Google Announces Prediction API

Slashdot highlights one of the more interesting announcements to come out of the Google I/O developer conference. This particular API is invite only at the moment which may be wise to at least initially throttle its usage. As big as Google’s data centers are, I cannot imagine that does much to constrain the sort of heavy, heavy toll of computation that goes into a system like this.

The Prediction API is a simple to program, supervised learning system. You feed data in and guide the system to learn how to characterize that data. As a result, it is able to extrapolate properties and trends from the data, depending on how you trained it. There are some good example uses on the web site.

  • Language identification
  • Customer sentiment analysis
  • Product recommendations & upsell opportunities
  • Message routing decisions
  • Diagnostics
  • Document and email classification
  • Suspicious activity identification
  • Churn analysis
  • And many more…

The uses are really only constrained by your data. There are a couple of choices of machine learning techniques to tailor analysis and prediction. I am curious as to how such a general services stacks up to more specialized, commercial software like targeted personalization and log analysis for security monitoring. I suspect this may be more useful at the lower end, to gets some idea of what sort of value could be derived from your data via a more in depth analysis.

Regardless, I enjoy the idea of being able to easily harness a simple machine learning system with a small bit of code. Hints at AI on tap if extrapolated out beyond the horizon.

2 Replies to “Google Announces Prediction API”

    1. I think it is going to be complementary. Where the Prediction API drops off, then it looks like you could crowd source better predictions and analysis through Kaggle. MLComp might help those trying to use Google’s service evaluate and chose the best performing learning technique. It could potentially be used on the same data to make that judgment not just on Google’s algorithms but others as well.

      I also had a thought about how it might compare to Wolfram Alpha. Alpha may have superior learning techniques but its data sets are closed. Wolfram might do well to open up submission of data and be open enough to allow comparison through something like MLComp. That is assuming Wolfram himself is justified in his views of how much more advanced his technology is than anyone else’s.

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