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How much of AI's recent success is due to the Forer Effect?

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Re: How much of AI's recent success is due to the Forer Effect?

#81

Post by knewter »

giantrobot wrote: post #76
pixl97 wrote: post #55

Earlier quoted context omitted.

Eh, part of the problem is people don't currently understand what LLMs are doing... Have you ever had a dumb/wrong thought in your head? I'm going to go ahead and answer yes for you, you do all the time. In fact you don't (hopefully) verbalize a stream of consciousness to other people around you. In general you think of something then reflect on what it is true/false. This is not what LLMs do, they pitch back the fir…

Even if the error rate of LLMs decreases with additional GPU power there's little rhyme or reason to their confabulations. Even if only 1% of the code is in error there's no guidance or pattern to where those errors might be. It's like reviewing an overconfident junior developer's code except you can't learn their particular weaknesses. If a developer is bad about memory leaks, you know to check their every PR for me…

Yes but read the tree of thoughts paper

Re: How much of AI's recent success is due to the Forer Effect?

#82

Post by renyujia »

knewter wrote: post #81
giantrobot wrote: post #76

Earlier quoted context omitted.

Even if the error rate of LLMs decreases with additional GPU power there's little rhyme or reason to their confabulations. Even if only 1% of the code is in error there's no guidance or pattern to where those errors might be. It's like reviewing an overconfident junior developer's code except you can't learn their particular weaknesses. If a developer is bad about memory leaks, you know to check their every PR for me…

Yes but read the tree of thoughts paper

BTW here is the link to the Tree of Thoughts paper "Large Language Model Guided Tree-of-Thought":

https://arxiv.org/pdf/2305.08291.pdf

https://github.com/jieyilong/tree-of-thought-puzzle-solver

In this paper, we introduce the Tree-of-Thought (ToT) framework, a novel approach aimed at improving the problem-solving capabilities of auto-regressive large language models (LLMs). The ToT technique is inspired by the human mind’s approach for solving complex reasoning tasks through trial and error. In this process, the human mind explores the solution space through a tree-like thought process, allowing for backtracking when necessary. To implement ToT as a software system, we augment an LLM with additional modules including a prompter agent, a checker module, a memory module, and a ToT controller. In order to solve a given problem, these modules engage in a multi-round conversation with the LLM. The memory module records the conversation and state history of the problem solving process, which allows the system to backtrack to the previous steps of the thought-process and explore other directions from there. To verify the effectiveness of the proposed technique, we implemented a ToT-based solver for the Sudoku Puzzle. Experimental results show that the ToT framework can significantly increase the success rate of Sudoku puzzle solving.

Re: How much of AI's recent success is due to the Forer Effect?

#83

Post by autokad »

isaacg wrote: post #45
autokad wrote: post #32

I remember I was working on a game where you fly a ship and shoot alien ships. I didnt have a lot of experience at the time so I just made the ships move in one direction and eventually take a random turn, but if you got within a distance they moved away from you, but if you got too close they would kamikaze toward you. but I needed to figure out when to shoot, so I just decided that they would randomly shoot when yo…

By the way, the word is "beeline", not "b-line". It originally was a reference to bees traveling back to their hive. Sorry to be pedantic.

did you say 'Sorry to bee pedantic'?

hyuk hyuk hyuk

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