Human consciousness itself is probably a local maximum. Not necessarily a bad thing to see how far a certain process can get you as long as you keep getting results.

At least until there's some other insight along the way.

If not countered, we have a genetic algorithm that may attempt to move our far descendants out of the local maximum. Plus we can change the environment, both locally and just move into space.

I agree w/ you about moving up a gradient quickly w/ the "GPU manufacturing <-> deep learning research" feedback loop. I think it could last a while though. One really important area of research is figuring out how to take better advantage of greater capacity. Also, how to do more with fewer training samples (0 shot, 1 shot, etc learning). Then there's reducing the precision of the units you're using to increase capacity through software. Applying these algos to video, audio, media generation, and others will eat up all the resources you can throw at it; the algos today could take advantage of larger capacity when applied to time-series. There's so much going on that I don't see it slowing down for at least 3-5 years.

Also, I'd like to point out that we've seen some big breakthroughs in the past 10 years. But for the past 10 years, the whole field of deep learning has been looked at with skepticism and has been very niche. Over the past couple of years, a lot of money and resources have been put in place to pursue this area of research. More money doesn't necessarily mean more results, but there are many many more people working on these problems than ever before. A lot of them are legitimately brilliant researchers in the prime of their careers. I think there's still more to see.

I am concerned about an Nvidia monopoly around deep learning hardware. They give away tons of free cards to deep learning research groups, but at some point they'll want people to start buying. I assume they expect that will be the enterprise set, but if suddenly they manage to move all their capable deep learning cards to Teslas only (which have a huge markup), it will put the hobbyist deep learning developer at a big disadvantage. The only check and balance on that is the fact that Nvidia makes cards for gamers too, and gaming card competition is still somewhat robust, so any technology that gives enterprise a big boost will probably make its way to their flagship gaming cards quickly. Nvidia's only real competition, AMD, is so far behind that they might as well not be in the business. As someone who usually roots for the underdog, it pains me to see AMD fumble so badly in this whole area.

Quantum computing could be a new hill, but I think that's a ways out and I don't know enough about the topic to speak with any real confidence.

As someone who does (non-NN) ML on AMD, I might ask why you think that AMD is so far behind. In my experience, their GPU hardware is excellent, maybe even better than Nvidia, especially if you factor the price in. Where they ARE lagging heavily is:

1. GPU as a service: While all major providers (AWS, Azure) offer Teslas on their servers, there is no AMD on the cloud (that I know of)

2. Key libraries: Nvidia comes with matrix libraries and cuDNN out of the box, while for AMD, there are only open source offerings that are a bit difficult to manage.

But, If you write your own software or rely on open source, AMD is quite performant and affordable. The problem is that it is really obscure. So, yes, they are really bad at marketing and if you are looking at them as a user instead of as a developer, they are invisible.

I think I agree with everything you just said.

AMD hardware is more or less the same as Nvidia; Nvidia doesn't really have any special sauces or patents that make their cards better than AMD. And you're right: in a lot of benchmarks, AMD is either faster or (at least) better-bang-for-buck than Nvidia.

Yet AMD doesn't have community support in software or cloud adoption or key libraries. The deep learning community has gone for what's easiest to work with and most readily available. Even though you might conceivably get some performance gain from AMD, it's outweighed by the amount of software that's been written in CUDA already, and how quickly that allows you to move.

Nvidia has taken an early lead and jumped on it, while AMD has only fallen further behind. I think one company just fundamentally understands the trend more than the other. But, for what is essentially hardware that answers the question, "how fast can you do matrix multiplication?", library support becomes a key differentiator. And that's where AMD is behind.

The key libraries part has kept me wondering. How much would it cost for AMD to assign a handful heavy duty engineers to this task (writing AMD optimised kernels for convolution etc)?

Their management has been fast asleep for at least 2 years

That is a part of the problem: they assigned people for the task, and produced open-source libraries for matrices, FFT, maybe even something for DNNs. But, those are not polished much, and you have to hunt them down and install them yourself. And, they do a really bad job at marketing.

On the other hand, finding and installing those libraries is nothing compared to actually developing GPU computing software, so, as I said, if you want to program GPUs, even such scattered state of AMD platform is not that worse than Nvidia. Because, in Nvidia, you install CUDA and you get everything set up. And then - what do you do? You still have to learn a not-so-easy black art of optimizing kernels for the actual hardware.

I haven't seen CuDNN equivalents (in terms of perf) for common machine learning frameworks from AMD. I don't think they exist, if they did, people would shift to using AMD.

For NVidia I have seen some faster kernels than the ones supplied by NVidia - https://github.com/NervanaSystems/neon - though CuDNN introduced Winograd kernels too in their last update

I do not know about the quality of this since I do not use NNs, but there is https://github.com/hughperkins/DeepCL and I think I have seen others.

AMD doesn't care about Deep Learning, or ML at all. To quote:

And in the case of the dollars spent on R&D, they seem to be very happy doing stuff in the car industry, and long may that continue—good luck to them. We're spending our dollars the areas we're focused on[1]

To be clear, stuff in the car industry is "just" stuff like self driving cars[2][3].

[1] http://arstechnica.co.uk/gadgets/2016/04/amd-focusing-on-vr-...

[2] https://blogs.nvidia.com/blog/2016/05/06/self-driving-cars-3...

[3] http://www.nvidia.com/object/drive-px.html

> We sure are going up this gradient quickly. But I don't think it's going to get us to a global maximum.

Yes, however, this process has the nice side effect of spurring a huge interest in fundamental AI research, which will lead to alternative algorithms that ultimately perform better.

Wait, how? It seems to me that it gives people an easy way to focus on one area of AI, saying "Guess we didn't need fundamental AI research after all".