Nah, at best we found a way to make one part of a collection of systems that will, together, do something like thinking. Thinking isn’t part of what this current approach does.
What’s most surprising about modern LLMs is that it turns out there is so much information statistically encoded in the structure of our writing that we can use only that structural information to build a fancy Plinko machine and not only will the output mimic recognizable grammar rules, but it will also sometimes seem to make actual sense, too—and the system doesn’t need to think or actually “understand” anything for us to, basically, usefully query that information that was always there in our corpus of literature, not in the plain meaning of the words, but in the structure of the writing.
This seems like the most viable path to me as well (educational background in neuroscience but don't work in the field). The brain is composed of many specialised regions which are tuned for very specific tasks.
LLMs are amazing and they go some way towards mimicking the functionality provided by Broca's and Wernicke's areas, and parts of the cerebrum, in our wetware, however a full brain they do not make.
The work on robots mentioned elsewhere in the thread is a good way to develop cerebellum like capabilities (movement/motor control), and computer vision can mimic the lateral geniculate nucleus and other parts of the visual cortex.
In nature it takes all these parts working together to create a cohesive mind, and it's likely that an artificial brain would also need to be composed of multiple agents, instead of just trying to scale LLMs indefinitely.
[0] https://transformer-circuits.pub/2024/scaling-monosemanticit...
It doesn't matter if that's happening or not. That's the whole point of the Chinese room - if it can look like it's understanding, it's indistinguishable from actually understanding. This applies to humans too. I'd say most of our regular social communication is done in a habitual intuitive way without understanding what or why we're communicating. Especially the subtle information conveyed in body language, tone of voice, etc. That stuff's pretty automatic to the point that people have trouble controlling it if they try. People get into conflicts where neither person understands where they disagree but they have emotions telling them "other person is being bad". Maybe we have a second consciousness we can't experience and which truly understands what it's doing while our conscious mind just uses the results from that, but maybe we don't and it still works anyway.
Educators have figured this out. They don't test students' understanding of concepts, but rather their ability to apply or communicate them. You see this in school curricula with wording like "use concept X" rather than "understand concept X".
I agree that a hypothetical perfectly-functioning Chinese room is, tautologically, impossible to distinguish from a real person who speaks Chinese, but that’s a thought experiment, not something that can actually exist. There’ll remain places where the “behavior” breaks down in ways that would be surprising from a human who’s actually paying as much attention as they’d need to be to have been interacting the way they had been until things went wrong.
That, in fact, is exactly where the difference lies: the LLM is basically always not actually “paying attention” or “thinking” (those aren’t things it does) but giving automatic responses, so you see failures of a sort that a human might also exhibit when following a social script (yes, we do that, you’re right), but not in the same kind of apparently-highly-engaged context unless the person just had a stroke mid-conversation or something—because the LLM isn’t engaged, because being-engaged isn’t a thing it does. When it’s getting things right and seeming to be paying a lot of attention to the conversation, it’s not for the same reason people give that impression, and the mimicking of present-ness works until the rule book goes haywire and the ever-gibbering player-piano behind it is exposed.
That's an interesting angle. Though of course we're not surprised by human behavior because that's where our expectations of understanding come from. If we were used to dealing with perfectly-correctly-understanding super-intelligences, then normal humans would look like we don't understand much and our deliberate thinking might be no more accurate than the super-intelligence's absent-minded automatic responses. Thus we would conclude that humans are never really thinking or understanding anything.
I agree that default LLM output makes them look like they're thinking like a human more than they really are. I think mistakes are shocking more because our expectation of someone who talks confidently is that they're not constantly revealing themselves to be an obvious liar. But if you take away the social cues and just look at the factual claims they provide, they're not obviously not-understanding vs humans are-understanding.
But even more, maybe consciousness is an invention of our 'explaining self', maybe everything is automatic. I'm convinced this discussion is and will stay philosophical and will never get any conclusion.
When I read stuff like this it makes me wonder if people are actually using any of the LLMs...
Now, neural nets that have a copy of themselves, can look back at what nodes were hit, and change through time... then maybe we are getting somewhere
Because I had no idea how these were built until I read the paper, so couldn’t really tell what sort of tree they’re barking up. The failure-modes of LLMs and ways prompts affect output made a ton more sense after I updated my mental model with that information.
The Emperor has no clothes.
What do you mean by novel? Almost all sentences it is prompted on are brand new and it mostly responds sensibly. Surely there's some generalization going on.
But how do you know a magician that knows how to do card tricks isn't going to arrive at real magic? Shakes head.
