As someone in the deep learning space who's not working at one of those companies (deep learning on hadoop with cloudera/red hat model is my game) I'd like to add here that while these labs are doing stuff with ads, it's not necessarily all that they are focused on.

I've visited a few of the teams and I'd like to say that many of the things being published out of there (say the recent real time vision stuff with cars published by yann lecun's group) or perhaps the voice stuff coming out of baidu.

That being said: I think you're ignoring the structure of the data that are involved with click through rates and the like.

Reconstructing sparse data and reccommenders is among the hardest problems in AI. Couple that with the at scale unstructured data(being media like text, audio) being used and we can see some real landmark results.

Real value is being created alongside some of the commercial research being done.

As for use cases of deep learning outside of ads, my customers are working on many cool problems that don't involve targeting. I've done training as well as deployments with a variety of companies now. Media centric products coupled with users are among some of the coolest datasets out there. Let me say that search and similarity among data is by far the best application of this stuff outside of yet another conv net classifier.

There's a practical side to this stuff that doesn't involve ads. I hope that helps a bit.

> There's a practical side to this stuff that doesn't involve ads.

Just have a look at the AIMA book (http://aima.cs.berkeley.edu/), it's full of applications not related to advertising (both of its authors, Russell and Norvig, have signed the letter). I'm going to implement many of them in my blog (http://ai-maker.com/), so if you want to have a lot of fun with AI, join me in this learning quest.

I did most of my first homework assignments out of that book a lot of years ago ;). I'm actually writing my own book on deep learning with oreilly now. Plenty to do ;)

I'd the thrilled to hear your story with AI and OReilly. It must be very encouraging to have this project with such an important editor.

Email's in profile.

I think you're misunderstanding something fundamental about how AI research works. The techniques, tools and ideas can be applied to anything by anyone. If google releases a deep learning framework, it can be used for cancer research or economic planning or anything else. Just because it was developed in the context of advertising doesn't mean that the applications are limited to that domain.

The idea that "some of the greatest minds of our generation are spending their time figuring out how to get people to click on more ads" is a bit of a myth. They're figuring out new algorithms, faster methods and new technological concepts that are being applied to ads first. The truth is that the research divisions of Google, Microsoft, IBM, etc publish a lot of research that isn't related to advertising at all, possibly the majority of what they publish. The best and brightest engineers are interested in engineering and theory, and despite working for advertising companies they tend to come up with good ideas that can be used in a wide variety of applications.

And, I think you're overly optimistic about the generality of some of these problems and the effort going into them.

I believe a significant portion of the implementation effort is going toward more effectively delivering ads and consumption, whether they're also writing a paper about indirectly related research or not. Google's most profitable business, by far, is their ads business. They would be crazy to not expend significant money and effort to retain their lead in that space. Other stuff that comes out of it (as nice as often it is, and as much as I enjoy it) is often just a pleasant side effect.

But, I could be wrong. I would also like to point out that I don't have other ideas on funding AI or big data research. I'm just uncomfortable with it, though I don't expect others to necessarily share that discomfort.

It's also going into surveillance, warfare and predictive policing etc. I believe this is mainly because only big companies or deep pocketed individuals have real access, but there could come a "golden age" some time later where the general public can access truly powerful API's and the like, and then we would see a whole new frontier emerge. That is, if they can generalize the algorithms and make a workable product that doesn't need so much manual training and oversight by the engineers creating the AI.

The problem with AGI research is that there have, to date, been few ways to survive as an AGI researcher. So once the commercial platforms became available to apply machine learning/AI whatever you want to call it, there was a way to continue Narrow AI research projects with the possible hope that maybe someday these narrow approaches would help lead to AGI.

In fact the whole AGI/HLAI/Strong AI thing has gotten a bad rap over and over that the majority of super serious researchers won't even talk about it in any seriousness a la Norvig et al. In fact Norvig arguably openly mocks AGI focused groups.

So until there is a real interest from some party that has money to put several billion into hardcore research, we will continue to see AI development live largely in the commercial world. Open source is a possibility like with opencog but history indicates that the bulk of the "big" minds and money don't go that way.

It must be hard to resist a big payslip, we're all humans, heads of family, we want to provide the best for our beloved ones.

I see a veneer of disdain to Norvig's attitude in your words. I don't know much about this criticism, but to me he has done a lot of good to us researchers with his AIMA book. In fact, this is the core that vertebrates my blog: http://ai-maker.com/. In the end, Goertzel (guru in AGI) works again for a financial prediction firm, doesn't he?

I believe this article to o be relevant here:http://m.theatlantic.com/magazine/archive/2013/11/the-man-wh...

I very much share your sentiment. The research unfortunately seems to be done where there is most commercial drive for it, which nowadays means tricking people into parting themselves with their money.

> I consider this dangerous, in a way that I can't really put my finger on. Is it merely that some of the best minds in the field are being distracted by selling more crap that people don't need? Or is it that AI will have at its developmental core a model of inducing consumption as its prime directive? Or, am I just an asshole that doesn't like too many things?

http://slatestarcodex.com/2014/07/30/meditations-on-moloch/

This is long, but very, very good and may clear up some confusions.

I think the danger lies in our tendency to ignore the big picture. We work in teams and groups and we create these bubbles where everyone just assumes that a basic set of things has been figured out by other people. You can second-guess what's going on around you, but it's not your job to test if the entire thing makes any sense at all (that recent CIA report might be an example of this). If you work at some tech giant doing AI research, you focus on the next task. Even asking something like "should we really be gathering that info about customers?" will either get you fired or bullied (I imagine) - because that's some lawyer's job, or your boss's, or even that of your country's legislator. Who are you to doubt the direction your entire field is going, anyway?

Some of the best AI researchers working on this problem probably won't stop the community from pushing towards it. The majority of people in general is happy with just doing their job.

Lots of innovation in the early days of the internet came from porn. Whatever you may think of porn, I think those innovations have arguably had wider benefits than just the porn industry. Similarly, AI originally built to maximize ad clicks and online purchases might eventually be applied to more "noble" causes.

I'm put in mind of a story about this I read... presumably about two years ago: http://www.kaleidotrope.net/archives/autumn-2012/dead-mercha...