[nopinsight]

You are implying either:

* Understanding complex language does not require logic/reasoning,

* There are infinitely many forms of logic/reasoning or at least more than those existing in a vast training set.

Neither of which is likely true.

What do you think of the Minerva system, which can solve multi-step quantitative reasoning questions better than many competent students and most adults?

https://ai.googleblog.com/2022/06/minerva-solving-quantitati...

Note: If you look at LSAT test samples, many questions are tests of complex logical reasoning, a requisite for legal professions.

[fnordpiglet]

You nailed what I find discomforting about these discussions. They’re incredibly narrowly focused on a specific implementation that satisfies hitherto unsolved problems by pointing out its doesn’t do already solved problems. But surely folks realize the human brain isn’t a single monolithic processing program but an ensemble of specialized subsystems that organize to form the mind. Why would you assume you wouldn’t do the same with AI systems? We’ve been tackling reasoning, inference, problem solving, information retrieval, mathematics, logic, and other domains for decades with some stupendous results. But they lacked the ability to ingest and translate language into some intermediate semantic form and take output and reconstruct it into a human language. Likewise vision, and audio processing and input output has been a struggle until recently.

I also really strongly disagree that it’s basically doing some sort of information retrieval design where based on language it regurgitates some sort of markov expectations. You can ask it to do very complex translations of a concept from one domain to another and expressed in a form that’s certainly never been done before and it does it with alacrity. At the very minimum it “remembers” things from the past in the conversation and can associate the semantic ideas across prompts and synthesize cogent responses - that in itself implies it has some semantic “understanding” of the structure of the language. That is a huge missing piece in our tool kit to date.

Frankly I feel these threads expose just how jaded and unable to dream we have become, that even when a wonder walks up and hits you in the nose we can’t even see it.

[Aperocky]

Language prediction model is not a closely guarded secret, I suggest looking into academic papers about what they are and maybe even see/do some implementation yourself.

There are no magic, it is just a more complicated transpose, created by training over perhaps 10% of all available text on the internet.

It does have a lot of use, for one I think it would probably put grammarly out of business, and maybe even do some work for law firms.

[Aperocky]

> Understanding complex language does not require logic/reasoning

The key is understanding. It does not need to, it has already seen the question asked in a 100 different ways, it also seen the answer to all of those. It just rephrases those answer via a neural network and that happen to pass the bar test.

> There are infinitely many forms of logic/reasoning or at least more than those existing in a vast training set.

More importantly, differences between forms are subtle and cannot be understood, that's why ChatGPT confidently give wrong answers on stackoverflow: https://meta.stackoverflow.com/questions/421831/temporary-po...

[ss108]

The LSAT tests formal logic. Some of it is complicated. Much less of it is required for the practice of law.

Src: scored 99.8th percentile on LSAT, tutored it, now working at major law firm

[ss108]

Also just adding to my earlier reply (can't edit), none of it is "complex" relative to the complexity of some of the concepts in computer science or more brainy parts of complicated professional software development.