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Why integers and floating point are necessary

Integer type support is needed for predictive analytics. This is far bigger than stencil kernels and filters.

Today, most unstructured data that is interesting (i.e. computationally tractable) is text. This is really integers. However, classification and regression of this integer data requires floating point for the scoring functions. What’s really going on is the integral text data is mapped into a vector space over the real numbers using a kernel trick. Then statistical learning happens there using the only complete theory we have: linear algebra.

Machine learning technology as culture:

  1. neural networks – before computers were connected in a global network called the Internet
  2. statistical learning theory – in the age of MapReduce
  3. predictive analytics – in the age of apps

The Wikipedia says, “Technology can be viewed as an activity that forms or changes culture.” This is the right way to view machine learning. It is defined by the relationship with society.

We are structuring our societies around markets for labor. When I first heard of Amazon Mechanical Turk, I thought it was twisted genius. “Artificial artificial intelligence,” said Jeff Bezos. Human intelligence is packaged and given value through a market.

I believe this cultural view of intelligence is not limited to people. It extends to machines too. If we restructure society so human activity revolves around machines, then machine activities revolve around people.


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