4 ms·
Without a definition of "true understanding" this is meaningless. Saying they "can't reason logically" is also purely subjective - you out the benchmark much h
by vidarh 3y ago
Without a definition of "true understanding" this is meaningless.
Saying they "can't reason logically" is also purely subjective - you out the benchmark much higher than I do, given I've seen ChatGPT provide reasoning at a level many humans fail to reach on a regular basis. This does not mean it's good at it, but that humans reasoning ability is often rather poor.
I see nothing in this other than an argument from them not being good enough now coupled with unsupported implied speculation of what understanding and reasoning and intelligence implies that we have little evidence either for or against.
All in all this comes across as blind belief.
- riku_iki 3y ago> given I've seen ChatGPT provide reasoning at a level many humans fail to reach on a regular basis. and you don't know for sure if it was really reasoning or memorization and stochastic parroting. From another hand there are many research results demonstrating that neural networks have limitation in learning even something like multiplying numbers with many digits.
- vidarh 3y agoI don't know if humans are "really reasoning or memorization and stochastic parroting" either without a testable hypothesis of what makes something "really reasoning" as opposed to the other two. And most humans struggle with multiplying numbers with even quite few digits.
- riku_iki 3y ago> And most humans struggle with multiplying numbers with even quite few digits. most of the humans can learn the algorithm, and do many iterations reliably if you give pen and paper, current LLMs struggle with this. So, if we define reasoning as learning and executing algorithm of complexity N, than humans can learn more complex algorithms for higher N.
- vidarh 3y agoCurrent LLMs have gotten better at this with each iteration without the extensive rote repetition of applying the algorithms following by additional training on corrections that we subject people to. Yet, still, I think you overestimate the average humans ability to reliably execute even algorithms that simple. I've had LLMs do that correctly up to far larger numbers than I've multiplied by hand. I could do it, but I'm not confident I wouldn't get sloppy and make mistakes without going back and reviewing my work. I've also had LLMs execute algorithms most people wouldn't even understand. E.g. converting between NFAs and DFAs, or taking a grammar in an unspecified BNF variant abd generate grammatically valid sentences in it, or conversely validating a sentence against the grammar of a fictional language. I think a whole lot of the arguments here assume a level of human reasoning well above average. That's not to say current LLMs do not also have glaring blind spots, but so it seems to me does people dismissing their apparent reasoning out of hand because of assumptions about human abilities we mostly haven't tested.
- riku_iki 3y ago> I've had LLMs do that correctly up to far larger numbers than I've multiplied by hand. but given enough time you can multiply any numbers, while LLM has fundamental inability to learn algorithm. > I've also had LLMs execute algorithms most people wouldn't even understand. E.g. converting between NFAs and DFAs this is a not good quality discussion, such claims should be expressed in the form of some research results: describing what you do exactly, how data has been obtained, verified for correctness, what is complexity of your NFAs etc, otherwise I personally have hard time to judge how indicative your claim.
- speedgoose 3y agoChatGPT knows how to write Python code, run it, and use the results. It can also use WolframAlpha. It’s not a pure LLM solution but it’s good enough to multiply numbers. Have you tried it?
- vidarh 3y ago
- LabMechanic 3y agoOkay, I mentioned this in some of my past comments, but try this: Ask your "AI" something about a non-popular domain (e.g., Clifford algebra) or even ask it to derive something (e.g., the geometric product) given some axioms and definitions. Mathematics is precise as it gets, yet it failed to derive the geometric product given a set of axioms (e.g., distributive property) and definitions. Ask it to derive a theorem from perspective geometry. Or, try something else that is not so popular or widespread (something outside of math), I suspect, that it also does a poor job deriving things from principles (axioms, definitions, theorems).
- bhdlr 3y agoIs there a difference between memorization + stochastic parroting if it is indistinguishable from human understanding? If a hunk of metal looks like a car, sounds like a car, and drives like a car is it a car?
- riku_iki 3y agoit is indistinguishable on some trivial results, but not scalable for next level of complexity.
- LabMechanic 3y agoWho knows, but until it can fool me by randomness (like Nassim Taleb suggests), i.e., deriving something given a set of axioms and definitions (from a non-widespread domain), then I might join your "ignorance is bliss" argument. Also, hype is good for the stock market, so keep hyping it up? At the end, Elon Musk success seems to stem from hype and sensationalism. CNN (i.e., "breaking news" everywhere) has financial success due to sensationalism and hype, so hype is good, I guess?
- LabMechanic 3y agoYep, or somehow they make it apparent that they indeed "parrot" stuff they had in their large dataset. See my past comments for details. Fooled by randomness? (See Nassim Nicholas Taleb)