111–120 of 164 posts
Earlier quoted context omitted.
> Yeah it's a trick question, the human error rate for it was about 30% (higher depending on the country). Error rate doesn't prove anything. The nature of the errors is what matters.
To err is human, it also seems that to err is AI.
We're choosing to call LLMs (and the little "harness" programs that query them in loops and execute their output) "AI", even though it doesn't make much sense. I absolutely love this technology but these aren't autonomous intelligences. They're little programs executing Bash scripts from JSON output. Our ideas about AI were naive. We thought passing a basic Turing test would require human-like intelligence. It turned…
I don't think it is fair to call a GPT model "fairly basic statistical text generation" - a Markov Text Generator I'd agree can be called basic statistics, but they are not fooling any humans in a Turing test.
> It would've been nice to reserve "AI" for superior human-like intelligence capable of genuine common sense and reasoning.
No, AI would absolutely be apt for describing a computing reasoning like a child
We're choosing to call LLMs (and the little "harness" programs that query them in loops and execute their output) "AI", even though it doesn't make much sense. I absolutely love this technology but these aren't autonomous intelligences. They're little programs executing Bash scripts from JSON output. Our ideas about AI were naive. We thought passing a basic Turing test would require human-like intelligence. It turned…
What I'm seeing here, reading this thread, is that "intelligence" isn't a thing. "Thing" in terms of a quantifiable that you can measure with tools and reason about, reproducibly. Everyone's got some idea what it is, so you get lots of different angles, but no one has an Intelligence Ruler we can hold up to a text output and say, yep, this one's got an INT of 14. Seems to be the crux of the disagreement.
I'm leaning towards there being a divide between those who feel "intelligence" is entirely separate from "sentience" and those who feel that one implies the other.
We're choosing to call LLMs (and the little "harness" programs that query them in loops and execute their output) "AI", even though it doesn't make much sense. I absolutely love this technology but these aren't autonomous intelligences. They're little programs executing Bash scripts from JSON output. Our ideas about AI were naive. We thought passing a basic Turing test would require human-like intelligence. It turned…
We've chosen to call Deep Blue and Half-Life 1 NPCs "AI" too. It boggles my mind that this "b-b-but it's not actual real AI" whine is even a thing. Were people saying this living in the cave for the past 5 decades of AI research?
1. The accuracy of the label.
2. The likelihood the label will cause problematic misunderstandings.
When my rice-cooker logic is advertised as "AI", that's a stretch, sure... But it's extremely unlikely to cause an investment bubble seeking the Rice Cooker Economic Singularity, incur protests from the Rice Cooker Emancipation League, or lead to weird folks in their basement seeking divine wisdom from its vaporous whispers.
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You are the one claiming that "they fail in ways completely unlike humans" insistently. Now go cough up some proof. I'll wait.
This isn't even controversial. The proof is available to anyone who uses these systems: They hallucinate tool state, drift from the objective while seeming to comply, switch languages randomly (Cyrillic or Japanese characters in output), confuse tasks they've planned for completed ones, and of course follow prompt injections embedded in files or web pages.
(And in particular, switching languages on the fly is normal for people who speak more than one well, it's something you learn not to do for the sake of people less comfortable with the languages involved.)
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You can call your little doggy "AI" if it makes you happy. Or you can keep calling them stochastic parrots as they solve decades-old open problems. The real question is how useful they are, and the answer "not at all" increasingly requires flat-earth levels of denial. it tells you to walk instead of drive to the car wash, you're not talking about the "AI" science fiction authors were dreaming of They sort of are. Thi…
> Or you can keep calling them stochastic parrots as they solve decades-old open problems. I didn't use that phrase at all. But computers calculated digits of π to trillions of digits. With a chat interface for a Python math program would look like the most impressive math genius if you took it back a few decades. > The real question is how useful they are... That's not the "real question" but an entirely different q…
Oh, ok then. That does change things a bit. The impression I'm getting is that you were suggesting they're not. What's succinctly the thing you're objecting to?
Is it Anthropomorphization?
I mean, sure, but watch out : when defending on that axis, it's easy to slip into Anthropodenial, right? Frans de Waal (from the same science that invented "Don't Anthropomorphize" ) can tell you about it.
> With a chat interface for a Python math program would look like the most impressive math genius if you took it back a few decades.
Well, exactly. Whether any particular generation of AI or software is yes/no "Like A Human Being" is probably the least interesting question axis. It's all just anthropocentrism.
Is that the thing you're trying to lay your finger on?
I think the biggest pushback this article will get here is the date. Although all he's saying is basically, "It's a tool, not a silver bullet". But the article is 3 years old and people will note that the models have been updated since then.
The models have updated but the biggest change is providers leaning in to them being "stochastic parrots," aka probabilistic computing, and if p(good response) > 0.5 then running the algorithm over and over again improves accuracy. Of course it's gussied up as "mixture of agents" "reasoning traces" "agentic dispatching" but high-level it's Randomized Algorithms 101.
I think the biggest pushback this article will get here is the date. Although all he's saying is basically, "It's a tool, not a silver bullet". But the article is 3 years old and people will note that the models have been updated since then.
The models have updated but the biggest change is providers leaning in to them being "stochastic parrots," aka probabilistic computing, and if p(good response) > 0.5 then running the algorithm over and over again improves accuracy. Of course it's gussied up as "mixture of agents" "reasoning traces" "agentic dispatching" but high-level it's Randomized Algorithms 101.
Earlier quoted context omitted.
You are the one claiming that "they fail in ways completely unlike humans" insistently. Now go cough up some proof. I'll wait.
This isn't even controversial. The proof is available to anyone who uses these systems: They hallucinate tool state, drift from the objective while seeming to comply, switch languages randomly (Cyrillic or Japanese characters in output), confuse tasks they've planned for completed ones, and of course follow prompt injections embedded in files or web pages.
In fact just the other day I commented on it to my fiancee after I randomly switched to French because we were discussing a trip and I mentioned a French location and pronounced it in French, and suddenly I was in "French mode" entirely unintentionally and it took a sentence before I realised.
That you think this is unique to LLM's suggests you simply don't know the diversity of human thought as well as perhaps you think you do. That's fine - none of us have a very complete view of that.
Earlier quoted context omitted.
You can call your little doggy "AI" if it makes you happy. But when you call something "AI" and it tells you to walk instead of drive to the car wash, you're not talking about the "AI" science fiction authors were dreaming of.
You can call your little doggy "AI" if it makes you happy. Or you can keep calling them stochastic parrots as they solve decades-old open problems. The real question is how useful they are, and the answer "not at all" increasingly requires flat-earth levels of denial. it tells you to walk instead of drive to the car wash, you're not talking about the "AI" science fiction authors were dreaming of They sort of are. Thi…