I would not be so sure about compute capacity? Neural network architectures are still in their infancy, it is very likely that more efficient approaches exist.
There could be huge efficiency savings within the implementations but when training costs are already this high it seems naive to think the low-hanging fruit is still there.
I was not familiar with Edge AI, interesting concept. I feel like improving the efficiency of very large models is much more likely.
The recent successes will lead to an even larger influx of $ in the short term --- this is an existential threat to Google, after all. We will see where things go!
I don't know why people just keep repeating this complete fantasy as if it were true. Where does it originate from, I wonder? I suspect someone said something like that on social media, their post went viral, and now all of the internet is reverberating with this thing. It's a meme, yes?
I don't have social media outside of HN.
It comes from a few observations:
1) Large models are still improving with increased parameter counts (we do not know where the ceiling is yet; it could be low but it could also be high).
2) Most current architectures train by using all model parameters to produce an output, which is vastly inefficient. While it is not clear how to improve on this in the general case yet, in the simpler problem of NERFs, sidestepping this issue has led to a ~100x improvement in training time.
3) https://mingukkang.github.io/GigaGAN/ very recently increased the parameter count of StyleGAN by selecting parameters dynamically at runtime. They improved on previous results by a very, very large margin, at somewhat comparable training times.
I stand by my claim: "neural network architectures are still in their infancy, it is very likely that more efficient approaches exist". I am not claiming that AI will become sentient or anything crazy and do not understand why you are associating my point of view with other people. I just said that it is likely that a novel technology will continue to improve (has this it ever NOT been the case for any new technology?).
Well, if you want to know whether neural nets are in their "infancy" you shouldn't make "observations", you should read the literature. It goes back many years. Go to the primary sources, why try to guess and risk guessing wrong, as here?
>> I stand by my claim: "neural network architectures are still in their infancy, it is very likely that more efficient approaches exist".
Half of your "claim" is incorrect. Don't just double down on it! There's really nothing to "claim" here, the "infancy" or not of neural nets is not a matter of claiming or guessing. Either you know what it is, or you don't.
"if you want to know whether neural nets are in their 'infancy' you shouldn't make 'observations'" -> I think you have understood this as "neural networks did not exist before" when what I meant is "neural networks will still change a lot in the future"
I was interested in discussing, specifically: will NNs continue scaling up in size in the near/long term. In answer to the statement "the training costs will exceed all the compute capacity in existence", I added "it is very likely that more efficient approaches exist.". You replied that NNs have existed for a long time, and that because of this, it is a complete fantasy that more efficient approaches exist. Do you feel like this is a fair assessment or no?
Well, my mistake then for taking you seriously. Many apologies. You can rest assured it won't happen again.
You are right that neural networks are not in their infancy. Best wishes.