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Notes -
Zeno's AGI.
For a long time, people considered the Turing Test the gold standard for AI. Later, better benchmarks were developed, but for most laypeople with a passing familiarity with AI, the Turing Test meant something. And so it was a surprise that when LLMs flew past the Turing Test in 2022 or 2023, there weren't trumpets and parades. It just sort of happened, and people moved on.
I wonder if the same will happen with AGI. To quote hype-man Sam Altman:
Okay, actually he said that about Chat GPT 4.5, but you get the point. The last 6 months have seen monumental improvements in LLMs, with DeepSeek making them much more efficient and xAI proving that the scaling hypothesis still has room to run.
Given time, AI has been reliably able to beat any benchmarks that we throw at it (remember the Winograd schema?). I think if, 10 years ago, if someone said that AI could solve PHD level math problems, we'd say AGI had already arrived. But it hasn't. So what ungameable benchmarks remain?
AGI should lead to massive increases in GDP. We haven't seen productivity even budge upwards despite dumping trillions into AI. Will this change? When?
AI discoveries with minimal human intervention. If a genius-level human had the breadth of knowledge that LLMs do, they would no doubt make all sorts of novel connections. To date, no AI has done so.
What stands in the way?
It seems like context windows might be the answer. For example, what if we wanted to make novel discoveries by prompting an AI. We might prompt a chain-of-reasoning AI to try to draw connections between disparate fields and then stop when it finds something novel. But with current technology, it would fill up the context window almost immediately and then start to go off the rails.
We stand at a moment in history where AI advances at a remarkable pace and yet is only marginally useful, basically just a better Google/Stack Overflow. It is as smart as a genius-level human, far more knowledgable, and yet also remarkably stupid in unpredictable ways.
Are we just one more advance away from AGI? It's starting to feel like it. But I also wouldn't be surprised if life in 2030 is much the same as it is in 2025.
I’ve never understood how the Turing test measured anything useful. The test doesn’t even require that the AI agent understand anything about its world or even the questions being asked of it. It just has to do well enough to convince a human that it can do so. That’s the entire point of the Chinese room rejoinder— an agent might well be clever enough to fool a person into thinking it understands just by giving reasonable no answers to questions posed.
The real test, to me, is more of a practical thing — can I drop the AI in a novel situation and expect it to figure out how to solve the problems. Can I take a bot trained entirely on being an English chatbot and expect it to learn Japanese just by interacting with Japanese users? Can I take a chatbot like that and expect it to learn to solve physics equations? That seems a much better test because intelligent agents are capable of learning new things.
I am flabbergasted by people, including the person who came up with the Chinese Room thought experiment, Searle, not seeing what seems to me to be the obvious conclusion:
The room speaks Chinese.
(Is that a problem? No, not at all. I just didn't think you'd be Chinese)
No individual component of the room speaks Chinese, including the human, but that is no impediment. No single neuron in your brain speaks English, but we have zero qualms about saying the entire network, i.e your brain, does.
You're just a Functionalist, exactly the sort of people the argument is supposed to criticize. Or you're missing the point.
Searles is a Biological Realist, which is to say that he believes that processes of mind are real things that emerge from the biochemical processes of human beings and that language (and symbol manipulation in general) is a reflection of those processes, not the process in itself. He thinks thoughts are real things that exist outside of language.
To wit, he argues that what the room is missing is "intentionality". It does not have the ability do to anything but react to input in ways that are predetermined by the design of the chinese manual, and insofar as any of its components are concerned (or the totality thereof) they are incapable of reflecting upon the ideas being manipulated.
Your brain does "speak chinese" properly speaking because it is able to communicate intentional thoughts using that medium. The mere ability to hold conversation does not qualify to what Searles is trying to delineate.
Not too familiar with Searle's argument, but isn't this just saying that the lack of ability to generalize out of distribution is the issue? But I don't get how being able to react to novel inputs (in a useful way) would even help things much. Suppose one did come up with a finite set of rules that allowed one to output Chinese to arbitrary inputs in highly intelligent, coherent ways. It's still, AFAICT, still just a room with a guy inside to Searle.
Perhaps it's the ability to learn. But even then, you could have the guy follow some RL algorithm to update the symbols in the translation lookup algorithm book, and it's still just a guy in the room (to Searle).
It's not even clear to me how one could resolve this: at some point, a guy in the room could be manipulating symbols in a way that mirrors Xi Jinpeng's neural activations arbitrarily closely (with a big enough room and a long enough time), and Searle and I would immediately come to completely confident and opposite conclusions about the nature of the room. It just seems flatly ridiculous to me that the presence of dopamine and glutamate impart consciousness to a system, but I don't get how to argue against that (or even get how Searle would say that's different from his actual argument).
Insofar as this is possible, (I believe Searle disagrees that it is), then the room does speak Chinese because it's just a brain.
But as I explain in the other thread, this means assuming computationalism is true, which renders the whole though experiment moot since it's supposed to be a criticism of computationalism.
I'm not sure how one would argue that it's not possible. Is the contention that there's something ineffable happening in neurons that fundamentally can't be copied via a larger model? That seems isomorphic to a "god of the gaps" argument to me.
"God of the gaps" cuts both ways. The cached Materialist narrative has some very large holes in it that are bridged through unexamined axioms and predictions that never update when falsified.
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It's quite simple in a world where the experience of consciousness is unexplained and machines don't offer the same sort of spontaneous behavior as humans, actually.
It's not compatible with strict materialism, but then again most people don't believe in strict materialism.
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