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Comment by CSMastermind | Hacker News Reader

He said if you're a student interested in building the next generation of AI systems not to work on LLMs. Which is different than telling no one to study or work on them.

He's been pretty consistent in saying that he doesn't think they're the future because they lack a world model.

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To be fair, designing a world model or a proper knowleddge representation system would go more into the foundations of mathematics that would likely require a literal genius to figure out. We'd be right back to the failures of symbolic systems during the AI Winter in the 90s.

I have the feeling that both, symbolic methods and Reinforcement Learning, will be play a major role in more intelligent AI systems.

I promped ChatGPT as follows

    I have a logical puzzle. I want you to write code for solving it using an SAT (or SMT) solver of your choice.

    "Question 2: Amit, Bharati, Cheryl, Deepak, and Eric are five friends sitting in a restaurant. They are wearing caps of five different colours — yellow, blue, green, white and red. Also, they are eating five different snacks — burgers, sandwiches, ice cream, pastries, and pizza.

        The person wearing a red cap is eating pastries.
        Amit does not eat ice cream, and Cheryl is eating sandwiches.
        Bharati is wearing a yellow cap and Amit wearing a blue cap.
        Eric is eating pizza and is not wearing a green cap."

It gave me python code for solving the problem using pysmt. Each constraint it added had a nice little comment referring back to the problem statement. After correcting a trivial typo, the code ran and produced the correct answer.

So in other words, LLM's are already almost powerful enough to use and integrate with a symbolic approach.

I asked: show me tbe nearest places to xxx of historical or natural beaty interest and add coordinates wikipedia, and google maps links. Create a python program that uses the coordinates and optimizes visiting them.

It created a functioning python program and I learnt scipy has a function that can solve tsp problems with a different name, Then it run it but timed out but ok

Does a human's inability to do that in less than 20 seconds prove that we aren't intelligent?

no. thinking takes time. the reason a computer is faster at this is because a computer doesn't think.

Our existing knowledge representation systems can already deal with this. This is beyond my field of knowledge, but I guess a better example of a problem we can't solve easily yet would be achieving paraconsistent logic; A logical system that can deal with contradictions without falling into the principle of explosion.

Like I said, this is some very abstract stuff that delves more into philosophy and mathematics that not many people are going to doing. The kind of system that people are trying to build here would be close to a "Theory of Everything".

I continue to find him a clown and blowhard.

They certainly have a world model. What that is and how it compares to ours is the interesting part.

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