For me as a lay-person, the article is disjointed and kinda hard to follow. It's fascinating that all the quotes are emotional responses or about academic politics. Even now, they are suspicious of transformers and are bitter that they were wrong. No one seems happy that their field of research has been on an astonishing rocketship of progress in the last decade.
dekhn · 1h ago
The way I see this is that for a long time there was an academic field that was working on parsing natural human language and it was influenced by some very smart people who had strong opinions. They focused mainly on symbolic approaches to parsing, rather than probabilistic. And there were some fairly strong assumptions about structure and meaning. Norvig wrote about this: https://norvig.com/chomsky.html and I think the article bears repeated, close reading.
Unfortunately, because ML models went brr some time ago (Norvig was at the leading edge of this when he worked on the early google search engine and had access to huge amounts of data), we've since seen that probabilistic approaches produce excellent results, surpassing everything in the NLP space in terms of producing real-world sysems, without addressing any of the issues that the NLP folks believe are key (see https://en.wikipedia.org/wiki/Stochastic_parrot and the referenced paper). Personally I would have preferred if the parrot paper hadn't also discussed environmental costs of LLMs, and focused entirely on the semantic issues associated with probabilistic models.
I think there's a huge amount of jealousy in the NLP space that probabilistic methods worked so well, so fast (with transformers being the key innovation that improved metrics). And it's clear that even state-of-the-art probabilistic models lack features that NLP people expected.
Repeatedly we have seen that probabilistic methods are the most effective way to make forward progress, provided you have enough data and good algorithms. It would be interesting to see the NLP folks try to come up with models that did anything near what a modern LLM can do.
mistrial9 · 1h ago
> most effective way to make forward progress
powerful response but.. "fit for what purposes" .. All of human writings are not functionally equivalent. This has been discussed at length. e.g. poetry versus factual reporting or summation..
If Chomsky was writing papers in 2020 his paper would’ve been “language is all you need.”
That is clearly not true and as the article points out wide scale very large forecasting models beat that hypothesis that you need an actual foundational structure for language in order to demonstrate intelligence when in fact is exactly the opposite.
I’ve never been convinced by that hypothesis if for no other reason that we can demonstrate in the real world that intelligence is possible without linguistic structure.
As we’re finding: solving the markov process iteratively is the foundation of intelligence
out of that process emerges novel state transition processes - in some cases that’s novel communication methods that have structured mapping to state encoding inside the actor
communications happen across species to various levels of fidelity but it is not the underlying mechanism of intelligence, it is an emerging behavior that allows for shared mental mapping and storage
aidenn0 · 2h ago
Some people will never be convinced that a machine demonstrates intelligence. This is because for a lot of people, intelligence exists a subjective experience that they have and the belief that others have it too is only inasmuch as others appear to be like the self.
shmel · 3m ago
How do they convince themselves that other people have intelligence too?
meroes · 1h ago
It doesn’t mean they tie intelligence to subjective experience. Take digestion. Can a computer simulate digestion, yes. But no computer can “digest” if it’s just silicon in the corner of an office. There are two hurdles. The leap from simulating intelligence to intelligence, and the leap from intelligence to subjective experience. If the computer gets attached to a mechanism that physically breaks down organic material, that’s the first leap. If the computer gains a first person experience of that process, that’s the second.
You can’t just short-circuit from simulates to does to has subjective experience.
And the claim other humans don’t have subjective experience is such non-starter.
aidenn0 · 31m ago
I think you're talking about consciousness rather than intelligence. While I do see people regularly distinguishing between simulation and reality for consciousness, I don't often see people make that distinction for intelligence.
> And the claim other humans don’t have subjective experience is such non-starter.
What about other primates? Other mammals? The smarter species of cephalopods?
Certain many psychopaths seem to act as if they have this belief.
dekhn · 1h ago
This is why I want the field to go straight to building indistinguishable agents- specifically, you should be able to video chat with an avatar that is impossible to tell from a human.
Then we can ask "if this is indistinguishable from a human, how can you be sure that anybody is intelligent?"
Personally I suspect we can make zombies that appear indistinguishable from humans (limited to video chat; making a robot that appears human to a doctor would be hard) but that don't have self-consciousness or any subjective experience.
6stringmerc · 1h ago
It is until proven otherwise because modern science still doesn’t have a consensus or standards or biological tests which can account for it. As in, highly “intelligent” people often lack “common sense” or fall prey to con artists. It’s pompous as shit to assert a black box mimicry constitutes intelligence. Wake me up when it can learn to play a guitar and write something as good as Bob Dylan and Tom Petty. Hint: we’ll both be dead before that happens.
aidenn0 · 43m ago
I can't write something as good as Bob Dylan and Tom Petty. Ergo I'm not intelligent.
languagehacker · 2h ago
Great seeing Ray Mooney (who I took a graduate class with) and Emily Bender (a colleague of many at the UT Linguistics Dept., and a regular visitor) sharing their honest reservations with AI and LLMs.
I try to stay as far away from this stuff as possible because when the bottom falls out, it's going to have devastating effects for everyone involved. As a former computational linguist and someone who built similar tools at reasonable scale for largeish social media organizations in the teens, I learned the hard way not to trust the efficacy of these models or their ability to get the sort of reliability that a naive user would expect from them in practical application.
Legend2440 · 51m ago
They are far far more capable than anything your fellow computational linguists have come up with.
As the saying goes, 'every time I fire a linguist, the performance of the speech recognizer goes up'
philomath_mn · 2h ago
Curious what you are expecting when you say "bottom falls out". Are you expecting significant failures of large-scale systems? Or more a point where people recognize some flaw that you see in LLMs?
Unfortunately, because ML models went brr some time ago (Norvig was at the leading edge of this when he worked on the early google search engine and had access to huge amounts of data), we've since seen that probabilistic approaches produce excellent results, surpassing everything in the NLP space in terms of producing real-world sysems, without addressing any of the issues that the NLP folks believe are key (see https://en.wikipedia.org/wiki/Stochastic_parrot and the referenced paper). Personally I would have preferred if the parrot paper hadn't also discussed environmental costs of LLMs, and focused entirely on the semantic issues associated with probabilistic models.
I think there's a huge amount of jealousy in the NLP space that probabilistic methods worked so well, so fast (with transformers being the key innovation that improved metrics). And it's clear that even state-of-the-art probabilistic models lack features that NLP people expected.
Repeatedly we have seen that probabilistic methods are the most effective way to make forward progress, provided you have enough data and good algorithms. It would be interesting to see the NLP folks try to come up with models that did anything near what a modern LLM can do.
powerful response but.. "fit for what purposes" .. All of human writings are not functionally equivalent. This has been discussed at length. e.g. poetry versus factual reporting or summation..
That is clearly not true and as the article points out wide scale very large forecasting models beat that hypothesis that you need an actual foundational structure for language in order to demonstrate intelligence when in fact is exactly the opposite.
I’ve never been convinced by that hypothesis if for no other reason that we can demonstrate in the real world that intelligence is possible without linguistic structure.
As we’re finding: solving the markov process iteratively is the foundation of intelligence
out of that process emerges novel state transition processes - in some cases that’s novel communication methods that have structured mapping to state encoding inside the actor
communications happen across species to various levels of fidelity but it is not the underlying mechanism of intelligence, it is an emerging behavior that allows for shared mental mapping and storage
You can’t just short-circuit from simulates to does to has subjective experience.
And the claim other humans don’t have subjective experience is such non-starter.
> And the claim other humans don’t have subjective experience is such non-starter.
What about other primates? Other mammals? The smarter species of cephalopods?
Certain many psychopaths seem to act as if they have this belief.
Then we can ask "if this is indistinguishable from a human, how can you be sure that anybody is intelligent?"
Personally I suspect we can make zombies that appear indistinguishable from humans (limited to video chat; making a robot that appears human to a doctor would be hard) but that don't have self-consciousness or any subjective experience.
I try to stay as far away from this stuff as possible because when the bottom falls out, it's going to have devastating effects for everyone involved. As a former computational linguist and someone who built similar tools at reasonable scale for largeish social media organizations in the teens, I learned the hard way not to trust the efficacy of these models or their ability to get the sort of reliability that a naive user would expect from them in practical application.
As the saying goes, 'every time I fire a linguist, the performance of the speech recognizer goes up'