message and an example of how a similar message is decoded. The model's task is to decode the new message using logical reasoning.
Steps Demonstrated by the Model:
Understanding the Task:
The model identifies that it needs to decode a cipher using the example provided.
Analyzing the Example:
It breaks down the given example, noting the lengths of words and potential patterns.
Observes that ciphertext words are twice as long as plaintext words, suggesting a pairing mechanism.
Formulating Hypotheses:
Considers taking every other letter, mapping letters to numbers, and other possible decoding strategies.
Tests different methods to see which one aligns with the example.
Testing and Refining:
Discovers that averaging the numerical values of letter pairs corresponds to the plaintext letters.
Verifies this method with the example to confirm its validity.
Applying the Solution:
Uses the discovered method to decode the new message step by step.
Translates each pair into letters, forming coherent words and sentences.
Drawing Conclusions:
Successfully decodes the message: "THERE ARE THREE R'S IN STRAWBERRY."
Reflects on the correctness and coherence of the decoded message.
Does the Model Exhibit Reasoning?
Based on the definition of reasoning in AI:
Novel Problem-Solving: The model applies a decoding method to a cipher it hasn't seen before.
Logical Progression: It follows a step-by-step process, testing hypotheses and refining its approach.
Adaptability: Transfers the decoding strategy from the example to the new cipher.
Explanation: Provides a detailed chain of thought, explaining each step and decision.
Conclusion:
The model demonstrates reasoning by logically deducing the method to decode the cipher, testing various hypotheses, and applying the successful strategy to solve the problem. It goes beyond mere pattern recognition or retrieval of memorized data; it engages in analytical thinking akin to human problem-solving.
Addressing the Debate:
Against Reasoning (ActorNightly's Perspective):
Argues that reasoning requires figuring out new information without prior training.
Believes that LLMs lack feedback loops and can't perform tasks like optimizing a bicycle frame design without explicit instructions.
For Reasoning (Counterargument):
The model wasn't explicitly trained on this specific cipher but used logical deduction to solve it.
Reasoning doesn't necessitate physical interaction or creating entirely new knowledge domains but involves applying existing knowledge to new problems.
Artificial Intelligence Perspective:
AI researchers recognize that while LLMs are fundamentally statistical models trained on large datasets, they can exhibit emergent reasoning behaviors. When models like GPT-4 use chain-of-thought prompting to solve problems step by step, they display characteristics of System 2 reasoning.
Final Thoughts:
The model's approach in the cipher example aligns with the AI definition of reasoning. It showcases the ability to:
Analyze and understand new problems.
Employ logical methods to reach conclusions.
Adapt learned concepts to novel situations.
Therefore, in the context of the cipher example and according to AI principles, the model is indeed exhibiting reasoning.
