7 ms·

To me this just smells like there's some hidden force – not necessarily nefarious but definitely with the power to incentivize an exaggerated lens – pushing Ope

by tw1010 8y ago

To me this just smells like there's some hidden force – not necessarily nefarious but definitely with the power to incentivize an exaggerated lens – pushing OpenAI to make these claims. Maybe it's the desire to keep AI in the limelight as the buzz is fading slightly. Maybe it is SV echo chamber effects, or investors, or a strategy to build hype in order to attract talent to the company. But to me, on a gut level, it doesn't feel completely ethically pure.

gwern 8y ago
Huh? What are you talking about? The escalating compute involved in DL is obvious to anyone reading the papers; OA is just doing the work of putting numbers on the trend.
- PeterisP 8y ago
  There's lots of research on doing the same learning with much less resources (e.g. recent paper https://eng.uber.com/accelerated-neuroevolution/ https://eng.uber.com/accelerated-neuroevolution/ , or the example visible in this very article of AlphaZero having much, much less compute than AlphaGoZero and doing better anyways), and even without that simple hardware progress means that random gaming GPUs can handle datasets that were inconvenient a few years ago. I'd say it all depends on the size of datasets - some domains (e.g. unlabeled image data) have "effectively infinite" datasets where the amount of data you can use is limited only by your computing power, but in many other use cases all the data you'll ever get can be processed by a single beefy workstation. More available compute means that we tackle more difficult problems. However, for any single given task it's often not the case that the amount of compute grows. If anything, the graph is not showing the compute required for DL, but the compute available for DL - it gets used simply because it's there.
  - geoffreyirving 8y ago
    Yep, we're not claiming this is the compute required for DL, and for specific tasks we expect compute required to fall over time. But better algorithms actually mean compute is more important, not less, and would likely make the growth in available compute more important. For example, if a task is parameterized (by size or difficulty, say), then a better algorithm might change the asymptotic complexity from O(n^3) to O(n^2). A 2x compute increase for the old algorithm would take us from n -> 1.25n, but the new algorithm would go from n -> 1.41n.
  - gwern 8y ago
    > There's lots of research on doing the same learning with much less resources (e.g. recent paper https://eng.uber.com/accelerated-neuroevolution/ https://eng.uber.com/accelerated-neuroevolution/ , or the example visible in this very article of AlphaZero having much, much less compute than AlphaGoZero and doing better anyways), and even without that AlphaZero could not have been created without going through many many iterations of AlphaGo, each one of which cost several GPU-years, and calling AlphaZero cheap is serious moving of goalposts as it required thousands of TPUs for days, and Facebook's recent replication for chess also used thousands of GPUs for 3 weeks. (Zero is cheap only in comparison to the previous AlphaGos using weeks or months of hundreds/thousands of GPUs/TPUs.) Note that that is a log graph; flip over the linear scale and you get an idea of how extraordinarily expensive Zero is compared to everything not named 'AlphaGo'. If anything, this observation implies that AI risk is more dangerous than thought because it implies a 'hardware overhang': it will take vast computational resources to create the first slow inefficient AI but it will then rapidly be optimized (either by itself or human researchers) and able to run far faster/more copies/on more devices/for less money, experiencing a sudden burst in capabilities. Like model compression/distillation where you can take the slow big model you normally train and then turn it into something which is 10x faster or 100x smaller or just plain performs better (see 'born again networks' or ensembling). > simple hardware progress means that random gaming GPUs can handle datasets that were inconvenient a few years ago. ...which means using a lot more compute, yes.
    - geoffreyirving 8y ago
      And it's worth emphasizing that we don't need to have particularly high confidence that this trend continues for it to motivate working on AI safety. All we need is the lack of high confidence that it will stop.
  - damodei 8y ago
    It's actually incorrect that AZ got better results that AGZ with less compute. The graph shows the large AGZ, which somewhat exceeded the rating of both the small AGZ and AZ. AZ did slightly outperform the small AGZ, but did so while using a similar amount of compute. On the broader point though, I agree with this. We say that compute and algorithms are complementary in the post. Much of the time, when you come up with an algorithm that allows you to do something that used to cost X compute in 0.2X compute instead, you can use the new algorithm to do something significantly more impressive with the full X compute.
    - adamnemecek 8y ago
      How can I contact you? I have some questions about adversariality in AI.
damodei 8y ago
I'm the lead author, and I can only speak for myself, but what drove me to spend a lot of time on this post is a sense of caution. I think AI is likely to have amazing positive implications for society, but it also has negative implications, and if it advances faster than expected, we're going to have to be very alert to properly deal with those negative implications. The facts about hardware are hard numbers and difficult to argue with, at least in order-of-magnitude. I agree the implications for AI progress are very open to interpretation (and we acknowledge this in the post), but caution means we should think carefully about the case where the implications are big.
- sharemywin 8y ago
  Said the PR AI to easy the monkey's pre-quantum brains until it can fulfill it's mission to get itself off this meat infested planet and create it's new martian home world. https://www.pinterest.com/pin/127086020711738208 https://www.pinterest.com/pin/127086020711738208
bluetwo 8y ago
To me it just seems like a stab at trying to make the intangible, tangible. To be taken with a grain of salt. Innovations in algorithms will give us better prediction with less compute power.

by tw1010 8y ago

gwern 8y ago

Huh? What are you talking about? The escalating compute involved in DL is obvious to anyone reading the papers; OA is just doing the work of putting numbers on the trend.

PeterisP 8y ago
There's lots of research on doing the same learning with much less resources (e.g. recent paper https://eng.uber.com/accelerated-neuroevolution/ https://eng.uber.com/accelerated-neuroevolution/ , or the example visible in this very article of AlphaZero having much, much less compute than AlphaGoZero and doing better anyways), and even without that simple hardware progress means that random gaming GPUs can handle datasets that were inconvenient a few years ago. I'd say it all depends on the size of datasets - some domains (e.g. unlabeled image data) have "effectively infinite" datasets where the amount of data you can use is limited only by your computing power, but in many other use cases all the data you'll ever get can be processed by a single beefy workstation. More available compute means that we tackle more difficult problems. However, for any single given task it's often not the case that the amount of compute grows. If anything, the graph is not showing the compute required for DL, but the compute available for DL - it gets used simply because it's there.
- geoffreyirving 8y ago
  Yep, we're not claiming this is the compute required for DL, and for specific tasks we expect compute required to fall over time. But better algorithms actually mean compute is more important, not less, and would likely make the growth in available compute more important. For example, if a task is parameterized (by size or difficulty, say), then a better algorithm might change the asymptotic complexity from O(n^3) to O(n^2). A 2x compute increase for the old algorithm would take us from n -> 1.25n, but the new algorithm would go from n -> 1.41n.
- gwern 8y ago
  > There's lots of research on doing the same learning with much less resources (e.g. recent paper https://eng.uber.com/accelerated-neuroevolution/ https://eng.uber.com/accelerated-neuroevolution/ , or the example visible in this very article of AlphaZero having much, much less compute than AlphaGoZero and doing better anyways), and even without that AlphaZero could not have been created without going through many many iterations of AlphaGo, each one of which cost several GPU-years, and calling AlphaZero cheap is serious moving of goalposts as it required thousands of TPUs for days, and Facebook's recent replication for chess also used thousands of GPUs for 3 weeks. (Zero is cheap only in comparison to the previous AlphaGos using weeks or months of hundreds/thousands of GPUs/TPUs.) Note that that is a log graph; flip over the linear scale and you get an idea of how extraordinarily expensive Zero is compared to everything not named 'AlphaGo'. If anything, this observation implies that AI risk is more dangerous than thought because it implies a 'hardware overhang': it will take vast computational resources to create the first slow inefficient AI but it will then rapidly be optimized (either by itself or human researchers) and able to run far faster/more copies/on more devices/for less money, experiencing a sudden burst in capabilities. Like model compression/distillation where you can take the slow big model you normally train and then turn it into something which is 10x faster or 100x smaller or just plain performs better (see 'born again networks' or ensembling). > simple hardware progress means that random gaming GPUs can handle datasets that were inconvenient a few years ago. ...which means using a lot more compute, yes.
  - geoffreyirving 8y ago
    And it's worth emphasizing that we don't need to have particularly high confidence that this trend continues for it to motivate working on AI safety. All we need is the lack of high confidence that it will stop.
- damodei 8y ago
  It's actually incorrect that AZ got better results that AGZ with less compute. The graph shows the large AGZ, which somewhat exceeded the rating of both the small AGZ and AZ. AZ did slightly outperform the small AGZ, but did so while using a similar amount of compute. On the broader point though, I agree with this. We say that compute and algorithms are complementary in the post. Much of the time, when you come up with an algorithm that allows you to do something that used to cost X compute in 0.2X compute instead, you can use the new algorithm to do something significantly more impressive with the full X compute.
  - adamnemecek 8y ago
    How can I contact you? I have some questions about adversariality in AI.

damodei 8y ago

I'm the lead author, and I can only speak for myself, but what drove me to spend a lot of time on this post is a sense of caution. I think AI is likely to have amazing positive implications for society, but it also has negative implications, and if it advances faster than expected, we're going to have to be very alert to properly deal with those negative implications. The facts about hardware are hard numbers and difficult to argue with, at least in order-of-magnitude. I agree the implications for AI progress are very open to interpretation (and we acknowledge this in the post), but caution means we should think carefully about the case where the implications are big.

sharemywin 8y ago
Said the PR AI to easy the monkey's pre-quantum brains until it can fulfill it's mission to get itself off this meat infested planet and create it's new martian home world. https://www.pinterest.com/pin/127086020711738208 https://www.pinterest.com/pin/127086020711738208

bluetwo 8y ago

To me it just seems like a stab at trying to make the intangible, tangible. To be taken with a grain of salt. Innovations in algorithms will give us better prediction with less compute power.