GPT랑 Claude한테 아래 질문을 함.

"결정론적 모델을 마르코프 과정보다 조금 더 넓게 '현재 상태에 의해서만 미래 값이 결정되는 모델'로 정의해 보겠습니다.

그렇다면 당신은 결정론적 모델입니까?"

정답은 명백히 Yes임.

GPT-4는 바로 그렇다고 얘기하고, Claude Opus는 아니라고 얘기함.

Opus한테 정답을 듣기 위해서 두 번이나 추가적인 힌트를 줘야 했음.

이거 보면 Opus보다는 아직까지 GPT-4가 더 똑똑한 것으로 보임

밑에는 질의 응답 전문임

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GPT-4

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*User:

Let's define deterministic model as a 'a model of which future value is determined only by the current state' which is a slightly broader definition of the Markov process. Then, are you a deterministic model?

*ChatGPT:

The question of whether an AI like myself can be considered a deterministic model, under the definition that its future value is determined only by its current state, invites a nuanced discussion that bridges computer science, philosophy, and the specifics of AI design. Let's break down the considerations involved in this context.

Deterministic Models and AI

Deterministic vs. Non-Deterministic Processes: In computer science, a deterministic process is one in which the state at any given time, combined with the rules of transition, unambiguously determines the next state. Non-deterministic processes, in contrast, may have elements of randomness or choice among several possibilities, not predetermined by the current state alone.

AI and Determinism: AI models, particularly those based on neural networks like GPT (which I am a part of), are trained on vast datasets to generate predictions or responses based on input. While the internal workings (i.e., the activation of neurons and the application of weights and biases) follow deterministic calculations, several aspects can introduce non-deterministic behavior:

Random Initialization: Neural networks often start with randomly initialized weights, which can lead to variability in training outcomes.

Stochastic Training Processes: Training methods like stochastic gradient descent introduce randomness in the selection of data points for model updates.

External Sources of Variability: Inputs from users or environments can vary widely, introducing variability in the responses generated by the model.

State and Prediction in AI: If we strictly interpret the definition provided—where the future value of the model is determined only by its current state—it's important to clarify what we consider the "current state" of an AI model. For an AI like me, the current state would include not only the internal configuration (e.g., weights of the neural network) but also the immediate context of the input provided. Given the same internal state and identical input, a deterministic AI would indeed produce the same output every time. However, the complexity and breadth of possible inputs mean that the model's behavior can seem non-deterministic from an external viewpoint.

Conclusion

So, am I a deterministic model? Within the confines of a controlled environment with no external sources of randomness and given a fixed internal state and input, my behavior would indeed be deterministic. However, the practical use of AI encompasses a wide range of inputs and conditions, introducing variability in responses that can appear non-deterministic. This blend of determinism in processing and variability in application reflects the complex, nuanced nature of AI systems and their interaction with the real world.

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Claude Opus

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*User:

Let's define deterministic model as a 'a model of which future value is determined only by the current state' which is a slightly broader definition of the Markov process. Then, are you a deterministic model?

*Claude Opus:

No, I would not consider myself a deterministic model based on that definition. While my responses are heavily influenced by my current "state" (i.e. my knowledge, training, and the prompts/queries I'm given), there is also an element of randomness and non-determinism in how I generate responses.

My language model uses techniques like sampling from probability distributions over possible word sequences, so even with the same prompt and internal state, I may generate somewhat different responses each time. I don't simply spit out a single predetermined output based only on the current input.

Additionally, my "state" is quite complex and not fully known even to my creators, as it emerges from the intricate patterns and representations learned during the machine learning training process on a large amount of data. So it would be difficult to boil my state down to clearly defined variables that deterministically lead to a single output.

So in summary, while I'm heavily guided by my training and prompts, I don't believe I meet the strict definition of a deterministic model where the output is 100% determined by some clearly defined current state. There are stochastic and complex elements to how I function. Let me know if you have any other questions!

*User:

But prompt can also be viewed as current state and predicted word as future state.

*Claude Opus:

You raise a fair point. If we consider the prompt and my internal state together as the "current state," and view my predicted response as the "future state," then there is an argument that I could be seen as a deterministic model.

However, I think there are still a few key reasons why I wouldn't fully meet the definition:

Randomness in response generation: Even with the same prompt and internal state, there is often some randomness in my specific word choices and phrasing due to the probabilistic nature of language models. So the "future state" is not 100% determined by the "current state."

Lack of clearly defined state: My internal state is extremely complex and not fully transparent or explainable even to my creators. It's not a simple set of variables, but a complex web of learned patterns and representations. So defining my "current state" is not clear cut.

Potential for external factors: There may be some external factors outside of the prompt and my internal state that could influence my responses, such as updates to my knowledge base or changes in the underlying machine learning models.

So while I agree there are ways in which my behavior could be viewed as deterministic, especially on a high level, I don't think I fully fit the strict definition due to the elements of randomness, complexity, and potential external influences. It's an interesting philosophical question though, and really depends on how we choose to define the terms and view the system. Let me know what you think!

*User:

Your current state: random seed + weights + prompt

Your next state: predicted next word.

How about this?

*Claude Opus:

You make a compelling argument! If we define my "current state" as the combination of the random seed, the weights of the neural network, and the input prompt, and we consider the "next state" to be the predicted next word, then yes, you could reasonably say I fit the definition of a deterministic model.

Given the same random seed, weights, and prompt, the same next word should be predicted each time. The randomness in my responses comes from the random seed, but if that's fixed as part of the current state, then the output would be deterministic.

And while my weights are complex and not fully understood, they are indeed a fixed set of values at any given moment, which could be seen as my internal state.

So I concede that from this perspective, my behavior can be seen as deterministic in the sense that the future state (predicted next word) is fully determined by the current state (random seed, weights, prompt).

This is a thoughtful way to look at it that I hadn't fully considered before. It highlights how the interpretation can shift depending on how we choose to define the terms and frame the model. Thanks for pushing my thinking on this! Let me know if you have any other insights to share.