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      IS AI SO GREAT IF IT'S SO EXPENSIVE?

      As you are an astute businessperson, you are asking whether the investment is worth the effort. After all, this is experimental stuff and Google is still trying to teach a car how to drive itself.

      Christopher Berry, Director of Product Intelligence for the Canadian Broadcasting Corporation, puts the business spin on this question.7

      And heaven knows, we have plenty of well‐paid people spending a great deal of time doing incredibly routine work.

      So machine learning is great. It's powerful. It's the future of marketing. But just what the heck is it?

      WHAT'S ALL THIS AI THEN?

      What are AI, cognitive computing, and machine learning? In “The History of Artificial Intelligence,”8 Chris Smith introduces AI this way:

      In brief – AI mimics humans, while machine learning is a system that can figure out how to figure out a specific task. According to SAS, multinational developer of analytics software, “Cognitive computing is based on self‐learning systems that use machine‐learning techniques to perform specific, humanlike tasks in an intelligent way.”9

      THE AI UMBRELLA

      We start with AI, artificial intelligence, as it is the overarching term for a variety of technologies. AI generally refers to making computers act like people. “Weak AI” is that which can do something very specific, very well, and “strong AI” is that which thinks like humans, draws on general knowledge, imitates common sense, threatens to become self‐aware, and takes over the world.

      We have lived with weak AI for a while now. Pandora is very good at choosing what music you might like based on the sort of music you liked before. Amazon is pretty good at guessing that if you bought this, you might like to buy that. Google's AlphaGo beat Go world champion Lee Sedol in March 2016. Another AI system (DeepStack) beat experts at no‐limit, Texas Hold'em Poker.10 But none of those systems can do anything else. They are weak.

      Artificial intelligence is a large umbrella. Under it, you'll find visual recognition (“That's a cat!”), voice recognition (you can say things like, “It won't turn on” or “It won't connect to the Internet” or “It never arrived”), natural language processing (“I think you said you wanted me to open the garage door and warm up your car. Is that right?”), expert systems (“Based on its behavior, I am 98.3 % confident that is a cat”), affective computing (“I see cats make you happy”), and robotics (I'm acting like a cat).

      THE MACHINE THAT LEARNS

      The magic of machine learning is that it was designed to learn, not to follow strict rules. This is the most fundamental aspect to understand and the most important to remember when you hit that inevitable frustration when things start going slightly off‐track. A rules‐based system does exactly what it's told and nothing more. We are comforted by that. A command to send out a gazillion e‐mails with the “<first_name>” after the salutation does precisely that. That's good.

      Of course, when the database has something fishy in the first_name field, then somebody gets an e‐mail that begins, “Hello, Null, how are you?” Once humans know to look for those sorts of mistakes, we create processes to check and correct the data before hitting Send the next time.

      When a batch of e‐mails goes out that all say, “Hello, <first_name>, how are you?” and the e‐mails all include those brackets and that underline, we know to flail the programmers until they find the errant semicolon that caused the problem. In both cases, we can backtrack, find the problem, and fix it.

      Machine learning has data and is given a task. It performs the task and waits for feedback. If the feedback is positive, it tries to duplicate the same actions the next time for a similar task. If the feedback is negative, it requires information on where it went wrong.

       Guess the Animal

      In 1980, at the Personal Electronics store in Santa Barbara, California, I was shown a program that could guess the animal you had in mind. I was flabbergasted. It was a simple case of Asimov's “Any sufficiently advanced technology is indistinguishable from magic.”