Figure 1.1 A McKinsey survey finds advertising and marketing highly ranked for disruption.
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
Look at the progress that Google has made in terms of its self‐driving car technology. They invested years and years and years in computer vision, and then training machines to respond to road conditions. Then look at the way that Tesla has been able to completely catch up by way of watching its drivers just use the car.
The emotional reaction that a data scientist is going to have is, “I'm building machine to be better than a human being. Why would I want to bring a machine up to the point of it being as bad as a human being?”
The commercial answer is that if you can train a generic Machine Learning algorithm well enough to do a job as poorly as a human being, it's still better than hiring an expensive human being because every single time that machine runs, you don't have to pay its pension, you don't have to pay its salary, and it doesn't walk out the door and maybe go off to a competitor.
And there's a possibility that it could surpass a human intelligence. If you follow that argument all the way through, narrow machine intelligence is good enough for problem subsets that are incredibly routine.
We have so many companies that are dedicated to marketing automation and to smart agents and smart bots. If we were to enumerate all the jobs being done in marketing department and score them based on how much pain caused, and how esteemed they are, you'd have no shortage of start‐ups trying to provide the next wave of mechanization in the age of information.
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:
The term artificial intelligence was first coined by John McCarthy in 1956 when he held the first academic conference on the subject. But the journey to understand if machines can truly think began much before that. In Vannevar Bush's seminal work As We May Think (1945) he proposed a system which amplifies people's own knowledge and understanding. Five years later Alan Turing wrote a paper on the notion of machines being able to simulate human beings and the ability to do intelligent things, such as play Chess (1950).
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.”
It: Think of an animal…
Is it an elephant? (y or n)
Me: n
It: You win. Help me learn from my mistake before you go … What animal were you thinking of?
Me: a rabbit
It: Give me a question to distinguish a rabbit from an elephant.
Me: Is it a small animal?
It: For a rabbit, what is the answer to your question? (y or n)
Me: y
It: Thanks.
Play again? (y or n)
Me: y
It: Think of an animal …
Is it a small animal? (y or n)
Me: y
It: Is it a rabbit? (y or n)
Me: n
It: You win. Help me learn from my mistake before you go … What animal were you thinking of?
Me: a frog
It: Give me a question to distinguish a frog from rabbit and an elephant.
Me: Is it green?
It: Thanks.
Play again? (y or n)
Me: No, I see how this works now.
It: I don't understand.
Play again? (y or n)
Me: n
It: