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0 pointsA_D_E_P_T1y ago0 comments

I don't think that anybody predicts "facts" -- there are no oracles, and if you predict a physical concept, it's very easy to get things wrong. Outcomes are, in some cases, almost statistical.

(A physical concept could be something as simple as how to catch a frisbee, or, alternatively, imagine a cat trying to predict how best to swipe at a fleeing mouse. If the mouse zigs when it could have zagged, the cat, for all its well-honed instincts, may miss. It may have predicted wrongly.)

Predicting tokens is really quite similar. I really think that it's the same type of thing.

Getting facts right is a matter of error correction and knowledgebase utilization, which is why "reasoning models" with error correction layers and RAG are so good.

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mdp20211y ago

> there are no oracles

If you mean "guessing without grounds", that is exactly the phenomenon which is expressed by bad thinkers in both the carbon and the silicon realms, and that is what we are countering.

> predict[ing] "facts"

It's called "Science". In a broader way, it's called "intelligence" ("Intelligence is being able to predict the outcomes of an experience you never had" ~~ Prof. Patrick Winston)

> Getting facts right is a matter of

It is a matter of procedurally adhering to an attitude of iterative quality refinement of ideas, and LLMs seem to be dramatically bad at "procedures".

A_D_E_P_TOP1y ago

When you say "predicting facts" you imply "predicting true future events." Delphi is no longer operational, so it simply can't be done. (At least, not past a certain -- very, very low -- complexity threshold in the macroscopic non-quantum world.)

"Science" is coming up with, and testing, theories -- they may be true, they may be false, and you can't know, and shouldn't hold a very strong position, until you test them. It's true that a more intelligent person will come up with better hypotheses and more inventive ways to put them to the test, but that's not what you seemed to be talking about, nor are we in any disagreement on that point.

A more intelligent cat will also catch mice more effectively -- it'll have a more accurate mental model of the mouse and of its own physical capabilities in time and space. Still, the outcome of the hunt is never perfectly predictable. Some outcomes are statistical -- and, intriguingly, LLMs mirror this in how they predict tokens.

> LLMs seem to be dramatically bad at "procedures".

How do you figure, and how did you reach this conclusion?

mdp20211y ago

> When you say "predicting facts" you imply "predicting true future events"

And Michelson and Morley did through Einstein's theory. And Jack did when he said "if my theory is correct, that falling brick will break my skull more probably than not". And it's a matter in which LLMs tend to fail, when they go "surely your operating system will have a `scratchmyback` command to allow you to work more hours sitting in front of it, it just makes sense".

> How do you figure [that «LLMs seem to be dramatically bad at "procedures"»], and how did you reach this conclusion?

I just tried with a main widespread engine, and it failed. And it showed that it still seemed to be guessing an output instead of actually checking to build the output (as if remembering that very often "2+2=4" instead of checking "1 and 1, and 1 and 1: 1, 2, 3, 4").

1 more reply

mdp20211y ago

I'll give you another example:

current Neural Network architectures seem to perform in a dreamlike state in which "oh in that area there should be a piece of finger this way oriented";

humans also have a wake state module in which they count them fingers.

These NNs seem to dream; we can be awake.

j / k navigate · click thread line to collapse

0 pointsA_D_E_P_T1y ago0 comments

I don't think that anybody predicts "facts" -- there are no oracles, and if you predict a physical concept, it's very easy to get things wrong. Outcomes are, in some cases, almost statistical.

Predicting tokens is really quite similar. I really think that it's the same type of thing.

Getting facts right is a matter of error correction and knowledgebase utilization, which is why "reasoning models" with error correction layers and RAG are so good.

0 comments

mdp20211y ago

> there are no oracles

If you mean "guessing without grounds", that is exactly the phenomenon which is expressed by bad thinkers in both the carbon and the silicon realms, and that is what we are countering.

> predict[ing] "facts"

It's called "Science". In a broader way, it's called "intelligence" ("Intelligence is being able to predict the outcomes of an experience you never had" ~~ Prof. Patrick Winston)

> Getting facts right is a matter of

It is a matter of procedurally adhering to an attitude of iterative quality refinement of ideas, and LLMs seem to be dramatically bad at "procedures".

A_D_E_P_TOP1y ago

> LLMs seem to be dramatically bad at "procedures".

How do you figure, and how did you reach this conclusion?

mdp20211y ago

> When you say "predicting facts" you imply "predicting true future events"

> How do you figure [that «LLMs seem to be dramatically bad at "procedures"»], and how did you reach this conclusion?

1 more reply

mdp20211y ago

I'll give you another example:

current Neural Network architectures seem to perform in a dreamlike state in which "oh in that area there should be a piece of finger this way oriented";

humans also have a wake state module in which they count them fingers.

These NNs seem to dream; we can be awake.

j / k navigate · click thread line to collapse