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Your conclusion is absurd. If you agree this model is overall an improvement on the prior one, ie performs better on the same tasks and can do tasks the previous one couldn’t, it’s basically a given that it will get more use than GPT4

Better in niche areas doesn't mean its going to get more use.

Everyone was super hyped about all the "cool" stuff that GPT4 could solve, but in the end, you still can't do things like give it a bunch of requirements for a website, let it run, and get a full codebase back, even though that is well within its capabilities. You have to spend time with prompting in to get it to give you what you want, and in a lot of cases, you are better off just typing the code yourself (because you can visualize the entire project in your head and make the right decisions about how to structure stuff), and using it for small code generations.

This model is not going to radically change that. It will be able to give you some answers that you had to specifically manually prompt before automatically, but there is no advanced reasoning going on.

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.

Definition of Reasoning in AI:

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.

>What I mean is that when its generate a response, the computation happens on a snapshot from input to output, trying to map a set of tokens, into a set of tokens. Model doesn't operate on a context larger than the window

The weights in the model have the larger context, the context length size of data is just the input, which then gets multiplied by those weights, to get the output.

For your “better example”, it can literally already do this. I just tested this with 4o and it worked great (and I’ll say more accurately than a human would estimate most likely). I used 4o because it appears that the chain of thought models don’t accept image input yet.

I don’t want to post identifiable information so I will avoid linking to the convo or posting screenshots but you can try it yourself. I took 5 pictures of a child’s magnetic tile sitting on the floor and here is the output:

Me: (5 pictures attached)

Me: Estimate how much this weighs.

ChatGPT 4o: From the images, it appears that this is a small, plastic, transparent, square object, possibly a piece from a magnetic tile building set (often used in educational toys). Based on the size and material, I estimate this piece to weigh approximately 10 to 20 grams (0.35 to 0.7 ounces). If it's part of a toy set like Magna-Tiles, the weight would be on the lower end of that range.

But for some reason I have a feeling this isn’t going to be good enough for you and the goalposts are about to be pushed back even farther.

“In the cypher example, all it’s doing is basically using a separate model to break a particular model into chain of thought, and prompting that. And there is plenty in the training set of GPT about decrypting cyphers.” I’m sorry, but are you suggesting that applying a previously learned thought process to new variables isn’t reasoning? Does your definition of reasoning now mean that it’s only reasoning if you are designing a new-to-you chain of thought? As in, for deciphering coded messages, you’re saying that it’s only “reasoning” if it’s creating net new decoding methodologies? That’s such an absurd goalpost.

You wouldn’t have the same goalposts for humans. All of your examples I bet the average human would fail at btw. Though that may just be because the average human is bad at reasoning haha.

I didn't ask for an estimation, I asked for the exact weight. A human can do this given the process I described.

If the chain of thought was accurate, then it would be able to give you an internemdiate output of the shape in some 3d format spec. But nowhere in the model does that data exist, because its not doing any reasoning, it just still all statistically best answers.

I mean sure, you could train a model on how to create 3d shapes out of pictures, but again, thats not reasoning.

I don't get why people are so attached to these things being intelligent. We all agree that they are usefull. Like it shouldn't matter if its not intelligent to you or anyone else.