4 ms·

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, i

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.

mdp2021 2y 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_T 2y 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?
  - mdp2021 2y 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").
    - A_D_E_P_T 2y ago
      Here's the issue: Prediction isn't only about performing experiments in science, or engineering tasks. It's an ongoing process and something that may very well be tied to our very existence as conscious observers, in that it extends our spatiotemporal sense. Forget Einstein for a minute. When you drive a car, you hold a mental model of your position and velocity in time and space, of the expected behaviors of other drivers, of the conditions of the road, and you continually adjust your behavior in accordance with that model. Almost anything that requires attention is something that requires us to build a mental model of the future -- and predict that future. So, yeah, you can hew closely to validated scientific theories and "predict" how things will happen in that sense. But, as you walk home from your meeting at the astronomical society, you stop at a crosswalk, look both ways, and you're back to making essentially probabilistic predictions about how crossing the road is going to go. I get the sense that you dislike them, but really LLMs are not so different. How they handle probability and prediction is different in degree, but I don't think that it's entirely different in kind. > 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"). You've never memorized your multiplication tables? Boss Terry Tao has a reasonably high opinion of the abilities of LLMs as mathematicians, which is remarkable -- really astounding -- considering how they're built and trained, as essentially language prediction and manipulation machines.

mdp2021 2y 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_T 2y 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?
- mdp2021 2y 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").
  - A_D_E_P_T 2y ago
    Here's the issue: Prediction isn't only about performing experiments in science, or engineering tasks. It's an ongoing process and something that may very well be tied to our very existence as conscious observers, in that it extends our spatiotemporal sense. Forget Einstein for a minute. When you drive a car, you hold a mental model of your position and velocity in time and space, of the expected behaviors of other drivers, of the conditions of the road, and you continually adjust your behavior in accordance with that model. Almost anything that requires attention is something that requires us to build a mental model of the future -- and predict that future. So, yeah, you can hew closely to validated scientific theories and "predict" how things will happen in that sense. But, as you walk home from your meeting at the astronomical society, you stop at a crosswalk, look both ways, and you're back to making essentially probabilistic predictions about how crossing the road is going to go. I get the sense that you dislike them, but really LLMs are not so different. How they handle probability and prediction is different in degree, but I don't think that it's entirely different in kind. > 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"). You've never memorized your multiplication tables? Boss Terry Tao has a reasonably high opinion of the abilities of LLMs as mathematicians, which is remarkable -- really astounding -- considering how they're built and trained, as essentially language prediction and manipulation machines.