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Machine learning is really good at partially solving just about any problem

From cdixon.org

There’s a saying in artificial intelligence circles that techniques like machine learning (and NLP) can very quickly get you, say, 80% of the way to solving just about any (real world) problem, but going beyond 80% is extremely hard, maybe even impossible.  The Netflix Challenge is a case in point: hundreds of the best researchers in the world worked on the problem for 2 years and the (apparent) winning team got a 10% improvement over Netflix’s in-house algorithm.  This is consistent with my own experience, having spent many years and dollars on machine learning projects.

This doesn’t mean machine learning isn’t useful – it just means you need to apply it to contexts that are fault tolerant:  for example, online ad targeting, ranking search results, recommendations, and spam filtering.  Areas where people aren’t so fault tolerant and machine learning usually disappoints include machine translation, speech recognition, and image recognition.

That’s not to say you can’t use machine learning to attack these non-fault tolorant problems, but just that you need to realize the limits of automation and build mechanisms to compensate for those limits.  One great thing about most machine learning algorithms is you can infer confidence levels and then, say, ship low confidence results to a manual process.

A corollary of all of the above is that it is very rare for startup companies to ever have a competitive advantage because of their machine learning algorithms.  If a worldwide concerted effort can only improve Netflix’s algorithm by 10%, how likely are 4 people in an R+D department in a startup going to have a significant breakthrough.  Modern ML algorithms are the product of thousands of academics and billions of dollars of R+D and are generally only improved upon at the margins by individual companies.

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