Making sense of machine learning
As Matt Asay observed last week, AI appears to be reaching “peak ludicrous mode,” with almost every software vendor laying claim to today’s most hyped technology. In fact, Gartner’s latest Hype Cycle for Emerging Technologies places machine learning at the Peak of Inflated Expectations.
Hang on — see what I did there? I used “AI” and “machine learning” interchangeably, which should get me busted by the artificial thought police. The first thing you need to know about AI (and machine learning) is that it’s full of confusing, overlapping terminology, not to mention algorithms with functions that are opaque to all but a select few.
This combination of hype and nearly impenetrable nomenclature can get pretty irritating. So let’s start with a very basic taxonomy:
Artificial intelligence is the umbrella phrase under which all other terminology in this area falls. As an area of computer research, AI dates back to the 1940s. AI researchers were flush with optimism until the 1970s, when they encountered unforeseen challenges and funding dried up, a period known as “AI winter.” Despite such triumphs as IBM’s 1990s chess-playing system Deep Blue, the term AI did not recover really from its long winter until a few years ago. New nomenclature needed to be invented.
Machine intelligence is synonymous with AI. It never gained the currency AI did, but you never know when it might suddenly become popular.
Machine learning is the phrase you hear most often today, although it was first coined in the 1950s. It refers to a subset of AI in which programs feed on data and, by recognizing patterns in that data and learning from them, execute functions or make predictions without being explicitly programmed to do so. Most of the recent advances we hear about fall under the rubric of machine learning. Why is it so hot today? You often hear that Moore’s Law and cheap, abundant memory have given new life to old machine learning algorithms, which have led to a wave of practical applications, particularly relating to pattern recognition. That’s true, but even more important has been the hyperabundance of data to enable machine learning systems to learn.
Cognitive computing has been the phrase preferred by IBM and bestowed on its Jeopardy winner Watson. As best as I can determine, cognitive computing is more or less synonymous with AI, although IBM’s definition emphasizes human interaction with that intelligence. Some people object to the phrase because it implies human-like reasoning, which computer systems in their current form are unlikely to attain.
Neural networks are a form of machine learning dating back to early AI research. They very loosely emulate the way neurons in the brain work — the objective generally being pattern recognition. As neural networks are trained with data, connections between neurons are strengthened, the outputs from which form patterns and drive machine decisionmaking.
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