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> By 2025, either these models will have hit a wall on diminishing returns and it will take a complete pivot to some other approach to continue to see notable g

> By 2025, either these models will have hit a wall on diminishing returns and it will take a complete pivot to some other approach to continue to see notable gains, or the products will continue to have improved at compounding rates and access will have become business critical in any industry with a modicum of competition. Is there a single example in AI, or even technology as a whole, where simply continuing to apply one technique has led to compounding growth? Even Moore's Law, according to none other than Jim Keller[1], is more a consequence of thousands of individual innovations that are each quite distinct from others but that build on each other to create this compounding growth we see. There is no similar curve for AI. In this case, GPT-3 (released in 2020) uses the same architecture as GPT-2 (released in 2019), expanded to have ~100x more parameters. It's not hard to see that compounding this process will rapidly hit diminishing returns quickly in terms of time, power consumption, cost of hardware, etc. Honestly, if Google, Amazon and Microsoft didn't see increased computational cost as a financial benefit for their cloud services, people might be willing to admit that GPT-3 is a diminishing return itself: for 100x parameters, is GPT-3 over 100x better than GPT-2? It seems that the big quantum leaps in AI come from new architectures applied in just the right way. CNNs and now transformers (based on multi-head attention) are the ways we've found to scale this thing, but those seem to come around every 25 years or so. Even the classes of problems they solve seem to change discretely and then approach some asymptote. Copilot will probably improve, but I doubt we will see much compounding. My best guess is that Copilot will steadily improve "arithmetically" as users react to its suggestions, or even that it will just change sporadically in response to this. [0]https://youtu.be/Nb2tebYAaOA?t=1975 https://youtu.be/Nb2tebYAaOA?t=1975

kromem 5y ago
My whole point is that the way in which AI progress can intersect itself can lead to compounding effects in ways technology has not. One way to improve upon GPT-3 is almost certainly going to be adding discriminators into the mix: "GeDi: A Powerful New Method for Controlling Language Models" https://blog.salesforceairesearch.com/gedi/ https://blog.salesforceairesearch.com/gedi/ And it may be that rather than it being a CNN discriminator such as in that case, it will be a second transformer: "[2102.07074] TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up" https://arxiv.org/abs/2102.07074 https://arxiv.org/abs/2102.07074 My point is we may not need to reinvent some new model separate from transformers/GANs and the work so far as long as novel ways of intersecting trained models produces results that can in turn feed intersect with other models. NVIDIA in particular has had some interesting focus on using AI to generate training data for AI. "NVIDIA Omniverse Replicator Generates Synthetic Training Data for Robots | NVIDIA Technical Blog" https://developer.nvidia.com/blog/generating-synthetic-datasets-isaac-sim-data-replicator/ https://developer.nvidia.com/blog/generating-synthetic-datas... I think it's very unlikely we need to wait 25 years for a new paradigm. In fact, I'm willing to wager a bet that whatever new paradigm will arrive will itself be the output of AI simulating different models.
kmod 5y ago
There has been some effort to quantify "AI scaling laws": ie how much performance increases as we scale up the resources involved. See section 1.2 of https://arxiv.org/pdf/2001.08361.pdf https://arxiv.org/pdf/2001.08361.pdf My main takeaway from that paper is that a 2x increase in training cost improves performance by 5% (100x by 42%). I only skimmed the paper though. To me this says that model scaling will not get us very much farther: we can probably do one more 100x but not two. I talked to someone working on model scaling and they see the same numbers and draw a very different conclusion: my interpretation of their argument is that they view scaling money as easy versus finding new fundamental technical advances.
- ShamelessC 5y ago
  > my interpretation of their argument is that they view scaling money as easy versus finding new fundamental technical advances. This paper only discussed transformers. Given the current pace of research, it's not a given that transformers won't be replaced by something else that has better scaling laws. And indeed, this paper tells you the amount of compute and data you need for a transformer. But Moore's law isn't doing great lately. For the purposes of research,you may be able to train trillion-parameter models - but you will likely not run such a model on your phone. In order to merit the large cost of both training and running predictions (which don't even fit the entire model in a single GPU for larger models) - models will need to become more parameter efficient than the vanilla transformer. Otherwise it's just too expensive.
- baryphonic 5y ago
  I hadn't seen that paper before, but it is excellent. Thank you for sharing! > To me this says that model scaling will not get us very much farther: we can probably do one more 100x but not two. I totally agree with this assessment, and note the absence of a GPT-4 release.

kromem 5y ago

My whole point is that the way in which AI progress can intersect itself can lead to compounding effects in ways technology has not. One way to improve upon GPT-3 is almost certainly going to be adding discriminators into the mix: "GeDi: A Powerful New Method for Controlling Language Models" https://blog.salesforceairesearch.com/gedi/ https://blog.salesforceairesearch.com/gedi/ And it may be that rather than it being a CNN discriminator such as in that case, it will be a second transformer: "[2102.07074] TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up" https://arxiv.org/abs/2102.07074 https://arxiv.org/abs/2102.07074 My point is we may not need to reinvent some new model separate from transformers/GANs and the work so far as long as novel ways of intersecting trained models produces results that can in turn feed intersect with other models. NVIDIA in particular has had some interesting focus on using AI to generate training data for AI. "NVIDIA Omniverse Replicator Generates Synthetic Training Data for Robots | NVIDIA Technical Blog" https://developer.nvidia.com/blog/generating-synthetic-datasets-isaac-sim-data-replicator/ https://developer.nvidia.com/blog/generating-synthetic-datas... I think it's very unlikely we need to wait 25 years for a new paradigm. In fact, I'm willing to wager a bet that whatever new paradigm will arrive will itself be the output of AI simulating different models.

kmod 5y ago

There has been some effort to quantify "AI scaling laws": ie how much performance increases as we scale up the resources involved. See section 1.2 of https://arxiv.org/pdf/2001.08361.pdf https://arxiv.org/pdf/2001.08361.pdf My main takeaway from that paper is that a 2x increase in training cost improves performance by 5% (100x by 42%). I only skimmed the paper though. To me this says that model scaling will not get us very much farther: we can probably do one more 100x but not two. I talked to someone working on model scaling and they see the same numbers and draw a very different conclusion: my interpretation of their argument is that they view scaling money as easy versus finding new fundamental technical advances.

ShamelessC 5y ago
> my interpretation of their argument is that they view scaling money as easy versus finding new fundamental technical advances. This paper only discussed transformers. Given the current pace of research, it's not a given that transformers won't be replaced by something else that has better scaling laws. And indeed, this paper tells you the amount of compute and data you need for a transformer. But Moore's law isn't doing great lately. For the purposes of research,you may be able to train trillion-parameter models - but you will likely not run such a model on your phone. In order to merit the large cost of both training and running predictions (which don't even fit the entire model in a single GPU for larger models) - models will need to become more parameter efficient than the vanilla transformer. Otherwise it's just too expensive.
baryphonic 5y ago
I hadn't seen that paper before, but it is excellent. Thank you for sharing! > To me this says that model scaling will not get us very much farther: we can probably do one more 100x but not two. I totally agree with this assessment, and note the absence of a GPT-4 release.