Machine Learning already matters
Posted by Cheng Soon Ong on June 20, 2012
"Much of machine learning (ML) research has lost its connection to problems of import to the larger world of science and society." So begins Kiri Wagstaff's position paper that will have a special plenary session on June 29 at ICML 2012. The paper then goes on to lament about the poor state of affairs in machine learning research. The paper is an interesting read, and it addresses an important question that any adolescent field faces: "How do I justify my existence?"
I'd like to take the half full glass view. Machine Learning already matters!.
Kiri herself uses examples that show that machine learning already has impact. In her introduction, she mentions the CALO project, which forms the basis of Siri on the iPhone 4S, which has revolutionised the way the general public perceives human computer interactions. She also mentions spam detection, which Gmail has generalized to sorting all email with Priority Inbox.
A quick look around the web reveals other success stories:
The recent technology quarterly section of the Economist 2 June 2012 edition discusses the use of robots and how we would need to start legislating them. Ironically, in our human desire to appropriate blame in case of failure, we may have to block learning. Quoting the article: "This has implications for system design: it may, for instance, rule out the use of artificial neural networks, decision-making systems that learn from example rather than obeying predefined rules."
Searching for the phrase "machine learning" in PLoS Computational Biology returns 250 hits, showing how machine learning has revolutionised biological research in the high throughput age.
At NIPS 2008 at the last talk of the Machine Learning in Computational Biology mini-symposium, I had the pleasure to be inspired by Thomas Lengauer's activities proposing anti-HIV therapy. I'd say that this "solves" challenge number 5 in Kiri's list. Remarkably (unfortunately?), their recommendation site, remains just that, a recommendation site, and has yet to navigate the legislative nightmare of getting a website to prescribe drugs. In an answer to a question, he said that Germany was one of the few places in the world where the legislation even allows for doctors to use such drug recommendation sites. A scan of the titles cited by the review article reveals keywords which would fit comfortably in a machine learning venue:
- multiple linear regression - simple linear model - prediction-based classification - artificial neural networks - self organising feature maps - non-parametric methods - sparse models - convex optimization
But doom and gloom persists. Why? My personal opinion is that like most successful technologies, machine learning fades into the background once it has impact. In that vein of thought, we can measure the impact of machine learning by the decline of ICML, JMLR and friends. Meanwhile, I'm going to go back to making machine learning disappear...
Please join in the discussion at http://mlimpact.com/.
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