31–40 of 143 posts
This is like claiming a photorestor controlled night light "understands when it is dark" or that a bimetallic strip thermostat "understands temperature". You can say those words, and it's syntactically correct but entirely incorrect semantically.
Or like saying the photoreceptors in your retina understand when it's dark. Or like claiming the temperature sensitive ion channels in your peripheral nervous system understand how hot it is.
This is like claiming a photorestor controlled night light "understands when it is dark" or that a bimetallic strip thermostat "understands temperature". You can say those words, and it's syntactically correct but entirely incorrect semantically.
Or like saying the photoreceptors in your retina understand when it's dark. Or like claiming the temperature sensitive ion channels in your peripheral nervous system understand how hot it is.
When challenged, everybody becomes an eliminative materialist even if it's inconsistent with their other views. It's very weird.
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Sorry, this is more about the discussion of this article than the article itself. The moving goal posts that acolytes use to declare consciousness are becoming increasingly cult-y.
We spent 40 years moving the goal posts on what constitutes AI. Now we seem to have found an AI worthy of that title and instead start moving the goal posts on "consciousness", "understanding" and "intelligence".
I'd be really curious to see where the "attention" heads of the LLM look when evaluating the nullability of any given token. Does just it trust the Optional[int] return type signature of the function, or does it also skim through the function contents to understand whether that's correct?
It's fascinating to me to think that the senior developer skillset of being able to skim through complicated code, mentally make note of different tokens of interest where assumptions may need to be double-checked, and unravel that cascade of assumptions to track down a bug, is something that LLMs already excel at.
Sure, nullability is an example where static type checkers do well, and it makes the article a bit silly on its own... but there are all sorts of assumptions that aren't captured well by type systems. There's been a ton of focus on LLMs for code generation; I think that LLMs for debugging makes for a fascinating frontier.
Earlier quoted context omitted.
Sorry, this is more about the discussion of this article than the article itself. The moving goal posts that acolytes use to declare consciousness are becoming increasingly cult-y.
We spent 40 years moving the goal posts on what constitutes AI. Now we seem to have found an AI worthy of that title and instead start moving the goal posts on "consciousness", "understanding" and "intelligence".
Earlier quoted context omitted.
Sorry, this is more about the discussion of this article than the article itself. The moving goal posts that acolytes use to declare consciousness are becoming increasingly cult-y.
We spent 40 years moving the goal posts on what constitutes AI. Now we seem to have found an AI worthy of that title and instead start moving the goal posts on "consciousness", "understanding" and "intelligence".
We didn't "find" AI, we invented systems that some people want to call AI, and some people aren't convinced it meets the bar
It is entirely reasonable for people to realize we set the bar too low when it is a bar we invented
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Where is the boundary where this becomes semantically correct? It's easy for these kinds of discussions to go in circles, because nothing is well defined.
Hard to define something that science has yet to formally outline, and is largely still in the realm of religion.
I believe that the type of understanding demonstrated here doesn't. Consciousness only comes into play when we become aware that such understanding has taken place, not on the process itself.
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> Each word/token is chosen as that which is statistically most likely to follow the previous. The best way to predict the weather is to have a model which approximates the weather. The best way to predict the results of a physics simulation is to have a model which approximates the physical bodies in question. The best way to predict what word a human is going to write next is to have a model that approximates human…
LLMs don't approximate human thought , though. They approximate language . That's it. Please, I'm begging you, go read some papers and watch some videos about machine learning and how LLMs actually work. It is not "thinking." I fully realize neural networks can approximate human thought -- but we are not there yet, and when we do get there, it will be something that is not an LLM, because an LLM is not capable of tha…
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It's not age-old nor is it controversial. LLMs aren't intelligent by any stretch of the imagination. Each word/token is chosen as that which is statistically most likely to follow the previous. There is no capability for understanding in the design of an LLM. It's not a matter of opinion; this just isn't how an LLM works. Any comparison to the human brain is missing the point that an LLM only simulates one small part…
Many people don't think we have any good evidence that our brains aren't essentially the same thing: a stochastic statistical model that produces outputs based on inputs.
We’re all just elementary particles being clumped together in energy gradients, therefore my little computer project is sentient—this is getting absurd.