Like every so-called basic science. 1st batteries, radio, lasers etc also looked like that.

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.

There is a reason why "Research" and "Development" are listed as separate concepts when people talk about R&D. The point of most research papers is not to directly invent a working product, it's to produce fundamental insights that may be useful in future applications.

See my previous comment: not in machine learning research.

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.

It really is a great paper, the problem is when your entire field has few real applications but everyone is making too much money to admit that. Cool tech demos like this is awesome as a blog post, but not sure why it's academic...

Well, the paper has an experiment where they train an agent based on their system to play two of the three games (PacMan, PacManMaze and VizDoom) and compare its result to two other systems (an LSTM-based one and a model-based RL one, WorldModel). Their system gets the best score in Pacman but unfortunately the version that does better than WorldModel is the one that doesn't have the memory module whose usefulness is a central claim of their paper. WorldModel outperforms their system in VizDoom by ~300 points, though their system without the memory module "solves" VizDoom (i.e. scores just a bit over than 750; that is 765) and they claim that it's the first Gan-based system that achieves this.

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.

I don't know where you got the idea that the purpose of an academic paper is to produce the schematics for a working commercial product. The fact that it's novel and interesting is sufficient grounds for producing a paper about it. Why do you think projects like these are solely the domain of blog posts?

That is not true for academic papers in computer science and particularly in machine learning were contributions are expected to have some sort of practical application that is at least possible to foresee.

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".

> That is not true for academic papers in computer science and particularly in machine learning were contributions are expected to have some sort of practical application that is at least possible to foresee.

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.

Well of course, it's not 100% true- who said anything about 100%? There are theoretical branches of computer science; but most of computer science is not purely theoretical. Certainly machine learning (and more broadly speaking, much of AI) is like I say.

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?

A lot of the ML techniques we're putting to use today came out of papers from the 80s. These techniques weren't practical to do much beyond toy examples, but they were proven to work. Now, we're able to use those very techniques to teach a machine to identify cat videos with 75% accuracy using unsupervised learning! [0]

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[0]: https://www.wired.com/2012/06/google-x-neural-network/

A great sales pitch for data-center GPU's