September 12, 2016
PUBLISHED BY Geoffrey Moore
The rise of cloud computing brings with it the promise of infinite computing power. The rise of Big Data brings with it the possibility of ingesting all the world’s log files. The combination of the two has sparked widespread interest in data science as truly the “one ring to rule them all.” When we speculate about such a future, we tend to use two phrases to describe this new kind of analytics—artificial intelligence (AI) andmachine learning. Most people use them interchangeably. This is a mistake.
AI develops conceptual models of the world that are underpinned by set theory and natural language. In this context, every noun or noun phrase represents a set. Every predicate implicates that set in other sets. If all human beings are mortal, and you are ahuman being, then you are mortal. It’s an exercise in Venn diagrams. By extending these diagrams through syntax, semantics, and analogy, human beings build up conceptual models of the world that enable us to develop strategies for living. AI seeks to emulate this capability in expert systems.
Results in this area to date have been mixed and overall have fallen far short of the aspirations and projections of earlier decades. The problem is that natural language sets are inherently fuzzy at the edges. As a result, set algebra that works very well when applied to things that are near the centerpoint of a set becomes increasingly challenged as set elements approach their boundary conditions. Olympic sports clearly includes track and field and swimming. Does it include badminton? ping pong? golf? baseball? bowling? billiards? chess? Any claim you make about Olympic sports must be increasingly modified as you expand the aperture of your focus to accommodate more and more borderline cases. Ultimately such a system simply cannot scale. It can still be incredibly valuable, particularly in constrained domains like medical diagnosis, but it is not the ring we are looking for.
Machine learning is. Or at least it appears to be at the present time. Unlike AI which seeks to understand the world through conceptual models, machine learning has no such interest. It does not understand anything at all, nor does it want to. That’s because it does not seek emulate human intelligence, it seeks to simulate it. It does so through sheer brute mathematical force. Basically, any digital thing you present to a machine learning engine—say, a photographic image or a body of text—is converted into a string of integers, and everything that happens after that is some type of mathematical manipulation of that string. In the world of machine learning you and I really are just numbers.
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