I don't see that trend. Bleeding edge research has always been beyond what's practical, by its very nature. The focus is on pushing the boundaries, not to build something efficient/commercial.
It's the followup work that makes it practical. See for example the evolution of WaveNet. It was breakthrough research, but yeah it wasn't practical. Now a couple years later and we're generating speech on mobile devices of similar quality (see the recent Facebook research), and able to teach these machines using orders of magnitude less data (see 15.ai).
Not to mention the plethora of research that is explicitly about making neural networks more efficient and easier to train (e.g. EfficientNet; 1cycle; etc).
Bleeding edge stuff like AlphaStar, BigGAN, GPT-2, etc are just that: bleeding edge. They'll get more efficient and practical every year, just like everything else in machine learning.
Development always starts with some novel technology that has major flaws, but some good nuggets. Then it is further developed to be practical.
But eventually we reached the point where they are not only competitive, they may be positioned to fully overtake combustion cars.
I wonder how many discoveries on electric cars ended up being fully dead ends along the path that took us to the point we are now?
The learning part is done by training on the output of an existing simulator. Obviously, if you already have the output of a simulator, you don't need to learn a simulator so the only real use of this approach is to train a simulator by watching a human agent interacting with the environment. So the idea is to eventually apply this thing to training robots to interact in the real world by watching how humans do it (this is not clearly stated in the paper; it's my charitable interpretation of the very poor motivation of learning a simulator from the output of an existing simulator).
The only problem is that creating an accurate simulation of a human's interaction with the real world (let alone an accurate simulation of the real world) is prohibitively expensive. Thus, for the time being, this kind of idea works only in very, very simple environments like PacMan.
So we don't yet have an expensive prototype electic car that nevertheless can do everything an er other kind of car can do. We have... not sure what. The description of an idea with an example of how such a thing could be achieved in the far future, when we can train robots to act like humans by watching us.
thinking back on the PC, Web, cloud, and mobile growth periods, each came with a transformative change in the capabilities, cost, and availability of computing power. We're only seeing two of these aspects in AI today - with the cost side heading in the wrong direction.
Pushing boundries != commercialization potential
The english longbow was vastly superior to the first guns that came out, which were slow to load, unreliable to shoot, didn't work in the rain, and were extremely inaccurate.
3d printed stuff sucks. It's not very strong, it's not very pretty and takes a long time to create.
Meanwhile, take a look at this ML photo processing technique:
In my field we make robots do a thing, say folding laundry, as an example of dextrous manipulation[1], and the popular conversation is about whether your laundry robot makes commercial sense and not about your novel grasping strategy and nifty motion planner that makes this sorta-possible for the first time. But everyone sees your video. Pros and cons.
[1] laundry folding at Berkeley (not me) https://youtu.be/gy5g33S0Gzo
B) This specific example actually shows ML toolchains are orders of magnitude better, than existing ones. Imagine you wanted to make a football game from scratch, and this technique would let you do that by just feeding a lot of football game recordings to a program, as opposed to hiring software devs and graphics designers, and iterating for a year.
This isn't too bad of a situation if this GPU provides 100% of your value proposition. but, if you needed your product to perform 5 such tasks - or competition/customer demand forces you to move to a bigger model with a 10 words per second inference rate then you may find it hard to balance customer value vs. price vs. margin.
https://twitter.com/br_/status/979442438254166016
> "selling AWS at a loss" is crisp shorthand for a lot of startups' business models!
Just 10000x? More like 6 orders of magnitude
True, though my first thought was that this would be interesting from a reverse engineering point of view. Then again Pacman is pretty simple concept. Something worth reverse engineering is probably a lot more complex.
I see a strong parallel. It's a poor performance but important proof of concept.
Also, it's general in computing trend since assembly. Why compile when you can write machine code, waste of resources. Why have abstract compiler, when you can write assembly. Why have higher level language when it cant generate code optimal for target when it doesnt know it. Then web came, it's not faster than 20 years ago because with increased computing power bloat came. Somehow it always turns that seemingly wasteful apporach outweights its cons. Empirically lowered entry cost and increased applicability has very very high value. At least higher that I used to assess.
Also, the paper itself tries (hard) to motivate its approach with a real-world application, in particular, learning a simulator to train robotic agents to interact with the real world.
A bit thin of the ground (especially when it comes to supporting the claim about the usefulness of the memory module) but the way neural nets research and especially RL research is going (from what I've seen, not my area of expertise) that's the kind of result that you can expect will get your paper published these days.
Indeed, a common criticism against papers in machine learning is "what is a real-world application of this approach?". If you can't answer that convincingly then it's unlikely your paper gets published, particularly if it has rather mediocre results otherwise, as is the case with this paper (see my earlier comment about the comparison to the two other systems which is a bit of an anticlimax).
In fact, the paper (well, the preprint version) makes a clear attempt to motivate the work with a practical application, saying that it aims to "learn a simulator [for robotic agents] by simply watching an agent interact with an environment".
This is 100% not true for computer science and I don't know why you think it's true. There's are entire branches of computer science (e.g. complexity theory) that are highly theoretical and are certainly not geared towards immediate practical application.
Even in more applied branches of computer science like ML there are many papers that are geared more towards the fundamental research side of things.
> saying that it aims to "learn a simulator [for robotic agents] by simply watching an agent interact with an environment".
Saying that a paper could lead to a useful application down the line is not the same that saying that the paper is intended to describe the schematics for a specific useful application.
I'm not sure who brought up a schematic? The bit I quoted is taken from the paper and it's a clear attempt to motivate the paper's approach as having a practical application. To clarify, do you disagree with that?
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Sure.
> That is essentially a kind of consciousness.
That is a flying leap.
It just means some people poked a pile of a bazillion if-else statements long enough, until the desired result came out. I believe everyone here knows the xkcd I am referring to.
Still, I think it's an interesting result. But, as someone on Fridman's podcast said the other day: recent advances in AI have all been thanks to advances in computing power, not any new ideas. The future if AI seems to depend on Moore's Law, more than anything.
A) It's no longer practical to incorporate into real products given the hardware costs for training and inference. In NLP, large transformer based models can easily occupy a 5 thousand dollar GPU at a paltry 50-60 words per second processing pace.
B) The Research often demonstrates things that existing non-ML toolchains were already reasonable at doing with a better result, and a faster iteration time.
This demo looks like it's replicated the core mechanics of pac-man with worse graphics, 3-4 orders of magnitude higher resource consumption, and undoubtably many odd bugs hidden away from the pretty demo. It even needed the game to exist in the first place! While this is fascinating from the technical and academic perspective, it's hard to be convinced that any gains we see in the field won't be offset by increased hardware/latency costs from a product standpoint.