Thoughts Regarding the Michael Jordan Interview on IEEE Spectrum

For the few that may not have already seen it Dr. Michael Jordan was interviewed by IEEE Spectrum recently. He offers commentary on a number of topics including computer vision, deep learning, and big data.

Overall I found the article to be an interesting read though it seems to offer little new over what he said on his AMA on Reddit.

Ultimately I find my self agreeing with his position on computer vision. Even given the major strides we have made as of late with convnets and the like we are still far from having a system as capable as we are at vision tasks. After all, the state-of-the-art challenge is the classification of just 1,000 classes of objects in high resolution images. This is a hard problem but it is something that we, humans, and many other animals do trivially.

I am a bit torn about his perspective on deep learning. Notably because of the statement "it’s largely a rebranding of neural networks." I have encountered this idea a couple of times now but I argue that it is not accurate. It is true that neural networks are a favored tool amongst those in the deep learning community and that the strides made in the DL community have been seen while using NNs. But as Bengio et al. note in their forth-coming text called Deep Learning, it "involves learning multiple levels of representation, corresponding to different levels of abstraction." Neural networks have been shown to do this but it has not been shown that they are required to perform such a task. On the flip side, they are out performing other methods that could be used.

Another point that stood out to me were is comments on the singularity. I find myself waffling on this topic and his comments help highlight the reason. Specifically he points out that discussions of the singularity are more philosophical in nature. I rather enjoy philosophy. I often say that if I had another life I would be a mathematician but if I had another one beyond that I would be a philosopher. More so than I am now anyway. I meet so many AI/ML people that think the singularity folks are just crackpots. And if we are being honest, there do seem to be more than a reasonable proportion of crackpots in the community. However that does not prevent us from approaching the topic with sound and valid argumentation. We just have to be prepared to encounter those that cannot or chose not.

Edit 2014-10-23: It appears Dr. Jordan was a bit displeased with IEEE Spectrum interview as he explains in Big Data, Hype, the Media and Other Provocative Words to Put in a Title. The long and short of it appears to be that he believes his perspective was intentionally distorted for the reason that many of my colleagues have been discussing. Namely the title, and arguably the intro, imply much stronger claims than his subsequent comments in the article seem to allude to. As such he he felt the need to clarify his perspectives.

On the one hand I though that a careful critical read of the interview allowed one to pick out his perspective fairly well. But in reading his response there appear to be some things that seem to come across just plain wrong. For instance his opinion about whether we should be collecting and exploring these large data sets. In the interview he makes the great point that we must be cognizant of bad correlations that can and will likely arise. But in the context I did get the impression that he was arguing against doing it all, i.e. collecting and analyzing such data sets, whereas in his response he argues that doing it can be a good thing because it can contribute to the development of principals that are currently missing.

As a side note, I find it interesting that he did not link to the interview but instead gave a link to it. As if to say, let's not lend any more credibility to this article than is absolutely necessary.

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