Thread

LeCun has "zero concerns" about AI wiping out humanity, recent "rogue" incidents

399 points · 752 comments · Anon84

edot · 1791067681 · focus · HN ↗
Absolutely based. Finally someone of stature in the industry calling this whole fear overblown. Bill Gates sounded like a nontechnical goofball in his Ezra Klein interview where he basically just screamed that the Terminator is real.
There are lots of real worries (government use to suppress the people with minimal manpower or popular support, brainrot and fake news, unemployment due to the belief that LLMs can replace people, education collapse, etc.) we should instead be looking at. This whole rogue AI shtick is tiresome.
1. reasonableklout · 1791077807 · focus · HN ↗
  LeCun has been consistently wrong about LLMs though, claiming that they were a dead end and that they'd never be able to do spatial reasoning, which was disproved a year later with GPT-4 [1]. He is also opposed by his fellow Turing laureates Geoffrey Hinton and Yoshua Bengio, who both signed the CAIS statement on AI extinction risk [2].
  [1]: <a href="https://www.reddit.com/r/OpenAI/comments/1d5ns1z/yann_lecun_confidently_predicted_that_llms_will/" rel="nofollow">https://www.reddit.com/r/OpenAI/comments/1d5ns1z/yann_lecun_...
  [2]: safe.ai/statement-on-ai-risk
  1. oxag3n · 1791083471 · focus · HN ↗
    [1] is not a valid proof LeCun was wrong, LLMs still can't do spacial reasoning when it can't be derived from the training data. He didn't argue that GPT 5000 won't be able to describe something with words.
    simianwords · 1791095070 · focus · HN ↗
    we have benchmarks proving it can do spatial reasoning.
    bawis · 1791101111 · focus · HN ↗
    Aaah, the old benchmarks maxxing argument, having precise and clear definition of what "spatial reasoning" is, what, and most importantly WHY, the benchmarks of choice are would settle this debate, otherweise let's not delve into it.
    qayxc · 1791105513 · focus · HN ↗
    ...poorly? <a href="https://youtu.be/ENWVpqtOdRI?t=867" rel="nofollow">https://youtu.be/ENWVpqtOdRI?t=867
    simianwords · 1791107164 · focus · HN ↗
    I’m not sure what you are trying to say
    qayxc · 1791116640 · focus · HN ↗
    When put to the test in real-world environment, the capabilities don't look as impressive as benchmarks and synthetic tests might indicate. So doubts about actual spatial reasoning capabilities remain.
    simianwords · 1791118206 · focus · HN ↗
    I see. Gary Marcus said that AI won’t be able to make a coffee in any arbitrary home kitchen.
    I think it’s a good test and I think LLMs will reach it in 3 years. Current benchmarks maybe slightly incorrect.
    I’m happy to make a 4:1 bet in my favour that I’m correct about the kitchen bet.
    sdeframond · 1791119811 · focus · HN ↗
    I cant make coffee in an arbitary house kitchen. People tend to put stuff anywhere but where at look for them...
    lelandfe · 1791121620 · focus · HN ↗
    Your prompter need merely to say “keep going” each time you report that you haven’t found the grounds yet.
    fnord77 · 1791128591 · focus · HN ↗
    [delayed]
    pixl97 · 1791133150 · focus · HN ↗
    Careful, you'll have them accelerating their goal posts up to the speed of light if you keep questioning them. That much kinetic energy is dangerous.
    Of course this is the same reason LeCun holds very little sway with his words for me, they seem to be terrible predictors of the future.
    widdershins · 1791103853 · focus · HN ↗
    I've seen recent AIs make detailed and technically impressive 3D models. You might argue "they're not doing spatial reasoning, they're making measurements with code and doing math to configure relative positions". Fine, but at a certain point that becomes functionally indistinguishable from spatial reasoning.
    qayxc · 1791105489 · focus · HN ↗
    I don't know - real-world tests leave me unconvinced: <a href="https://youtu.be/ENWVpqtOdRI?t=867" rel="nofollow">https://youtu.be/ENWVpqtOdRI?t=867
    brainwad · 1791115925 · focus · HN ↗
    This really doesn't match my experience. I can ask an LLM to modify engineering plans using vague natural language prompts and it will find the right place in the plan from the description and then make appropriate modifications, which necessarily requires doing spacial reasoning.
    mym1990 · 1791127183 · focus · HN ↗
    Or it’s just taking common examples from training and applying those copied heuristics to your problem? Doesn’t mean it’s actually reasoning about the space and how to solve the problem. It’s the equivalent of a student writing an answer they saw somewhere else without understanding “why”.
    brainwad · 1791130563 · focus · HN ↗
    Looking at the reasoning traces it sure seems like it's reasoning. It internally debates which of the possibly matching parts of the input are the one described by me in the prompt and picks the right one based on sound reasoning.
    fwip · 1791142411 · focus · HN ↗
    [delayed]
    brainwad · 1791144391 · focus · HN ↗
    It's not just text that appears at first blush to resemble reasoning, it's actual sound reasoning. And it can chain it for hours at a time without breaking down.
    jeremyjh · 1791132181 · focus · HN ↗
    Why does CoT significantly improve their performance? Also, most of what they are applying they learned in post-training by solving similar problems themselves. This isn't about regurgitating pre-trained knowledged.
    mym1990 · 1791157966 · focus · HN ↗
    I have not seen the research on CoT and how it impacts spatial reasoning and outcomes, so I can’t comment on that part. I do see continuously that in almost every example of non-trivial image generation and 3d modeling, there are quirks that point to the fact that the model does not understand relationships within the space based on physics.
    CoT seems to work really well for text based generation but once you’re past that and into physics models and detailed relationship mapping, it may work better but it’s not enough to make me believe it’s “reasoning” in a way that humans do.
    jxcole · 1791129541 · focus · HN ↗
    I think the point here is that LeCun was arguing that training on pure text would not grant spatial understanding. I believe most models are trained on spatial data as well, so you are both right.
    brainwad · 1791130708 · focus · HN ↗
    Is that what he meant? He works on models with an explicitly spatial internal representation, whereas I was using a standard LLM that edited the provided plan by using a bajillion python calls to inspect small regions of the image at a time.
    mistercow · 1791134305 · focus · HN ↗
    > when it can't be derived from the training data
    This sounds like a goalpost on wheels. Can you define clearly where your stake in the ground is?
  2. realusername · 1791108405 · focus · HN ↗
    Why chatgpt is still struggling very hard with photo editing and proportions though? It can't modify anything in a picture without messing the 3d space.
    Isn't that a lack of spacial reasoning?
  3. mohamedkoubaa · 1791123107 · focus · HN ↗
    Almost everyone who knows what they are talking about is saying that LLMs are a dead end.

Open on Hacker News to reply ↗

Unofficial Hacker News client; not affiliated with Y Combinator.