61–70 of 164 posts
Earlier quoted context omitted.
We could've slapped a chat interface on calculators and called them "AI" because they can do superhuman math instantly. Most technical people would've thought that was stupid. LLMs are the same kind of category mistake.
Funny enough, calculators went through this exact same thing when they came out. "If the calculator can do math for the students, will they still learn?"
Earlier quoted context omitted.
Funny enough, calculators went through this exact same thing when they came out. "If the calculator can do math for the students, will they still learn?"
We didn't have confused people claiming calculators were human-like intelligences doing math.
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.
That's a bad and tired example as it confuses people just as much as it does (or rather, did?) LLMs.
The thing there though is that, if a human were given time to think about it, they'd probably go "hang on a minute", and with the LLMs that didn't seem to happen. They just kept confidently reasoning down the absurd path.
That reminds me, I recently had an AI write a ton of tests proving the "correctness" of a feature it had implemented completely backwards. (I noted that if I had been using a language that required formal proofs, that wouldn't have helped either: it would have just provided a formal proof for the absurd implementation!)
Earlier quoted context omitted.
That's a bad and tired example as it confuses people just as much as it does (or rather, did?) LLMs.
It's one example that points out a major (possibly fundamental) flaw. I can point to prompt injection as another example. There are tons more if you're interested. Are you actually claiming LLMs operate based on human-like intelligence?
Humans keep overestimating just how high the bar of "human-like intelligence" is.
Earlier quoted context omitted.
Can you give me one example that works on Claude right now? I'm never sure whether this indicates "no reasoning present" or you've just hit an odd behaviour in the AI such that its reasoning fails. For example, you present a problem in a way that's dissimilar to the way problems are presented in its training set. That doesn't mean it's not reasoning, just it can only reason correctly in some circumstances.
The models are continually patched with training and post-training. All you have to do is find an area they haven't patched yet, and they'll be just as stupid. I run into deep technical examples every day where they fail in the most basic ways no human ever would. I'm pretty sure most people building these models would admit they don't operate as human-like intelligences? It's baffling that anyone thinks they are.
But that doesn’t mean they don’t reason.
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…
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 question that is easily answered. Nothing I wrote suggested they're not incredibly useful.
> Data from Star Trek TNG failing to understand figures of speech.
These are just little instances of bad writing. Data is very much an attempt at displaying a human-like intelligence.
Earlier quoted context omitted.
It will only become an existential threat (in my humble opinion) when they can run influenced inference locally offline almost instantaneously. Then we need to be concerned about not being able to switch it off.
What do you mean "influenced inference" almost instantaneously? My laptop from 6 years ago can run an agent in a very fast loop in a web browser. It's not going to take over anything though since it's Gemma 4 E2B with only 2 billion parameters.
Your local model doesn't need to take anything over if for an extreme example it was just given an infrastructure system full access, say electricity grid, it wont have the context to create redundant copies of itself but it could easily decide humans don't need electricity anymore.
Also I'm not sure your model will have the context to know "it's time to reinfer" especiallynot "on the fly". My phrasing could be better but I'm talking about more powerful models.
Earlier quoted context omitted.
It's one example that points out a major (possibly fundamental) flaw. I can point to prompt injection as another example. There are tons more if you're interested. Are you actually claiming LLMs operate based on human-like intelligence?
We're on Hacker News. Do I really have to point out the existence of social engineering to you? Or that scamming old people out of their life savings is a profitable enough activity that there are entire call centers dedicated to the task? Humans keep overestimating just how high the bar of "human-like intelligence" is.
You could have humans calculate 2+2 all day and get a surprisingly high error rate. That reveals a flaw in how humans operate.
LLMs fail for entirely different reasons. Their mistakes don't imply they're human-like at all.
It's not about the error rate.
Earlier quoted context omitted.
The models are continually patched with training and post-training. All you have to do is find an area they haven't patched yet, and they'll be just as stupid. I run into deep technical examples every day where they fail in the most basic ways no human ever would. I'm pretty sure most people building these models would admit they don't operate as human-like intelligences? It's baffling that anyone thinks they are.
Yes I agree, they’re an alien kind of intelligence. But that doesn’t mean they don’t reason.
These LLM models/agents absolutely do not reason in the sense that humans do, so you're quietly redefining the word.
You can say of course decide to call them an "alien kind of intelligence" that "reasons" but you could just as reasonably say that calculators are an "alien" kind of intelligence that "reasons" about math differently than us.