Not much actually technically-grounded ML discussion occurs on Hacker News.

I somewhat agree. It's more often than not headlines, anecdotes and philosophy about ML. But where is the common denominator high enough to talk about it in a deeper way?

I've visited /r/machinelearning, /r/computervision /r/reinforcementlearning, and although they are often better than HN, they also either partly suffer from the same condition, or are almost dead with no real activity.

TLDR; what are some good places to hang out for good ML discussions and news?

/r/machinelearning is okay.

Twitter has good conversation as well, if you follow all the ML people.

Most researchers are in labs so they usually discuss these things in person with their colleagues. Also the community is relatively small so a lot of discussion happens at conferences and most people are first or second degree connections.

> I've visited /r/machinelearning, /r/computervision /r/reinforcementlearning, and although they are often better than HN, they also either partly suffer from the same condition, or are almost dead with no real activity.

The issue is that most of the people that practice ML don't talk about it in the open (with the exceptions of academics on Twitter). The best discussions I've found happens in the labs or cross-lab email lists.

That's because, AFAIK, no any meaningful problems were solved by this algorithm yet. I'm not even sure it can make a Max(array) on its own.

Yet now it's hyped

Then why's it worth discussion?

Why is there no "general" deep learning algorithm?!

>Why is there no "general" deep learning algorithm?!

Because "deep learning" is almost always just neural networks with many layers and some tweaks to the unit types to alleviate vanishing-gradient issues, trained by stochastic gradient descent. In rather theoretical terms, "deep learning" involves using gradient descent, and gradient-increasing tweaks, to search for a specific continuous circuit in a given space of continuous circuits. It's only as general as the hypothesis class you search through, which here is, again, continuous "circuits" composed out of "neuron" units of specific types, numbers, and arrangements.

Now, in computability terms, given a large enough (continuous or discrete) circuit, possibly a recurrent one, you can represent any computable function. However, in learning terms, that doesn't make a useful computable function at all easy to find in a very-high dimensional space.