Comment by beders
7 hours ago
Thank you for putting it so succinctly.
I keep explaining to my peers, friends and family that what actually is happening inside an LLM has nothing to do with conscience or agency and that the term AI is just completely overloaded right now.
> I keep explaining to my peers, friends and family that what actually is happening inside an LLM has nothing to do with conscience or agency
What would the insides have to look like to have anything to do with conscience or agency?
One thing that has happened is that "AI" has been an academic discipline since literally the 1950s. The term was originally used in the hope that we would soon be able to emulate human minds. This turned out to be hard, but the name stuck to the discipline.
Now, suddenly, this name has been broadcast to every human in the world more or less. To them, it's a new term, and it obviously means something human mind-like. But to people who work on AI, that's not generally what it means. (Which isn't to say that some of them don't think we're near to achieving that; they just use other terms like "AGI" for that goal). So the name, which has a long history, is deceptive to people who aren't familiar with computer science.
AI is exactly the right term: the machines can do "intelligence", and they do so artificially.
Just like we have machines that can do "math", and they do so artificially.
Or "logic", and they do so artificially.
I assume we'll drop the "artificial" part in my lifetime, since there's nothing truly artificial about it (just like math and logic), since it's really just mechanical.
No one cares that transistors can do math or logic, and it shouldn't bother people that transistors can predict next tokens either.
> AI is exactly the right term: the machines can do "intelligence", and they do so artificially.
AI in pop culture doesn't mean that at all. Most people impression to AI pre-LLM craze was some form of media based on Asmiov laws of robotics. Now, that LLMs have taken over the world, they can define AI as anything they want.
In 2018, ie “pre-LLM”, the label “AI” was already stamped to everything, so I highly doubt that most people thought that their washing machines are sentient in any way. I remember this starkly, because my team was responsible at Ericsson (that time, about 120k employees) for one of the crucial step to have models in production, and basically every single project wanted that stamp.
The shift in meaning has been slowly diluted more and more across decades.
> Most people impression to AI pre-LLM craze was some form of media based on Asmiov laws of robotics.
I'll reveal you a secret: "positronic brains" are just very fast parallel computers running LLMs.
> what actually is happening inside an LLM has nothing to do with conscience or agency
What makes you think natural brains are doing something so different from LLMs?
Structurally a transformer model is so unrelated to the shape of the brain there's no reason to think they'd have many similarities. It's also pretty well established that the brain doesn't do anything resembling wholesale SGD (which to spell it is evidence that it doesn't learn in the same way).
>Structurally a transformer model is so unrelated to the shape of the brain there's no reason to think they'd have many similarities.
Substrate dissimilarities will mask computational similarities. Attention surfaces affinities between nearby tokens; dendrites strengthen and weaken connections to surrounding neurons according to correlations in firing rates. Not all that dissimilar.
Sure the implementation details are different.
I suppose I should have asked by what definition of "consciousness and agency" are today's LLMs (with proper tooling) not meeting?
And if today's models aren't meeting your standard, what makes you think that future LLMs won't get there?
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If platonic representation hypothesis holds across substrates, then it might matter very little, in the end. It holds across architectures in ML, empirically.
The crowd of "backpropagation and Hebbian learning + predictive coding are two facets of the very same gradient descent" also has a surprisingly good track record so far.
For starters, natural brains have the innate ability to differentiate between things that it knows and things that it have no possibility of knowing...
https://personal.utdallas.edu/~otoole/CGS2301_S09/7_split_br...
See page 53. While it is absolutely more prevelant in LLMs, human brains can also want a story for why their brains do things they are't plugged into.
Lol. Are you sure about that or you just made it up?
Modern LLMs are fairly good at that as well.
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Any amount of reading into how we understand brains and LLMs to work.