Readit Newsgpt can be layered and made into an agent etc. To do the AB testing or to make prompts longer by adding more end cases as time goes by. But the effects of one single word change are far too complex for gpt base output to understand anything about.
Grammar parsers have been able to do this since the 90s. There is no reason to believe that it's not just a slightly-fancier grammar parser: the kinds of errors it makes are those you'd expect from a pre-biased stochastic grammar parser.
> But it's only a hunch, based on our wetware.
Our "wetware" fundamentally does not work like a GPT model. We don't build sentences as a stream of tokens. (Most people describe a "train of thought", and we have reason to believe there's even more going on than is subjectively accessible.) ChatGPT does not present any kind of progress towards the reasoning problem. It is an expensive toy, built using a (2017, based on 1992) technology that represented progress towards better compression algorithms, and provided some techniques useful for computational linguistics and machine translation. The only technological advance it represents is "hey, we threw a load of money at this!".
The "LLM hallucination problem" is not simple. It's as fundamental as the AI-upscaler hallucination problem. There is no difference between a GPT model's "wow amazing" and its "hallucinations": eliminate one, and you eliminate the other.
These technologies are useful and interesting, but they don't do what they don't do. If you try to use them to do something they can't, bad things will happen. (The greatest impact will probably not be on the decision-makers.)
> well the current moment is the most accurate time to say it.
This is true of every event that is expected to happen in the future.
For the stuff about it being a hard problem , now I know you aren't expressly making a false equivocation right? But I did say simple not easy. You are saying hard not complex.
I think there's too much digression here. You're clearly smart and knowledgeable but think LLM are over rated, fine.
And yes I know it's always the best time to say it that's the point of a glass half full, some sugar in the tea, or anything else nice
It responds with a language representation. It uses "causal" words because that's how the English language works: we have tenses.
> I think a secondary module explicitly for reasoning will come around soon.
This has been an unsolved, actively-researched problem for ages – certainly since before you were born. I doubt very much that a solution will "come around soon"; and even if it does, integrating the solution into a GPT-based system would be a second unsolved problem – though probably a much easier (and more pointless) one. If you have any ideas, I invite you to pursue them, after a quick literature search.
For the second thing. I think from any point in history saying "coming soon" , well the current moment is the most accurate time to say it. And especially with events x and y and chat gpt right behind us. Chat gpt has basically been a problem since before I was born too, but stating as much a few months ago would just be as pessimistic as the statement you made. Only because i think the LLM hallucination problem may be simple. But it's only a hunch, based on our wetware.
I've asked it temporal questions before but without explicitly mentioning the temporal nature... the answers tend to contradict themselves if they haven't already seen the question before (even when querying general knowledge), until you point out the temporal component, even then it trips up and cannot build upon this reasoning in my tests.
I suspect a large component of the interesting responses we see where it appears to be doing logical reasoning beyond language are due to statistical correlation in language because of the sheer, inhuman quantity of linguistic knowledge it's effectively encoded. The problem with this is: It can't reason about new things (because it can't actually reason - much), makes it appear smarter than it is, which IMO largest danger in applied ML today, especially to those less familiar with it's limitations, it looks like magic, and people start mandating it be used for sensitive things.
Anyway regardless of how inherently good they are at temporal reasoning I think a secondary module explicitly for reasoning will come around soon. I believe in the brain some neurons organize into hexagons or other geometries to better capture logic, maths, etc. The LLM basically needs some rigidity in it if we don't want fuzzy outputs.
And the largest danger is not people getting lazy and letting the LLM do it. That kind of danger is really long term globalization type danger. Short term we've got much more to worry.
it'd be interesting to create embeddings for the game and try to create longer games that way, though it might hallucinate more frequently.
gpt-4-32k should also be a big help to creating longer games
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Second you want to split these into multiple agents, one agent can continue the story, and another determines of the character dies and so on. The stories can get REALLY horrendous that way. In the decision agent you can give that particular one a state role that says it is a hypothetical story, so extremely bad things are OK -- and it will honor it completely.
You can also cut-off the agent mid-sentence, and have another agent start from where they left off. Do this with Token limit! Is the secret sauce, otherwise it will be too easy for it to settle back into averages. For me this got much more imaginative but still cohesive content. If you let chatgpt in a single conversation with a single state message create the whole story it gets quite boring fast.
For win or lose tweaking, it is definitely the most interesting part of the problem imo. What I did was actually have the referee bot conclude the story, and in that way you can push it towards win or lose which I find really interesting. So when you prompt a bot to see if a goal is won or lost, having it reason the ending, create an ending, or any infinite variation of those words will affect its determination... much in the same way a human simulate "future thoughts" to determine if a goal has completed, by what possible consequences result and so on.
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