121–130 of 258 posts
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> After all, earth could be understood as solar powered super computer, that took a couple of million years to produce humanity. This is similar to a line I've seen Elon Musk trot out on a few occassions. It's a product of a materialistic philosophy (that the universe is only matter).
Yes, and?
I'm in the other camp: I remember when we thought an AI capable of solving Go was astronomically impossible and yet here we are. This article reads just like the skeptic essays back then. AGI is absolutely possible with current technology - even if it's only capable of running for a single user per-server-farm. ASI on the other hand... https://en.m.wikipedia.org/wiki/Integrated_information_theor...
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I think we already knew this though. Because the Turing test was passed by Eliza in the 1960's. PARRY was even better and not even a decade later. For some reason people still talk about Chess performance as if Deep Blue didn't demonstrate this. Hell, here's even Feynman talking about many of the same things we're discussing today, but this was in the 80's https://www.youtube.com/watch?v=EKWGGDXe5MA
Ten years ago I was explaining to halls of appalled academic administrators that AI would be replacing them before a robot succeeds in sorting out their socks.
The article starts out talking about white supremacy and replacing women. This isn't a proof. This is a social sciences paper dressed up with numbers. Honestly - Computer Science has given us more clues about how the human mind might work than cognitive science ever did.
Basically the linked article argues like this: > That’s because cognition, or the ability to observe, learn and gain new insight, is incredibly hard to replicate through AI on the scale that it occurs in the human brain. (no other more substantial arguments were given) I'm also very skeptical on seeing AGI soon, but LLMs do solve problems that people thought were extremely difficult to solve ten years ago.
> but LLMs do solve problems that people thought were extremely difficult to solve ten years ago. Well for something to be G or I you need them to solve novel problems. These things have interested most of the Internet and I've yet to see a "reasoning" disentangle memorization from reasoning. Memorization doesn't mean they aren't useful (not sure why this was ever conflated since... Computers are useful...), but it's…
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> How can you not understand the difference between "humans are not absolutely perfect or reliable at this task" and "LLMs by their very nature cannot perform this task"? This is a very good distillation of one side of it. What LLMs have taught us is a superficial grasp of language is good enough to reproduce a shocking proportion of what society has come to view as intelligent behaviors. i.e. it seems quite plausibl…
I think we already knew this though. Because the Turing test was passed by Eliza in the 1960's. PARRY was even better and not even a decade later. For some reason people still talk about Chess performance as if Deep Blue didn't demonstrate this. Hell, here's even Feynman talking about many of the same things we're discussing today, but this was in the 80's https://www.youtube.com/watch?v=EKWGGDXe5MA
Whatever you think about AGI, this is a dumb paper. So many words and references to say - what. If you can't articulate your point in a few sentences you probably don't have a point. There are all kinds of assumptions being made in the study about how AI systems work, about what people "mean" then they talk about AGI etc. The article starts out talking about white supremacy and replacing women. This isn't a proof. Th…
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> but LLMs do solve problems that people thought were extremely difficult to solve ten years ago Agreed. I would have laughed you out of the room 5 years ago if you told me AI's would be writing code or carrying on coherent discussions on pretty complex topics in 2024. As far as I'm concerned, all bets are off after the collective jaw drop that the entire software engineering industry did when we saw GPT4 released. W…
> It turns out that the larger these models get, the more unexpected emergent capabilities they have, so I'm mostly in the camp of thinking AGI is just a matter of time and resources. AI research has a long history of people saying this. Whenever there is a new fundamental improvement, it looks like you can just keep getting better results by throwing more resources at it. However, eventually we end up reaching a poi…
AGI is already here for most definitions of general.
Intelligence generally requires something more. Intelligence needs to be factual, curious, and self-improving. If you told me ChatGPT rewrote itself, or suggested new hardware to improve efficiency, that’s intelligence. You’ll know we have AGI when the algorithm is the one asking the questions about physics, mathematics, finding knowledge gaps, and developing original hypothesis and experiments. Not even close.