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    AI

    Alexander Terenin Calls AI a Mirror to Human Hierarchies

    Cornell AI researcher posts quote comparing AI to social media as a revealing mirror.

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    15 Sources, 27d ago, first seen 27d ago

    TLDR

    Alexander Terenin, Assistant Research Professor at Cornell working on Bayesian models and Gaussian processes, posted the observation on social media. He wrote that many people hold a deep-rooted need for hierarchies with winners and losers chosen by those at the top. Terenin added that AI functions like social media by acting as a mirror that displays a more honest version of ourselves, which some find unsettling. The post links to another tweet and stands as the researcher's own stated view.

    Combined views

    431.2K

    15 Sources, first seen 27d ago

    Combined views

    431.2K

    15 Sources, first seen 27d ago

    1.6K likes
    1.6K likes
    183 comments
    501 saves
    142 reposts
    183 comments
    501 saves
    142 reposts

    Sentiment

    Positive24.3%75.7%Negative

    Summary

    Sentiment

    Positive24.3%75.7%Negative

    Many accounts rejected claims that AI could soon solve all mathematics, arguing it cannot create new ideas or insights the way humans do, while others welcomed the shift to AI solver engines as an exciting advance.

    Based on 81 sentiment-bearing replies from 74 accounts across 3 conversations.

    Summary

    Many accounts rejected claims that AI could soon solve all mathematics, arguing it cannot create new ideas or insights the way humans do, while others welcomed the shift to AI solver engines as an exciting advance.

    Based on 81 sentiment-bearing replies from 74 accounts across 3 conversations.

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    15 Sources

    @avt_imFor many, the need for a hierarchy with winners and losers - picked according to the criteria of those at the top, of course - is deep-rooted and powerful. Like social media, AI is a mirror - we hold it up, see a more honest version of ourselves, and don't like what we learn.
    @phinance99True. (And I was reposting, btw, intending to show support for your original post.) My feeling is that the negative reactions to AI outputs -- whether software or math proofs -- look like defensiveness or lamentations about a romanticized view of a field or process. But if you ever truly were seeking results (rather than just enjoying the process) this is an amazing time. The more important point (imho), which gets completely lost in the lamentations, is that we now have a new process to enjoy. No field can be "solved" because the space of problems is too vast, and we aren't just going to do the same things as before, only faster. Instead, we can all think bigger thoughts, take bigger steps, and pursue grander goals.
    @paulgInteresting prediction. He might be right, but I think the two cases are different, if only because individual employees at the labs are genuinely curious about open problems.
    @getjonwithitHmmm... if only there were a tool that let someone define a scientific problem, have AI formally specify a mathematical model for it, automatically verify its correctness properties, and then simulate it autonomously on dedicated compute...
    @HeinrichKuttler@avt_im I agree but I'd cut the math folks some slack; it's not everyday that a superhuman machine entity come along and upends your field.
    @WilliamBrykWe have to accept the possibility that all mathematics could fall to AI within some months, however unsettling that might seem. ie AI better than all humans at math. That's because math is not fundamentally "hard"... primates are just really bad at it. We did not evolve to be efficient at manipulating math symbols. We think of Terrence Tao as a legend, and he is on a human scale -- multiple standard deviations better at math than me. But to an AI, going from Will-level ability to Terrence-level math ability just means something like a 10x bigger context window, 10x bigger RL run, and 10x more test-time compute. Solving math in the past seemed to require some creative human spark we couldn't understand, but it's increasingly looking more like a mechanical search process over symbols. The aura and mystique mathematicians maintained for millennia can be reframed as them being extremely talented at this mechanical process. They can't compete with an alien creature trained exactly for this purpose, with compute specs far beyond Terrence. By 2027 we'll be cranking out mathematical beauties each morning like Alan Turing cranking out Enigma codes before breakfast. We'll still be bottlenecked by compute for a few years, so the big discoveries will come gradually -- those will take the most computation. Is this sad? Well like software engineering, those people who enjoy it for love of the craft or for their unique abilities compared to other humans will feel diminished. But those who love it for the end results will relish in a golden age of math. Humans will still be the ones mapping the mathematical landscape. The AIs will act like the helicopters taking us wherever we want to go. You gotta pay to ride in compute. You could map different human intellectual activities to whether it's "hard" in a computational sense. Human-level chess is easy, that's why computer solved it early. Moving limbs in physical space however is very hard. We're good at it bc we benefited from hundreds of millions of years of evolution. Software engineering is somewhere in the middle, because it's intertwined with a messy complex physical world filled with computationally hard emotions and group dynamics. So that's why weirdly we'll soon have AIs that dominate all humans on math, and yet we won't fully trust them as software engineers.
    @_ueajtbh I do kinda understand where the mathematicians are coming from, million line essentially brute force proofs don't really teach us anything. It does really still seem like sparks of genius are missing, though this is still a remarkable standard we are holding the models to considering almost all humans don't meet it either.
    @francoisfleuretMy philosophical conclusion of the state of AI is that if you play you are a mathematician, and you write gazillions of stuff that really look like math, you happen to actually do maths from time to time. If you have a device that does *ding* when it happens, you are gold.

    15 Sources

    @avt_imFor many, the need for a hierarchy with winners and losers - picked according to the criteria of those at the top, of course - is deep-rooted and powerful. Like social media, AI is a mirror - we hold it up, see a more honest version of ourselves, and don't like what we learn.
    @phinance99True. (And I was reposting, btw, intending to show support for your original post.) My feeling is that the negative reactions to AI outputs -- whether software or math proofs -- look like defensiveness or lamentations about a romanticized view of a field or process. But if you ever truly were seeking results (rather than just enjoying the process) this is an amazing time. The more important point (imho), which gets completely lost in the lamentations, is that we now have a new process to enjoy. No field can be "solved" because the space of problems is too vast, and we aren't just going to do the same things as before, only faster. Instead, we can all think bigger thoughts, take bigger steps, and pursue grander goals.
    @paulgInteresting prediction. He might be right, but I think the two cases are different, if only because individual employees at the labs are genuinely curious about open problems.
    @getjonwithitHmmm... if only there were a tool that let someone define a scientific problem, have AI formally specify a mathematical model for it, automatically verify its correctness properties, and then simulate it autonomously on dedicated compute...
    @HeinrichKuttler@avt_im I agree but I'd cut the math folks some slack; it's not everyday that a superhuman machine entity come along and upends your field.
    @WilliamBrykWe have to accept the possibility that all mathematics could fall to AI within some months, however unsettling that might seem. ie AI better than all humans at math. That's because math is not fundamentally "hard"... primates are just really bad at it. We did not evolve to be efficient at manipulating math symbols. We think of Terrence Tao as a legend, and he is on a human scale -- multiple standard deviations better at math than me. But to an AI, going from Will-level ability to Terrence-level math ability just means something like a 10x bigger context window, 10x bigger RL run, and 10x more test-time compute. Solving math in the past seemed to require some creative human spark we couldn't understand, but it's increasingly looking more like a mechanical search process over symbols. The aura and mystique mathematicians maintained for millennia can be reframed as them being extremely talented at this mechanical process. They can't compete with an alien creature trained exactly for this purpose, with compute specs far beyond Terrence. By 2027 we'll be cranking out mathematical beauties each morning like Alan Turing cranking out Enigma codes before breakfast. We'll still be bottlenecked by compute for a few years, so the big discoveries will come gradually -- those will take the most computation. Is this sad? Well like software engineering, those people who enjoy it for love of the craft or for their unique abilities compared to other humans will feel diminished. But those who love it for the end results will relish in a golden age of math. Humans will still be the ones mapping the mathematical landscape. The AIs will act like the helicopters taking us wherever we want to go. You gotta pay to ride in compute. You could map different human intellectual activities to whether it's "hard" in a computational sense. Human-level chess is easy, that's why computer solved it early. Moving limbs in physical space however is very hard. We're good at it bc we benefited from hundreds of millions of years of evolution. Software engineering is somewhere in the middle, because it's intertwined with a messy complex physical world filled with computationally hard emotions and group dynamics. So that's why weirdly we'll soon have AIs that dominate all humans on math, and yet we won't fully trust them as software engineers.
    @_ueajtbh I do kinda understand where the mathematicians are coming from, million line essentially brute force proofs don't really teach us anything. It does really still seem like sparks of genius are missing, though this is still a remarkable standard we are holding the models to considering almost all humans don't meet it either.
    @francoisfleuretMy philosophical conclusion of the state of AI is that if you play you are a mathematician, and you write gazillions of stuff that really look like math, you happen to actually do maths from time to time. If you have a device that does *ding* when it happens, you are gold.