> It's really a sad comment on the state of reporting at MIT Tech Review that..

I feel compelled to point out that the only connection between the "MIT" tech review and MIT is that the magazine licenses the name from the alumni association. It's how the alumni associations funds itself and every MIT grad gets a lifetime subscription to a version of the magazine with the alumni notes bound into the back. I doubt many of us read it. I don't know how many people other than MIT grads read it, but I would imagine vanishingly few.

A friend of mine calls it "the magazine of things that will never happen" which I think is dead on. It's a shame because the editor, Jason Pontin, as actually a good guy so it's surprising that the magazine continued to suck after he took it over.

There are many reasons to criticize MIT (don't I know it!) but you can't judge the institute by this magazine.

I'm going to disagree a bit here. Tech Review does tend to focus on the possibilities of technology and to highlight potentially exciting research. Almost by definition, a lot of this stuff is never going to amount to anything commercially interesting. I suppose that TR could insert more implicit or explicit disclaimers to that effect but I find it a good source for insights into what's going on in the labs.

Personally, I think that Jason has brought a lot of positive changes to a magazine that, for a long time, tended toward a technology policy wonkish orientation.

So I think it's fair that a lot of what's written about "will never happen." But I'm not sure that's really avoidable if you cover cutting-edge research.

I like your comments and I am all about holding Tech Review to a high standard, but I think I am going to side with them on this. The key part is "from scratch". I'd venture that there are lots of AI projects that similarly relevant precursors to DeepMind, all of which (including your Backgammon examples) do not actually accomplish the same from-scratch abilities described.

The thing about Tesauro's backgammon work that excited the community is that the system trained by playing itself (http://webdocs.cs.ualberta.ca/~sutton/book/ebook/node108.htm... -- "To apply the learning rule we need a source of backgammon games. Tesauro obtained an unending sequence of games by playing his learning backgammon player against itself.").

Also, it didn't use an elaborate set of features and heuristics adapted for backgammon, just a simple representation of the state of the board (a list of 0/1 variables encoding how many pieces of each color are on each position).

This is pretty close to "from scratch", and I think the article would have done well to point out what is actually new here.