[AndrewKemendo]

This is not a research paper. In fact, most ML papers aren't research papers. Compare the FB paper to the first result in biorxiv under the genetics heading [1]. There are basically no similarities other than being done in LaTeX. I never expect a research paper to talk about how the research affected business processes, but again this isn't research in any traditional sense.

What this is, is documentation of how Facebook implemented a technology stack that uses reinforcement learning techniques to do something. Namely: "Notifications at Facebook"

So what can other developers and business owners take from this? I don't see anything about the down stream product impacts. Does it impact conversion to paid rate for users? Does it reduce human labor? How does it improve benefits to users. All I see them write are two things:

"We observed a significant improvement in activity and mean- ingful interactions by deploying an RL based policy for certain types of notifications, replacing the previous system based on supervised learning."

I'm sorry but there is absolutely nothing rigorous in that statement. How are "meaningful interactions" defined? Hopefully they aren't still arguing the formula (more interaction = makes users better off).

"After deploying the DQN model, we were able to improve daily, weekly, and monthly metrics without sacrificing notification quality."

Improve for who? Well obviously Facebook and how much activity people have. Not necessarily if the user is actually getting more value from it.

What's the Return on Investment for this system?

Listen, I'm a huge fan of being open with business practices, research etc...I'm also obsessive about RL and making progress in the field.

What I can't stand however is lack of rigorous and tangible proof of how we're making things better for users or the society broadly with RL yet, or even in most cases getting positive ROI for the effort we're putting into ML/DL.

I've built these tools at scale so it hurts to say this, but the economics just aren't yet lining up here across the entire ML/DL industry and that has me worried that another AI winter is coming.

[1]https://www.biorxiv.org/content/early/2018/11/01/422345

[ma2rten]

This kind of paper which describes a system is not uncommon in computer science. It's a way for future papers which use the system to cite it.

For example the scikit learn paper: http://www.jmlr.org/papers/volume12/pedregosa11a/pedregosa11...

[AndrewKemendo]

Indeed! This is an endemic issue in the computer sciences.

I don't think it's a bad way to approach knowledge dissemination by the way, it is however indicative of the problem of reprouducability and explainability in AI broadly.

I bring this point up simply to be another voice stating that we need more rigorous methodology in AI research if we are going to make advances that are focused first on knowledge, rather than primarily applicability of technology.

Way back before AI was cool, there was a good paper on this [1] that is very relevant to today.

Quoting from the abstract:

"There are two central problems concerning the methodology and foundations of Artificial Intelligence (AI). One is to find a technique for defining problems in AI. The other is to find a technique for testing hypotheses in AI. There are, as of now, no solutions to these two problems. The former problem has been neglected because researchers have found it difficult to define AI problems in a traditional manner. The second problem has not been tackled seriously, with the result that supposedly competing AI hypotheses are typically non-comparable."

[1]https://link.springer.com/chapter/10.1007/978-1-4471-3542-5_...