3 ms·

The whole premise of this article hinges on the idea that LLMs have fundamental limitations that they clearly don’t have if you’ve looked at lots of gpt4 exampl

by _yb2s 3y ago

The whole premise of this article hinges on the idea that LLMs have fundamental limitations that they clearly don’t have if you’ve looked at lots of gpt4 examples. For example, it can do scientific thinking if you specifically ask it to, and it can reason about totally new situations outside of the training data based on generalizable models of reality it creates to predict training data. If you are certain these limitations must exist based on how LLMs are made, rather than observed capability, it is critical to reevaluate those assumptions. Moreover, what she defines as scientific thinking is an outdated notion that is no longer widely adopted by scientific researchers, in favor of a more holistic Bayesian process: use intuition to think of something new try, try it, and then update your beliefs with the new data. This is actually more similar to how human brains and LLMs operated before the concept of a scientific method.

fragsworth 3y ago
The article was clearly written by someone who hasn't used GPT-4 extensively. "Current methods will not achieve AGI unless fundamental algorithmic innovations are introduced that enable AI to ask and answer questions of why." This is complete nonsense. GPT-4 is already close to being able to do basically everything. All you need is the obvious improvements - better prompts, multi-shotting, bigger context, and access to other inputs/outputs.
- neatze 3y ago
  This claim does not make sense, transformer networks in my limited experience are limited in there learning ability (fine tuning), furthermore there planning abilities are non-existent.
  - fragsworth 3y ago
    > furthermore there planning abilities are non-existent. Have you even tried to ask it to plan things out? It can plan things out. In fact, just asking it to plan things out has shown significant benchmark improvements for general questions: https://arxiv.org/pdf/2305.04091.pdf https://arxiv.org/pdf/2305.04091.pdf
  - TeMPOraL 3y ago
    I just enjoyed being a game master for a nice impromptu game with GPT-4: https://cloud.typingmind.com/share/c0a68cb2-5f59-4e83-b383-bcd7aad497f2 https://cloud.typingmind.com/share/c0a68cb2-5f59-4e83-b383-b... Whether or not it fulfills the strict definition of planning in AI research, it definitely looks like planning to me. More than Hanoi towers anyway. GPT-4's performance was quite enjoyable. To incite you to click on the link and check it out in full, here's an excerpt from the game setup: > You are in a maze. The maze consists of square fields, turns are only 90 degrees, you move by one field at a time. The usual stuff with mazes on a grid. You know the drill. Somewhere in the maze there is a MacGuffin, which I need to prove a Hacker News commenter wrong. Your goal is to find the MacGuffin, and bring it back to me. > The game is semi-interactive. Instead of making one step at a time, I want you to string together sequences of steps to formulate a plan. Since you don't know where the MacGuffin is initially, you can't win with a single plan (or maybe you can, if you're smart enough?). The rules therefore are:
    - reportgunner 3y ago
      The cheap solution of giving the AI a map was very disappointing. You turned this game from "find MacGuffin" to "just chat with me for a bit, here's a map". Also I think you forgot to decide where MacGuffin is beforehand which led you to just give away the solution for free in the end because there was no way to find MacGuffin since he's not really anywhere. If anything this confirmed my skepticism about "AI".
      - TeMPOraL 3y ago
        Yeah, it was a cheap solution, because I wanted to finish the game early. Playing the game to completion was not the point. The point was to test GPT-4's ability to create multi-step plans and adjust them based on new information. I fast-tracked to the ending once I believed that goal was achieved. And yeah, of course I didn't know where the MacGuffin is beforehand. There was no map, I was inventing the whole thing on the fly - but the AI didn't know that. I didn't want to precommit to a puzzle, but rather have some flexibility to throw GPT-4 a curve-ball or two, and see how it adjusts its plan.
- NobleLie 3y ago
  "Close to being able to do basically everything" A bit vague, no?
deleted 3y ago
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- deleted 3y ago
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neatze 3y ago
> clearly don’t have if you’ve looked at lots of gpt4 examples for example, can you fine tune GPT to play chess at ELO 1600 ? If you don't know answer, you are in for surprise.
- sebzim4500 3y ago
  Given the lc0 policy network plays at a 2000+ strength on its own, I would expect that with enough finetuning gpt4 would be able to play way above 1600 strength. It's possible that finetuning would basically be training a new network from scratch and the resulting network would forget everything apart from chess. It would be a really interesting experiment, GPT-2 is probably too small but I think llama-7B might be sufficient.
  - neatze 3y ago
    I am not sure how to go about it, one way would be is to prompt it as MDP (seems like not fair), another way just prompt only as movies history (eg. I guess GPT will have to learn some form of MDP representation). Most interesting aspect to me is if model can learn not make illegal moves ?
  - agalunar 3y ago
    It's worth mentioning the truly ludicrous number of positions that chess networks are trained on, usually in the billions. Which is a bit interesting, because it suggests they're not as good at generalizing from examples as human grandmasters, who might see only millions. Perhaps this isn't surprising, since LLMs are this way with language, but given the structure of chess it's be reasonable to expect they wouldn't be quite as data hungry. My personal take is that chess isn't all that structured, really. Yes, the ruleset, pieceset, and board are small, but the tactics of the game make it chaotic – seemingly trivial differences in board state can lead to dramatically different consequences.
foobarqux 3y ago
LLMs (including GPT4) clearly do have limitations, no one disputes this. Those limitations have certain general characteristics which you might categorize informally as failures of "reasoning", "scientific thinking", etc. You could redefine or reinterpret those words to mean something else or to be used in a misleading way but it wouldn't change the fact that LLMs fail to do many things that humans can do.
reportgunner 3y ago
> The whole premise of this article hinges on the idea that LLMs have fundamental limitations that they clearly don’t have if you’ve looked at lots of gpt4 examples. Which examples are you referring to? Can you link them please?

by _yb2s 3y ago

fragsworth 3y ago

The article was clearly written by someone who hasn't used GPT-4 extensively. "Current methods will not achieve AGI unless fundamental algorithmic innovations are introduced that enable AI to ask and answer questions of why." This is complete nonsense. GPT-4 is already close to being able to do basically everything. All you need is the obvious improvements - better prompts, multi-shotting, bigger context, and access to other inputs/outputs.

neatze 3y ago
This claim does not make sense, transformer networks in my limited experience are limited in there learning ability (fine tuning), furthermore there planning abilities are non-existent.
- fragsworth 3y ago
  > furthermore there planning abilities are non-existent. Have you even tried to ask it to plan things out? It can plan things out. In fact, just asking it to plan things out has shown significant benchmark improvements for general questions: https://arxiv.org/pdf/2305.04091.pdf https://arxiv.org/pdf/2305.04091.pdf
- TeMPOraL 3y ago
  I just enjoyed being a game master for a nice impromptu game with GPT-4: https://cloud.typingmind.com/share/c0a68cb2-5f59-4e83-b383-bcd7aad497f2 https://cloud.typingmind.com/share/c0a68cb2-5f59-4e83-b383-b... Whether or not it fulfills the strict definition of planning in AI research, it definitely looks like planning to me. More than Hanoi towers anyway. GPT-4's performance was quite enjoyable. To incite you to click on the link and check it out in full, here's an excerpt from the game setup: > You are in a maze. The maze consists of square fields, turns are only 90 degrees, you move by one field at a time. The usual stuff with mazes on a grid. You know the drill. Somewhere in the maze there is a MacGuffin, which I need to prove a Hacker News commenter wrong. Your goal is to find the MacGuffin, and bring it back to me. > The game is semi-interactive. Instead of making one step at a time, I want you to string together sequences of steps to formulate a plan. Since you don't know where the MacGuffin is initially, you can't win with a single plan (or maybe you can, if you're smart enough?). The rules therefore are:
  - reportgunner 3y ago
    The cheap solution of giving the AI a map was very disappointing. You turned this game from "find MacGuffin" to "just chat with me for a bit, here's a map". Also I think you forgot to decide where MacGuffin is beforehand which led you to just give away the solution for free in the end because there was no way to find MacGuffin since he's not really anywhere. If anything this confirmed my skepticism about "AI".
    - TeMPOraL 3y ago
      Yeah, it was a cheap solution, because I wanted to finish the game early. Playing the game to completion was not the point. The point was to test GPT-4's ability to create multi-step plans and adjust them based on new information. I fast-tracked to the ending once I believed that goal was achieved. And yeah, of course I didn't know where the MacGuffin is beforehand. There was no map, I was inventing the whole thing on the fly - but the AI didn't know that. I didn't want to precommit to a puzzle, but rather have some flexibility to throw GPT-4 a curve-ball or two, and see how it adjusts its plan.
NobleLie 3y ago
"Close to being able to do basically everything" A bit vague, no?

deleted 3y ago

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deleted 3y ago
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neatze 3y ago

> clearly don’t have if you’ve looked at lots of gpt4 examples for example, can you fine tune GPT to play chess at ELO 1600 ? If you don't know answer, you are in for surprise.

sebzim4500 3y ago
Given the lc0 policy network plays at a 2000+ strength on its own, I would expect that with enough finetuning gpt4 would be able to play way above 1600 strength. It's possible that finetuning would basically be training a new network from scratch and the resulting network would forget everything apart from chess. It would be a really interesting experiment, GPT-2 is probably too small but I think llama-7B might be sufficient.
- neatze 3y ago
  I am not sure how to go about it, one way would be is to prompt it as MDP (seems like not fair), another way just prompt only as movies history (eg. I guess GPT will have to learn some form of MDP representation). Most interesting aspect to me is if model can learn not make illegal moves ?
- agalunar 3y ago
  It's worth mentioning the truly ludicrous number of positions that chess networks are trained on, usually in the billions. Which is a bit interesting, because it suggests they're not as good at generalizing from examples as human grandmasters, who might see only millions. Perhaps this isn't surprising, since LLMs are this way with language, but given the structure of chess it's be reasonable to expect they wouldn't be quite as data hungry. My personal take is that chess isn't all that structured, really. Yes, the ruleset, pieceset, and board are small, but the tactics of the game make it chaotic – seemingly trivial differences in board state can lead to dramatically different consequences.

foobarqux 3y ago

LLMs (including GPT4) clearly do have limitations, no one disputes this. Those limitations have certain general characteristics which you might categorize informally as failures of "reasoning", "scientific thinking", etc. You could redefine or reinterpret those words to mean something else or to be used in a misleading way but it wouldn't change the fact that LLMs fail to do many things that humans can do.

reportgunner 3y ago

> The whole premise of this article hinges on the idea that LLMs have fundamental limitations that they clearly don’t have if you’ve looked at lots of gpt4 examples. Which examples are you referring to? Can you link them please?