Peter Norvig of Google likes to say that for machine learning, you should “worry about the data before you worry about the algorithm”.

Rather than argue about whether this algorithm is better than that algorithm, all you have to do is get ten times more training data. And now all of a sudden, the worst algorithm … is performing better than the best algorithm on less training data.

It’s a rallying cry taken up by many, and there’s a lot of truth to it.Â Peter’s talk here has some nice examples (beginning at 4:30). The maxim about more data holds over several orders of magnitude. For some examples of the power of big-data-simple-algorithm for computer vision, check out the work of Alyosha Efros’ group at CMU.Â This is all pretty convincing evidence that scale helps. The data tide lifts all boats.

What I find more interesting, though, is the fact that we already seem to have reached the limits of where data scale alone can take us. For example, as discussed in the talk, Google’s statistical machine translation system incorporates a language model consisting of length 7 N-grams trained from a 10^12 word dataset. This is an astonishingly large amount of data. To put that in perspective, a human will hear less than 10^9 words in an entire lifetime. It’s pretty clear that there must be huge gains to be made on the algorithmic side of the equation, and indeed some graphs in the talk show that, for machine translation at least, the performance gain from adding more data has already started to level off. The news from the frontiers of the Netflix Prize is the same – the top teams report that the Netflix dataset is so big that adding more data from sources like IMDB makes no difference at all! (Though this is more an indictment of ontologies than big data.)

So, the future, like the past, will be about the algorithms. The sudden explosion of available data has given us a significant bump in performance, but has already begun to reach its limits. There’s still lots of easy progress to be made as the ability to handle massive data spreads beyond mega-players like Google to more average research groups, but fundamentally we know where the limits of the approach lie. The hard problems won’t be solved just by lots of data and nearest neighbour search. For researchers this is great news – still lots of fun to be had!