One of the important reminders from game four of Alpha Go vs. Lee Sodol was the difference between what computers and humans are each best at.

Traditionally, computers were best at the most repetitive tasks, that were well understood and could be completely described.

If you talk to any release or test engineer, they will tell you that once you can fully describe a process, it’s only a few more steps to be able to automate it.

What makes Machine Learning so tantalizing is that it’s been giving hints of being able to learn to perform not-fully-described tasks, most recently Go.

At the same time, Machine Learning still requires thousands or millions of examples in order to be able to ‘see’ things, whereas humans can understand and make inferences with many fewer examples. It’s unclear to me (and I’m guessing most people) exactly why this is. It’s like there’s something about the way we learn things which helps us learn other things.

But back to the topic at hand. What game four showed us (yet again) is that the better defined the problem, the better humans perform vs. computers.

A different example of this is how high paid market research analysts are being replaced by automation, doing in minutes what would take the analysts days.

So, how do you stay relevant as things become more and more automated and automateable?

As Lee Sedol showed, one strategy is to play Calvinball[1]. Find the part of your discipline that is the least defined, and pour yourself into pushing that boundary, leaving defined pieces in your wake[2].

Note: Playing Strategema like Data is another ‘fun’ option[3], but most useful only when playing against a computer opponent, not so much for forging your own path. It consists of playing sub-optimal moves so as to confuse or anger the other player, to thrown them off balance. It is postulated that Deep Blue did this to Kasparov.

[1]Calvinball is a mostly fictional game invented by Bill Watterson for Calvin and Hobbes. The game has only one rule, that it can never be played the same way twice.

[2]Technically, Lee Sedol played a very ‘loose’ game, which was difficult to define, where parts of the board far away from each other were more easily related. You can also use this tactic to find things and do them in a way where humans are better than computers.

[3]We called this ‘victory through annoyance’ during undergrad. It had mixed reviews.