381–389 of 389 posts
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> But here we have real humans doing the exact same thing... I'd wager that a motivation in designing these systems it so they do not make these mistakes. Otherwise what's the point, really.
Our own, and other people's mistakes shape us, and our understanding. What even is perfect, anyway?
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>It fully ignores the potential for emergence. There's two kinds of emergence, one scientific, the other a strange, vacuous notion in the absence of any theory and explanation. The first case is emergence when we for example talk about how gas or liquid states, or combustibility emerge from certain chemical or physical properties of particles. It's not just that they're emergent, we can explain how they're emergent a…
> There's two kinds of emergence, one scientific I am not aware of any scientific kind of emergence. There's philosophical emergence, and its counterpoint - ontological reductionism. Most people have an intuitive sense that philosophical emergence is true, and that bubbles up in their writing, taken as an axiom that we're all supposed to go along with. On closer inspection, it is not clear to me that this isn't simpl…
> It’s as if a biologist were to say: “I have a great new theory of organisms. It lists many that exist and many that can’t possibly exist, and I can tell you nothing about the distinction.” > Again, we’d laugh. Or should. Should we? This reminds me acutely of imaginary numbers. They are a great theory of numbers that can list many numbers that do 'exist' and many that can't possibly 'exist'. And we did laugh when im…
Imaginary numbers are not relevant at all. There’s nothing whatsoever to do with the everyday use of the word imaginary. They could just as easily have been called “vertical numbers” and real numbers called “horizontal numbers” in order to more clearly illustrate their geometric interpretation in the complex plane. The term “imaginary number” was coined by Rene Descartes as a derogatory and the ill intent behind his…
In my view, there is a major flaw in his argument is his distinction into pure engineering and science: > We can make a rough distinction between pure engineering and science. There is no sharp boundary, but it’s a useful first approximation. Pure engineering seeks to produce a product that may be of some use. Science seeks understanding. If the topic is human intelligence, or cognitive capacities of other organisms,…
What you think about his argument about “not being able to distinguish possible language from impossible”?
And why is it inherent in ML design?
Does he assume that there could be such an instrument/algorithm that could do that with a certainty level higher than LLM/some ml model?
I mean, certainly they can be used to make a prediction/answer to this question, but he argues that this answer has no credibility? I mean, LLM is literally a model, ie probability distribution over what is language and what is not, what gives?
Current models are probably tuned more “strictly” to follow existing languages closely, ie that will say “no-no” to some yet-unknown language, but isn’t this improvable in theory?
Or is he arguing precisely that this “exterior” is not directly correlated with “internal processes and faculties” and cannot make such predictions in principle?
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LLMs cost significantly less than even a high schooler
Just because for now they are burning money and it's priced considerably under what it's costing them. Which is why I spoke of "cost" not of "price". They're in the "disrupt" phase. But that's not forever.
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I agree. But it’s worth being somewhat skeptical of ASI scenarios if you can’t, for example, give a well formulated math problem to a LLM and it cannot solve it. Until we get a Reimann hypothesis calculator (or equivalent for hard/old unsolved maths) it’s kind of silly to be debating the extreme ends of AI cognition theory
"I'm taking this talking dog right back to the pound. It completely whiffed on both Riemann and Goldbach. And you should see the buffer overflows in the C++ code it wrote for me."
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> AI this decade have not, at least so far, substantially deepened our understanding of our own intelligence and its constitution I would push back on this a little bit. While it has not helped us to understand our own intelligence, it has made me question whether such a thing even exists. Perhaps there are no simple and beautiful natural laws, like those that exists in Physics, that can explain how humans think and…
Neuroscientist here: > Perhaps there are no simple and beautiful natural laws, like those that exists in Physics, that can explain how humans think and make decisions...Perhaps it's all just emergent properties of some messy evolved substrate. Yeah, it is very likely that there are not laws that will do this, it's the substrate. The fruit fly brain (let alone human) has been mapped, and we've figured out that it's no…
These cases seem totally fascinating. Have you any links to examples or more information (i'm also curious about the curious detail of them tending to work in mail rooms)?
There was an interesting debate where Chomsky took a position on intelligence being rooted in symbolic reasoning and Asimov asserted a statistical foundation (ah, that was not intentional ;). LLM designs to date are purely statistical models. A pile, a morass of floating point numbers and their weighted relationships, along with the software and hardware that animates them and the user input and output that makes the…
I dunno if people knew it at that time, but those two views are completely equivalent.