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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 out to be possible with fairly basic statistical text generation, because fooling humans is easy.
It would've been nice to reserve "AI" for superior human-like intelligence capable of genuine common sense and reasoning. The irony is that the startup founders most worried about "AI" have created so much hype and funding that we may very well figure out how to build "real" AI.
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?
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.
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. Think of Data from Star Trek TNG failing to understand figures of speech. Not that it's terribly relevant; humans regularly fall for tricks like "Paris in the the spring" or "where do you bury the survivors".
There are so many other sites. So many others. Why are you here?
[1] https://en.wikipedia.org/wiki/Artificial_intelligence
As I understand it, a major reason it's a consistent chorus is because people don't want the "AI is here" talk to drown out (and thus slow the arrival or distribution of) speech/text/popular-understanding about actual strong AGI.
To make an analogy, it could be like this:
Some people were expecting 100 tulips (because they were told tulips are available and can be ordered), and they ordered them. They received 100 daisies. And were saying "OMG, THE TULIPS ARE HERE! THE TULIPS ARE HERE!"
A nearby observer might have said, "You know, those are daisies. Not tulips."
And 95% of people might have said back, "WE GOT 100 TULIPS! SAYS SO RIGHT HERE! THEY ARE BEAUTIFUL! STOP BEING A NAY-SAYER! THESE ARE BEAUTIFUL TULIPS!"
The 5% could just to think to themselves, and could get chastised by the crowd, if they were to say say it out loud: "Well, those are not nearly as beautiful as tulips. And if you don't take it up with the seller, you may never receive the real tulips you were after. Since you think or at least act as though you've been sold them already."
If it's not "actual real AI", we can keep pretending that human intelligence is something distinct and special - and that what our computers are doing now is some sort of other, obviously fake and vastly inferior thing.
When Deep Blue won at chess, people didn't revise their estimates of AI capabilities upwards. They revised their estimates of how much intelligence is required to play chess at world level downwards, by a lot. Surely playing chess must have never required any intelligence in the first place!
Now, the list of things that "must have never required any intelligence in the first place" includes gems like "reading comprehension at high school level", "copywriting", "frontend work", "CTF tasks", "theory of mind", "arguing with people online" and more.
If the goalposts were moved far enough that the claim to "actual intelligence" is denied to a double digit percentage of human population, hasn't something gone wrong somewhere?
"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.
We've called that "AGI" since the late 90s/early 00s (depending on whether you count first use or popularization). Even if AGI does come to pass, we'll still need "AI" since not all forms of AI will be AGI.
Well, the labs are in a weird bind. They need to keep increasing autonomy so the agents can do increasingly complex, long-horizon tasks. But at the same time, they're closely guarding against autonomy in the sense of "pursuing its own goals."
Over the past year and a half especially, several labs have mentioned adding safeguards against self-replication, resistance to shutdown etc. (Notably, shortly after they all started bragging about involving them in the AI training loop itself, i.e. "self-improvement".)
My point here is that the autonomy of which you seek might be only a few small mutations away, but the labs are actively working to prevent such a mutation. I don't expect that situation to last for very long.
Not that I expect an AI lab will be overtaken by a rogue intelligence any time soon, but that as the cost of training goes down, I expect more "open minded" organizations and individuals to become involved.
It only takes one.
That's going to be the beginning of a new era of biology, and it's a little unsettling to think about.
What would a frontier API have to be able to do to satisfy you?
We are now calling text and image generators "intelligent" in the same way a spell checker is intelligent.
Whatever it's become, "AI" research started as a way to study digital neurology, or how to digitize a mind, not just how to generate data.
The Turing Test should have had a caveat, it needs to fool a, "non-stupid" person, and we still have not gotten even close to passing that version.
They've been "patched" since but all models fail basic tests like "Should I walk or drive to the car wash which is 100 feet away" by recommending you walk.
So you'd just ask questions that require theory of mind, abstract and common sense reasoning, causal inference, learning novel rules, transferring knowledge novel situations, recognizing ambiguity, etc.
I think we should consider slime mold intelligent, and realise that it's a spectrum. Path finding is AI. There are probably forms of intelligence we have yet to discover.
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.
you might say almost no humans can do tht either but some human can but no ai can.
Even Turing him self did envision the Turing test as something to pass as intelligence, but rather as a more useful replacement for the troubled term.
That said, I think your quest is doomed. There will never be a superior human-like intelligence. Forever is a long time, but my reasoning for believing this is the same reason Turing offered a replacement. Intelligence is way too vague to be useful as a measurement for anything. And if we ever discover something that is more intelligent them humans (by whichever definition of intelligence) we will simply redefine intelligence to exclude that.
I find references to LLMs fooling humans in "casual conversations" [1] but that's not how I think the original Turing test was conceived - or at least that's not all versions that existed.
At the same time, before even LLMs appeared, the exact meaning of the test was under intense debate. The "Loebner Prize" [2] being awarded to fairly simple chatbots made serious computer scientists very embarrassed.
[1] https://neurosciencenews.com/ai-passes-turing-test-30733/ [2] https://en.wikipedia.org/wiki/Loebner_Prize
They fucking solve original math problems that you can't solve. They are indisputably intelligent, and they are indisputably artificial. That makes them indisputably "artificial intelligence." Denying that (or downvoting it, for that matter) is up there with denying evolution and the Moon landings.
It's time to start flying a different flag. You're making humans look stupid.
It turned out to be possible with fairly basic statistical text generation, because fooling humans is easy.
Yes, fooling humans is easy. Yet somehow we still consider ourselves qualified to say what is "intelligent" and what isn't, even though we can't seem to define the term.
As I just mentioned at https://news.ycombinator.com/item?id=48981624, we need you to stick to HN's rules if you want to keep commenting on the site.
https://news.ycombinator.com/newsguidelines.html
> If a chatbot appears to be manipulative, mean, weird, or deceptive, what kind of answer do we want when we ask why? Revealing the indispensable antecedent examples from which the bot learned its behavior would provide an explanation: we’d learn that it drew on a particular work of fan fiction, say, or a soap opera. We could react to that output differently, and adjust the inputs of the model to improve it. Why shouldn’t that type of explanation always be available? There may be cases in which provenance shouldn’t be revealed, so as to give priority to privacy—but provenance will usually be more beneficial to individuals and society than an exclusive commitment to privacy would be.
Remember 'View Source'? And how bundling engines eventually made it irrelevant? What if every piece of content had a genuinely accurate and useful View Source?
1. "Every time we figure out a piece of it, it stops being called AI; it becomes just computation." - Ray Kurzweil
2. "Technology n. - Something that doesn't work yet." - Douglas Adams
I've had a recurring theme where I would name a project incorrectly, and then waste weeks or months on what turned out to be an unsolvable problem. When I figured out the actual correct name for a project, the whole thing would be solved within a few days.
Naming things correctly is hard, and the consequences of failing to do that can be pretty severe. To name something correctly, you have to understand what it is.
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But it's still in its present form very intelligent in meaningful and useful ways. And it is not too soon to talk about concerns of a potential existential threat in the future. Because it could sooner than we might realize, threaten our existence.
Because of the potential, we should have a culture of caution as we continue to rapidly improve AI.
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.
"The need to conform to digital designs has created an ambient expectation of human subservience. A positive spin on A.I. is that it might spell the end of this torture, if we use it well."
Maybe someone can enlighten me but I really don't understand how either of these description make any sense at all. How is it not better described as "the right to decide what data can be extracted"?
I'm curious: have we found those people or those new jobs yet? Is a forward deployed engineer an example of this, yet they are now doing the job of two people (sales and coding).
Chassez le collectiviste, il revient au galop.
aka
Once a collectivist, always a collectivist.
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.
But I've noticed that if you mention anything that could be seen as slightly critical of LLMs, you'll get people out of the woodwork suggesting that the state of the art has made your criticism invalid.
Of course it's gussied up as "mixture of agents" "reasoning traces" "agentic dispatching" but high-level it's Randomized Algorithms 101.