I recently read “The Second Machine Age” by Erik Brynjolfsson and Andrew McAfee. The major points the book makes—that information technology and digitization is exponential, that automation is rapidly expanding into even cognitive tasks that until recently were thought doable only by humans, that the digital worldwide economy rewards winners and leaves even close seconds in the dust—are all quite well-known by now, but the authors have tied them together with a narrative that makes the book a good single place to read about them.
I particularly looked forward to the chapter titled “Racing With The Machines: Recommendations for Individuals”, in which they suggest what kind of work will be left for humans to do, and what skills we should try and develop to stay ahead of automation. I really wanted to know if they had cracked the nut of how we would stay gainfully employed. I was disappointed.
The the authors looked at prior attempts by Levy and Murnane that tried to predict limits on what activities would still require humans. They said that computers are good at following rules and bad at pattern-recognition, giving the example of driving a car as being too cognitively and sensorially complex to be automated. As the authors say in the book: “so much for that distinction.” Then they go right ahead and make their own prediction of the next frontier that computers will be unable to cross:
Our recommendations about how people can remain valuable knowledge workers in the new machine age are straightforward: work to improve the skills of ideation, large-frame pattern recognition, and complex communication instead of just the three Rs. And whenever possible, take advantage of self-organizing learning environments, which have a track record of developing these skills in people.
The biggest problem with this advice is that it is skating to where the puck is, not where it is going to be.
The example they lean on the most is freestyle chess where teams of humans and chess programs compete against each other (hence the title of the chapter). It turns out that the combination of a chess program guided by a human is more powerful than either alone. Freestyle chess is also a central example in Tyler Cowen’s latest book. Unlike Brynjolfsson and McAfee, Cowen wonders if this is just a transitional phase, and if humans will ultimately not add any value in this pairing.
Their recommendation about “ideation” and “large-frame pattern recognition” is not concrete enough. What does that mean specifically for someone choosing a college major today? And more importantly, can we be sure that those activities will remain out of the reach of computers by the time they graduate?
The debate about whether human thought is computable is an open one, but the vast majority of human cognitive activity does not happen anywhere near that threshold. In an ironic similarity to the diagonal cut, perhaps the only people secure in their isolation from automation are mathematicians probing the bounds of what is computable, and how.
But each one of the rest of us has to wonder if, within our lifetimes, our jobs are automatable. I program for a living, and while a good fraction of what I do is intellectually challenging, there is also some that makes me feel like just an operator.
Many non-technical authors think of Kasparov losing to Big Blue as a major milestone in AI, but that was largely due arriving at a point in time that Moore’s Law delivered enough computing beef to “solve” an exponentially complex game like chess. A more meaningful threshold would be when insight can be computed. For example, could a computer propose an algorithm with the simple elegance of Quicksort?
“Running with the machines” is a temporary strategy at best. That is simply the halfway house between humans doing something and it being fully automated. A more accurate phrase would be “run by the machines”, because cheap human labor is crowdsourced for the kinds of problems that are just barely outside a computer’s (current) reach.
I see two strategies for staying ahead of automation:
The first is to be the one doing the automation. In other words, to be a programmer. (Massive disclaimer: of course I would say that, being a programmer.) More bluntly, be the hunter instead of the hunted. The problem is that not everybody is able or willing to do that.
The second strategy is to be a doctor of machines. Large computing systems show organic behavior, and tending to them often requires the same mindset and behaviors as a doctor diagnosing and tending to patients. I like to draw an analogy to cities. A (relatively) small number of people initially construct the infrastructure (pipes, wires, roads), but a much larger number of people maintain them continuously.
We will have interesting lives.