5 ms·

LLMs keep showing more and more they are the wonder of AI that we awaited for decades: talking machines that every two months do progresses that two months befo

LLMs keep showing more and more they are the wonder of AI that we awaited for decades: talking machines that every two months do progresses that two months before were anticipated impossible because of <put here some limit that was actually in the prejudice of the skeptical AI community> (just stochastic parrots, no reasoning possible without symbolic representations, there are no longer tokens, ...) At the same time, part of the scientific community continues to diminish what was accomplished and the steps that are being made. A few months ago LeCun arrived to tell new researchers to move away from LLMs since they are a dead end: imagine the disservice he made to the surely non-zero folks that followed the advice, putting themselves out of the AI research that matters. (Incidentally, this skepticism of the Meta AI head must have something to do with the fact that Meta, despite the huge efforts allocated, produced the worst LLM among Anthropic, OpenAI, DeepSeek -- I bet Zuckerberg is asking questions lately). It's very hard to explain this behavior if not by psychological denial. [EDIT: you can't see the score of this comment, but I can: it's incredible how it goes from 3, to -2, to 1, and so forth. The community is split in two, and it is pretty sad since this is not a matter of taste or political inclination: there must be a single truth]

ramblerman 2y ago
I get the sentiment, but I actually think some skepticism in the system is healthy. Billions are flowing towards LLMS, and Sam Altman will overpromise AGI is just around the corner and the days of jobs are gone to fill his coffers to anyone that will listen. Additionally if we begin to use these things in real production environments where mistakes matters, knowing the exact limitations is key. None of this takes away from the fact that these are exciting times.
- antirez 2y ago
  Yes there is another part of the community that overhypes everything. But I can expect that from a CEO of an AI company (especially if he is Altman), but from researches? Also the fact that LLMs may reach superhuman expertise in certain fields in a short timeframe (a few years), since reinforcement learning is starting to be applied to LLMs may no longer be a totally crazy position. If it is possible to extend considerably the same approach seen in R1-Zero there could be low hanging fruits around the corner.
- comeonbro 2y ago
  This article is about things which aren't limitations anymore! You are applauding it as pushback for pushback's sake, but it's an article about limitations in biplane construction, published after we'd already landed on the moon.
  - suddenlybananas 2y ago
    Is there any evidence that these fundamental issues with compositionality have been resolved or are you just asserting it? Has the paper been replicated with a CoT model and had a positive result?
    - dr_dshiv 2y ago
      Well, yes — because modern models can solve all the examples in the article. The theory of compositionality is still an issue, but the evidence for it recedes. I think most of the issue comes from the challenge of informational coherence. Once incoherence enters the context, the intelligence drops massively. You can have a lot of context and LLMs can maintain coherence— but not if the context itself is incoherent. And, informationally, it is just a matter of time before a little incoherence gets into a thread. This is why agents have so much potential—being able to separate out separate threads of thought in different context windows reduces the likelihood of incoherence emerging (vs one long thread). Actually, maybe “cybernetic ecologies” are closer to what I mean than “agents.” See Anthropic’s “Building Effective Agents.” https://www.anthropic.com/research/building-effective-agents https://www.anthropic.com/research/building-effective-agents
      - anon84873628 2y ago
        >I think most of the issue comes from the challenge of informational coherence. Once incoherence enters the context, the intelligence drops massively. You can have a lot of context and LLMs can maintain coherence— but not if the context itself is incoherent. As a non-expert, part of my definition of intelligence is that the system can detect incoherence, a.k.a reject bullshit. LLMs today can't do that and will happily emit bullshit in response. Maybe the "gates" in the "workflows" discussed in the Anthropic article are a practical solution to that. But that still just seems like inserting human intelligence into the system for a specific engineering domain; not a general solution.
- dr_dshiv 2y ago
  I can’t communicate enough how the skepticism (“this is just hype” or “LLMs are stochastic parrots”) is the vastly dominant thought paradigm in European academic circles. So instead of everyone having some enthusiasm and some skepticism, you get a bifurcation where whole classes of people act as the skeptics and others as the enthusiasts. I view the strong skeptics as more “in the wrong” because they often don’t use LLMs much. If you are an actual enthusiastic user, you simply can’t get good performance without a very strong dose of skepticism towards everything LLMs output.
  - _t9ow 2y ago
    > I can’t communicate enough how the skepticism (“this is just hype” or “LLMs are stochastic parrots”) is the vastly dominant thought paradigm in European academic circles. I'm very curious. If you don't mind taking the time to elaborate, will you give a few examples of such skepticism/naysaying? Thank you.
    - tessellated 2y ago
      Some "academia" types I meet seem to be struck in this "skeptic vs enthusiast" discussion. How do we harness LLMs’ potential while rigorously mitigating harm?
  - fragmede 2y ago
    I don't think everyone shares those doubts. The first time you catch an LLM in a lie is sobering, but there are lots of areas, and thus lots of users, for whom it doesn't hallucinate for, because they're asking softball questions and it doesn't end up hallucinating, or hallucinations just really aren't aren't that big a deal. (eg an LLM horoscope generator or using it write sci fi.) so while we're on HN going back and forth about how outright lies by the system indight the whole thing for everybody, we should be careful to note that it's not for everybody, or rather, it's a known limitation so don't trust it to cite real cases for you as a lawyer, but using it to help you figure out what mens rea means in a practical sense by asking it questions about the concept, totally. Honestly, hallucinations happen so rarely for me because of the kinds of things I ask it, that it doesn't happen enough for me to not believe it's answers in low-stakes situations, or situations on the level of horoscope generation, and I'm sure I'm not alone in treating ChatGPT that way, despite evidence to the contrary.
kubb 2y ago
I'm sorry but they don't "do progress that was anticipated impossible", especially not every two months. They were predicted to end the software engineering profession for almost four years already. And it just doesn't happen, even though they can bang out a perfect to-do list in React in a matter of seconds. LLMs have incremental improvements on the quality of their responses as measured by benchmarks. The speed and cost of inference has also been improving. Despite that there was no major breakthrough since GPT 3. People keep trying to make them reason, and keep failing at it.
- throw310822 2y ago
  > They were predicted to end the software engineering profession for almost four years already ChatGPT was launched on November 30 2022. Two years and two months ago. The fact that in such a short timeframe you're talking about missed predictions is absurd, but telling of the accelerated timeframe in which we're living. The fact is that currently AI and LLMs are going through a phase of explosive improvement, to the point we can expect enormous improvements in capabilities every six months or so.
  - kubb 2y ago
    And what has enormously improved since ChatGPTs launch? Maybe you should ask it what it "thinks" about the hype surrounding it.
    - mmcnl 2y ago
      This is a good question. According to some the growth is exponential. Others think ChatGPT is basically still the same as it was at the end of 2022, minor differences aside. Why are the perspectives so different?
      - kubb 2y ago
        One of these archetypes is drowning in a hype-fueled news cycle, they mistake speculation for inevitability, dismiss skepticism as ignorance, and construct a reality where the technology's success is unquestionable. The other is simply using the technology for what it's good for, observing that it's slowly, incrementally improving at tasks that it was already capable of since the major breakthrough, and acknowledging its limitations. Incremental improvements don't give us any assurance that another major breakthrough is waiting around the corner.
sitkack 2y ago
It is becoming more and more important to determine for ourselves what is true and what is not. No person is right on most things, even when they are an expert in that thing. The biggest trap, is to believe someone because they are passionate, that they say it with conviction. Ignore most of the out of band signaling, take what they are saying and then also see if you can corroborate with another source. There are so many people who are wrong about so may things. I really appreciate that you are making your dev with ai videos, it shows people different, more humanistic ways of operating with AI. Most of what I use AI for is to understand and relearn things I only thought I knew. This I think, is the most powerful use of AI, not in the code writing or the image generation, but in understanding and synthesis. There is that hilarious tautological statement, "it is easy if you know it". This video https://www.youtube.com/watch?v=TPLPpz6dD3A https://www.youtube.com/watch?v=TPLPpz6dD3A shows how to use AI to be a personal tutor using the Socratic Method. This is what people should be using AI for, have it test you for things you think you are already good at and you will find huge gaps in your own understanding. Now go apply it to things you have no clue about. Speaking of parrots, a large volume of the anti AI sentiment, even here is by people repeating half truths they don't understand, confidently, about what AI cannot do. One would need a pretty tight formal case to prove such things. Everyone should be playing, learning and exploring with these new tools, not shutting each other down.
- sitkack 2y ago
  Another trope that stands out is that someone will take a model, run a battery of tests against it and then make general statements about what LLMs can and cannot do without understanding their architecture, the training data, and the training itself. And then they dress it up to sound scientific, when really they are making hasty generalizations to support a preconceived bias.
- antirez 2y ago
  Yes, the stochastic parrots story is one of the most strong instances in recent times where experts in a field are made blind by their own expertise (the mental model they have of certain things) to the point of being incapable of seeing trivial evidences.
rbranson 2y ago
Did you read the article? Dziri and Peng are not the “skeptical AI community,” they are in fact die hard AI researchers. This is like saying people who run benchmarks to find performance problems in code are skeptics or haters.
- antirez 2y ago
  I read the article: it does not look like very good research: It's simple to find flaws in LLMs reasoning / compositional capabilities looking at problems that are at the limit of what they can do now, or just picking problems that are very far from their computational model, or submitting riddles. But there is no good analysis of the limitations, nor inspection of how/how much better recently LLMs got exactly at this kind of problems. Also the article is full of uninformative and obvious things to show how LLMs fail in stupid tasks such as multiplication between large numbers. But the most absurd thing is that the paper looks at computational complexity in terms of direct function composition, and there is no reason an LLM should just use this kind of model when emitting many tokens. Note that even when CoT is not explicit, the LLM output that starts to shape the thinking process still makes it able to have technically unbound layers. With CoT this is even more obvious. Basically there is no bridge between their restricted model and an LLM.
raindear 2y ago
Progress is what happens thanks to AI skeptics busy defining model limitations. The limitations set attractive bars to pass.
pera 2y ago
Psychological denial of what exactly? And what part of the article/preprints you are commenting on? Every time an article exposing some limitation of the current wave of LLMs is submitted to HN there are comments like yours and I genuinely cannot understand the point you are trying to make: There is no such thing as a perfect technology, everything has limitations, and we can only improve our current state of the art by studying these and iterate.
unification_fan 2y ago
[flagged]
polotics 2y ago
you are I think badly misrepresenting what Yann Le Cun said: he didn't say LLM's were a dead end, he said to do research in directions that do not require billions of dollars of investment to show results, in particular for PhD's this is sensible, and in view of recent cheaper results, prescient
- fragmede 2y ago
  Sensible with the caveat that deepseek R1 still took millions of dollars off compute time, so you're not training the next one on the box in your basement with a pair of 3090s (though you could certainly fine-tune a shared quantized model). you can't run the full sized model on anything cheap, so. basement researcher still need access to a decent amount of funding, which likely requires outside help.
mdp2021 2y ago
> At the same time, part of the scientific community continues to diminish what was accomplished Revisit the idea: part of the public is bewildered by voices that started calling "intelligence" what was and apparently still is the precise implementation of unintelligence. The fault is in some, many people - as usual. Very recent state-of-the-art LLM models themselves declare that if the majority of their training data states that entity E is red they will say it's red, and if the majority says it's blue then they will say it's blue: that is the implementation of an artificial moron. And in fact, very recent state-of-the-art LLM models state cretinous ideas that are child level - because "that's what they have heard" (stuck, moreover analytically, in the simplifications intrinsic in expression). This architectural fault should be the foremost concern.
lowsong 2y ago
> …it is pretty sad since this is not a matter of taste or political inclination: there must be a single truth This is more of a salient point that you perhaps realized. In life there is no single absolute, unknowable truth. Philosophy has spent the entire span of human existence grappling with this topic. The real risk with AI is not that we build some humanity-destroying AGI, but that we build a machine that is 'convincing enough' — and the idea that such a machine would be built by people who believe in objective truth is the most worrying part.
- thrance 2y ago
  Depends, if you're a realist [1] (like most) then there can be such a thing as absolute truth, that you may not always be able to access. [1] https://en.wikipedia.org/wiki/Philosophical_realism?wprov=sfla1 https://en.wikipedia.org/wiki/Philosophical_realism?wprov=sf...
  - askl56 2y ago
    This is teleologically false. A teleological argument that assumes truth is contingent upon a specific worldview would indeed be flawed, because it would make truth an artifact of a given perspective rather than something independent of it.
nuancebydefault 2y ago
Well, there appears to be evolution in human perception of capabilities of LLMs. An example, the 'stochastic parrots' notion seems to have mostly died out, at least in HN comments.
rthrfrd 2y ago
I think if we referred to LLMs as AK (Artifical Knowledge) instead of AI it would be easier to have more cohesive discussions. I don’t see how there can be a single truth when there is not even a single definition of many of the underlying terms (intelligence, AGI, etc) which this discipline supposedly defines itself by. Combine that with a lot of people with little philosophical perspective suddenly being confronted with philosophical topics and you end up with a discourse that personally I’ve mostly given up on participating in until things calm down again. It feels like nobody remembers all the timelines for which we were supposed to have self-driving cars.
layer8 2y ago
I think that “part of the scientific community” actually wants to do what needs to be done: “We have to really understand what’s going on under the hood,” she said. “If we crack how they perform a task and how they reason, we can probably fix them. But if we don’t know, that’s where it’s really hard to do anything.”
deleted 2y ago
[deleted]

ramblerman 2y ago

I get the sentiment, but I actually think some skepticism in the system is healthy. Billions are flowing towards LLMS, and Sam Altman will overpromise AGI is just around the corner and the days of jobs are gone to fill his coffers to anyone that will listen. Additionally if we begin to use these things in real production environments where mistakes matters, knowing the exact limitations is key. None of this takes away from the fact that these are exciting times.

antirez 2y ago
Yes there is another part of the community that overhypes everything. But I can expect that from a CEO of an AI company (especially if he is Altman), but from researches? Also the fact that LLMs may reach superhuman expertise in certain fields in a short timeframe (a few years), since reinforcement learning is starting to be applied to LLMs may no longer be a totally crazy position. If it is possible to extend considerably the same approach seen in R1-Zero there could be low hanging fruits around the corner.
comeonbro 2y ago
This article is about things which aren't limitations anymore! You are applauding it as pushback for pushback's sake, but it's an article about limitations in biplane construction, published after we'd already landed on the moon.
- suddenlybananas 2y ago
  Is there any evidence that these fundamental issues with compositionality have been resolved or are you just asserting it? Has the paper been replicated with a CoT model and had a positive result?
  - dr_dshiv 2y ago
    Well, yes — because modern models can solve all the examples in the article. The theory of compositionality is still an issue, but the evidence for it recedes. I think most of the issue comes from the challenge of informational coherence. Once incoherence enters the context, the intelligence drops massively. You can have a lot of context and LLMs can maintain coherence— but not if the context itself is incoherent. And, informationally, it is just a matter of time before a little incoherence gets into a thread. This is why agents have so much potential—being able to separate out separate threads of thought in different context windows reduces the likelihood of incoherence emerging (vs one long thread). Actually, maybe “cybernetic ecologies” are closer to what I mean than “agents.” See Anthropic’s “Building Effective Agents.” https://www.anthropic.com/research/building-effective-agents https://www.anthropic.com/research/building-effective-agents
    - anon84873628 2y ago
      >I think most of the issue comes from the challenge of informational coherence. Once incoherence enters the context, the intelligence drops massively. You can have a lot of context and LLMs can maintain coherence— but not if the context itself is incoherent. As a non-expert, part of my definition of intelligence is that the system can detect incoherence, a.k.a reject bullshit. LLMs today can't do that and will happily emit bullshit in response. Maybe the "gates" in the "workflows" discussed in the Anthropic article are a practical solution to that. But that still just seems like inserting human intelligence into the system for a specific engineering domain; not a general solution.
dr_dshiv 2y ago
I can’t communicate enough how the skepticism (“this is just hype” or “LLMs are stochastic parrots”) is the vastly dominant thought paradigm in European academic circles. So instead of everyone having some enthusiasm and some skepticism, you get a bifurcation where whole classes of people act as the skeptics and others as the enthusiasts. I view the strong skeptics as more “in the wrong” because they often don’t use LLMs much. If you are an actual enthusiastic user, you simply can’t get good performance without a very strong dose of skepticism towards everything LLMs output.
- _t9ow 2y ago
  > I can’t communicate enough how the skepticism (“this is just hype” or “LLMs are stochastic parrots”) is the vastly dominant thought paradigm in European academic circles. I'm very curious. If you don't mind taking the time to elaborate, will you give a few examples of such skepticism/naysaying? Thank you.
  - tessellated 2y ago
    Some "academia" types I meet seem to be struck in this "skeptic vs enthusiast" discussion. How do we harness LLMs’ potential while rigorously mitigating harm?
- fragmede 2y ago
  I don't think everyone shares those doubts. The first time you catch an LLM in a lie is sobering, but there are lots of areas, and thus lots of users, for whom it doesn't hallucinate for, because they're asking softball questions and it doesn't end up hallucinating, or hallucinations just really aren't aren't that big a deal. (eg an LLM horoscope generator or using it write sci fi.) so while we're on HN going back and forth about how outright lies by the system indight the whole thing for everybody, we should be careful to note that it's not for everybody, or rather, it's a known limitation so don't trust it to cite real cases for you as a lawyer, but using it to help you figure out what mens rea means in a practical sense by asking it questions about the concept, totally. Honestly, hallucinations happen so rarely for me because of the kinds of things I ask it, that it doesn't happen enough for me to not believe it's answers in low-stakes situations, or situations on the level of horoscope generation, and I'm sure I'm not alone in treating ChatGPT that way, despite evidence to the contrary.

kubb 2y ago

I'm sorry but they don't "do progress that was anticipated impossible", especially not every two months. They were predicted to end the software engineering profession for almost four years already. And it just doesn't happen, even though they can bang out a perfect to-do list in React in a matter of seconds. LLMs have incremental improvements on the quality of their responses as measured by benchmarks. The speed and cost of inference has also been improving. Despite that there was no major breakthrough since GPT 3. People keep trying to make them reason, and keep failing at it.

throw310822 2y ago
> They were predicted to end the software engineering profession for almost four years already ChatGPT was launched on November 30 2022. Two years and two months ago. The fact that in such a short timeframe you're talking about missed predictions is absurd, but telling of the accelerated timeframe in which we're living. The fact is that currently AI and LLMs are going through a phase of explosive improvement, to the point we can expect enormous improvements in capabilities every six months or so.
- kubb 2y ago
  And what has enormously improved since ChatGPTs launch? Maybe you should ask it what it "thinks" about the hype surrounding it.
  - mmcnl 2y ago
    This is a good question. According to some the growth is exponential. Others think ChatGPT is basically still the same as it was at the end of 2022, minor differences aside. Why are the perspectives so different?
    - kubb 2y ago
      One of these archetypes is drowning in a hype-fueled news cycle, they mistake speculation for inevitability, dismiss skepticism as ignorance, and construct a reality where the technology's success is unquestionable. The other is simply using the technology for what it's good for, observing that it's slowly, incrementally improving at tasks that it was already capable of since the major breakthrough, and acknowledging its limitations. Incremental improvements don't give us any assurance that another major breakthrough is waiting around the corner.

sitkack 2y ago

It is becoming more and more important to determine for ourselves what is true and what is not. No person is right on most things, even when they are an expert in that thing. The biggest trap, is to believe someone because they are passionate, that they say it with conviction. Ignore most of the out of band signaling, take what they are saying and then also see if you can corroborate with another source. There are so many people who are wrong about so may things. I really appreciate that you are making your dev with ai videos, it shows people different, more humanistic ways of operating with AI. Most of what I use AI for is to understand and relearn things I only thought I knew. This I think, is the most powerful use of AI, not in the code writing or the image generation, but in understanding and synthesis. There is that hilarious tautological statement, "it is easy if you know it". This video https://www.youtube.com/watch?v=TPLPpz6dD3A https://www.youtube.com/watch?v=TPLPpz6dD3A shows how to use AI to be a personal tutor using the Socratic Method. This is what people should be using AI for, have it test you for things you think you are already good at and you will find huge gaps in your own understanding. Now go apply it to things you have no clue about. Speaking of parrots, a large volume of the anti AI sentiment, even here is by people repeating half truths they don't understand, confidently, about what AI cannot do. One would need a pretty tight formal case to prove such things. Everyone should be playing, learning and exploring with these new tools, not shutting each other down.

sitkack 2y ago
Another trope that stands out is that someone will take a model, run a battery of tests against it and then make general statements about what LLMs can and cannot do without understanding their architecture, the training data, and the training itself. And then they dress it up to sound scientific, when really they are making hasty generalizations to support a preconceived bias.
antirez 2y ago
Yes, the stochastic parrots story is one of the most strong instances in recent times where experts in a field are made blind by their own expertise (the mental model they have of certain things) to the point of being incapable of seeing trivial evidences.

rbranson 2y ago

Did you read the article? Dziri and Peng are not the “skeptical AI community,” they are in fact die hard AI researchers. This is like saying people who run benchmarks to find performance problems in code are skeptics or haters.

antirez 2y ago
I read the article: it does not look like very good research: It's simple to find flaws in LLMs reasoning / compositional capabilities looking at problems that are at the limit of what they can do now, or just picking problems that are very far from their computational model, or submitting riddles. But there is no good analysis of the limitations, nor inspection of how/how much better recently LLMs got exactly at this kind of problems. Also the article is full of uninformative and obvious things to show how LLMs fail in stupid tasks such as multiplication between large numbers. But the most absurd thing is that the paper looks at computational complexity in terms of direct function composition, and there is no reason an LLM should just use this kind of model when emitting many tokens. Note that even when CoT is not explicit, the LLM output that starts to shape the thinking process still makes it able to have technically unbound layers. With CoT this is even more obvious. Basically there is no bridge between their restricted model and an LLM.

raindear 2y ago

Progress is what happens thanks to AI skeptics busy defining model limitations. The limitations set attractive bars to pass.

pera 2y ago

Psychological denial of what exactly? And what part of the article/preprints you are commenting on? Every time an article exposing some limitation of the current wave of LLMs is submitted to HN there are comments like yours and I genuinely cannot understand the point you are trying to make: There is no such thing as a perfect technology, everything has limitations, and we can only improve our current state of the art by studying these and iterate.

unification_fan 2y ago

[flagged]

polotics 2y ago

you are I think badly misrepresenting what Yann Le Cun said: he didn't say LLM's were a dead end, he said to do research in directions that do not require billions of dollars of investment to show results, in particular for PhD's this is sensible, and in view of recent cheaper results, prescient

fragmede 2y ago
Sensible with the caveat that deepseek R1 still took millions of dollars off compute time, so you're not training the next one on the box in your basement with a pair of 3090s (though you could certainly fine-tune a shared quantized model). you can't run the full sized model on anything cheap, so. basement researcher still need access to a decent amount of funding, which likely requires outside help.

mdp2021 2y ago

> At the same time, part of the scientific community continues to diminish what was accomplished Revisit the idea: part of the public is bewildered by voices that started calling "intelligence" what was and apparently still is the precise implementation of unintelligence. The fault is in some, many people - as usual. Very recent state-of-the-art LLM models themselves declare that if the majority of their training data states that entity E is red they will say it's red, and if the majority says it's blue then they will say it's blue: that is the implementation of an artificial moron. And in fact, very recent state-of-the-art LLM models state cretinous ideas that are child level - because "that's what they have heard" (stuck, moreover analytically, in the simplifications intrinsic in expression). This architectural fault should be the foremost concern.

lowsong 2y ago

> …it is pretty sad since this is not a matter of taste or political inclination: there must be a single truth This is more of a salient point that you perhaps realized. In life there is no single absolute, unknowable truth. Philosophy has spent the entire span of human existence grappling with this topic. The real risk with AI is not that we build some humanity-destroying AGI, but that we build a machine that is 'convincing enough' — and the idea that such a machine would be built by people who believe in objective truth is the most worrying part.

thrance 2y ago
Depends, if you're a realist [1] (like most) then there can be such a thing as absolute truth, that you may not always be able to access. [1] https://en.wikipedia.org/wiki/Philosophical_realism?wprov=sfla1 https://en.wikipedia.org/wiki/Philosophical_realism?wprov=sf...
- askl56 2y ago
  This is teleologically false. A teleological argument that assumes truth is contingent upon a specific worldview would indeed be flawed, because it would make truth an artifact of a given perspective rather than something independent of it.

nuancebydefault 2y ago

Well, there appears to be evolution in human perception of capabilities of LLMs. An example, the 'stochastic parrots' notion seems to have mostly died out, at least in HN comments.

rthrfrd 2y ago

I think if we referred to LLMs as AK (Artifical Knowledge) instead of AI it would be easier to have more cohesive discussions. I don’t see how there can be a single truth when there is not even a single definition of many of the underlying terms (intelligence, AGI, etc) which this discipline supposedly defines itself by. Combine that with a lot of people with little philosophical perspective suddenly being confronted with philosophical topics and you end up with a discourse that personally I’ve mostly given up on participating in until things calm down again. It feels like nobody remembers all the timelines for which we were supposed to have self-driving cars.

layer8 2y ago

I think that “part of the scientific community” actually wants to do what needs to be done: “We have to really understand what’s going on under the hood,” she said. “If we crack how they perform a task and how they reason, we can probably fix them. But if we don’t know, that’s where it’s really hard to do anything.”

deleted 2y ago

[deleted]