Isn't that exactly how humans learn to respond to stimuli? Don't we just try to predict the best next response to everything?
Yes, It's statistics but the fun part is nobody is writing this statistical function by hand.
I think LLMs ultimately just take imitation to a creative and sophisticated extreme. And imitation simply doesn't comprise the whole of human intelligence at all, no matter how much it is scaled up.
The sophistication of the imitation has some people confused and questioning whether everything can be reduced to imitation. It can't.
The ability to imitate seeking a goal isn't identical to the ability to seek a goal.
The ability to imitate solving a problem isn't identical to the ability to solve a problem.
Imitation is very useful, and the reduction of everything to imitation is an intriguing possibility to consider, but it's ultimately just wrong.
There are levels of sophistication in "imitation". It follows a gradient. At the low end of this gradient is a bad imitation.
At the high end of this gradient is a perfect imitation. Completely indistinguishable from what it's imitating.
If an imitation is perfect than is it really an imitation?
If I progressively make my imitation more and more accurate am I progressively building an imitation or am I progressively building the real thing?
See what's going on here? You fell for a play on words. It's a common trope. Sometimes language and vocabulary actually tricks the brain into thinking in a certain direction. This word "imitation" is clouding your thoughts.
Think about it. A half built house can easily be called an imitation of a real house.
I struggle to think of one, maybe someone on HN has a good example.
Eg if I'm in middle school and learning quadratic equations, am I imitating solving the problem by plugging in the coefficients? Or am I understanding it?
Most of what I see coming out of chatGPT and copilot could be said to be either. If you're generous, it's understanding. If not, it's imitation.
When an LLM can create math to solve a problem, we will be much closer to AGI.
Seems like goalpost moving to me.
I think the real things that separate LLMs from humans at the moment are:
* Humans can do online learning. They have long term memory. I guess you could equate evolution to the training phase of AI but it still seems like they don't have quite the same on-line learning capabilities as us. This is what probably prevents them from doing things like inventing maths.
* They seem to be incapable of saying "I don't know". Ok to be fair lots of humans struggle with this! I'm sure this will be solved fairly soon though.
* They don't have a survival instinct that drives proactive action. Sure you can tell them what to do but that doesn't seem quite the same.
LLM’s actually admit to being wrong easily, but aren’t great at introspection and confabulate too often. also their Meta cognition is poor still.
Also ChatGPT is trained specifically to be helpful and subservient.
Did a wise arbiter of truth set up goalposts that I moved? I guess I didn't get the memo.
If the implied claim is "GPT would invent math too given enough time", go ahead and make that claim.
Collectively, yes. The criticism of AI has always been "well it isn't AI because it can't do [thing just beyond its abilities].
Maybe individually your goalpost hasn't moved, and as soon as it invents some maths you'll say "yep, it's intelligent" (though I strongly doubt it). But collectively the naysayers in general will find another reason why it's not really intelligent. Not like us.
It's very tedious.
The issue with most criticism of LLMs wrt AGI is that they come up with totally bogus reasons why it isn't and can't ever be real intelligence.
It's just predicting the next word. It's a stochastic parrot. It's only repeating stuff it has been trained on. It doesn't have quantum microtubules. It can't really reason. It has some failure modes that humans don't. It can't do <some difficult task that most humans can't do>.
Seems to be mostly people feeling threatened. Very tedious.
The fact that we have trained it on examples of human-produced maths texts (rather than through interacting with the world over several millennia) seems like more of an implementation detail and not piece of evidence about whether it has “understood” or not.
At temperature 0 they are effectively producing the token that maximizes a weighted sum of base LM probability and model reward.
But humans are able to build certain routines within their own system to help them to rationalize.
Maybe it is, maybe it isn't. Maybe we are "just" an incredibly powerful prediction engine. Or maybe we work from a completely different modus operandi, and our ability to predict things is an emergent capability of it.
The thing is, no one actually knows what makes us intelligent, or even how to define intelligence for that matter.