141–145 of 145 posts
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Bigger models hallucinate less. and we don't call it hallucinations but gofai mispredicts plenty.
> Bigger models hallucinate less. I'm skeptical. Based on what research?
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Anything other than clear definitions and unambiguous axioms (which happens to be most of the real world) and gofai falls apart. Like it can't even be done. There's a reason it was abandoned in NLP long before the likes of GPT. There aren't any class of problems deep nets can't handle. Will they always be the most efficient or best performing solution ? No, but it will be possible.
> There aren't any class of problems deep nets can't handle. Will they always be the most efficient or best performing solution ? No, but it will be possible. This assumes that all classes of problems reduce to functions which can be approximated, right, per the universal approximation theorems? Even for cases where the UAT applies (which is not everywhere, as I show next), your caveat understates the case. There are…
The grand goal of AI is a general learner that can at least tackle any kind of problem we care about. Are DNNs the best performing solution for every problem? No and I agree on that. But they are applicable to a far wider range of problems. There is no question what the better general learner paradigm is.
>* Or am I wrong? Is there a theoretical result I don't know about?
Thankfully, we don't need to get into theoreticals. Go ask GPT-4 to sort an arbitrary list of numbers. Change the length and try again.
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> _The_ (one) root reason? Ok, I’ll bite. A "secret" hiding in plain sight.
Does writing riddles help anyone? Maybe it helps you, maybe by giving you a smirk or dopamine hit? Think about others, please. What is obvious to you is not obvious to others. I recommend explaining and clarifying if you care about persuasion.
Pardon the cliffhanger style.
I have begun crafting an explanation, but not sure when it will be ready.
But when you recognize that thinking predates symbolic language, and start thinking about what thinking needs, you get closer to the answer.
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The language before language.
Care to expand on this, provide evidence, or even pointers to what you mean by it?
I have begun crafting an explanation, but not sure when it will be ready.
But when you recognize that thinking predates symbolic language, and start thinking about what thinking needs, you get closer to the answer.
I was born in late USSR and my father is software engineer. We had several books that were not available for "general public" (they were intended for libraries of science institutions). One of the book was, as I understand now, abridged translation of papers from some "Western" AI conference. And there were description if EURISCO (with claims that it not only "win some game" but also that it "invented new structure o…
I started my career in 1985, building expert systems on Symbolics Lisp machines in KEE and ART. Expert systems were so massively oversold... and it's not at all clear that any of the "super fantastic expert" systems ever did what was claimed of them. We definitely found out that they were, in practice, extremely difficult to build and make do anything reasonable. The original paper on Eurisko, for instance, mentioned…
I was one of the developers/knowledge engineers of the SpinPro™ Ultracentrifugation Expert System at Beckman Instruments, Inc. This was released in 1986, developed over about 2 years. This ran on an IBM PC (DOS)! This was a technical success, but not a commercial one. (The sales force was unfamiliar with promoting a software product, and which had little impact on their commissions vs. selling multi-thousand dollar equipment.) https://pubs.acs.org/doi/abs/10.1021/bk-1986-0306.ch023 (behind ACS paywall)
Our second Expert System was PepPro™, which designed procedures for the chemical synthesis of peptides (essentially very small proteins). This was completed and to be released in 1989, but Beckman discontinued their peptide synthesis instrument product line just two months before. This system was able to integrate end-user knowledge with the built-in domain knowledge. PepPro was recognized in the first AAAI Conference on Innovative Applications of Artificial Intelligence in 1989. https://www.aaai.org/Papers/IAAI/1989/IAAI89-010.pdf
Both of these were developed in Interlisp-D on Xerox 1108/1186 workstations, using an in-house expert system development environment, and deployed in Gold Hills Common Lisp for the PC.