If you’re a mechanic, you should look into freestyle chess because you may gain some insight into your future. What’s freestyle chess? It’s a fascinating offshoot of the traditional game that appears to throw out all of the rules about cheating, you can get all of the help you want and the top level games that result, speak for themselves.
Computers playing chess
In the 90s, computer programmers became fascinated with the idea of programming a computer to play chess, hoping one day to play well enough to vanquish a good human opponent. Like most every other area where computer power was focused, it wasn’t long before things advanced rapidly and in 1997, IBM’s Deep Blue beat grandmaster Garry Kasparov, perhaps the best player ever. The advancement since hasn’t slowed and you can now buy software for your own computer that will beat the best players, just load it up and prepare to be humbled. You can even play one program against another and see which one is really better, but who would want to do that?
Computers and humans playing chess as a team
Freestyle chess, on the other hand, is definitely interesting. The opposing players on either side can be teams of several individuals together plus a computer program or two or three, multiple computers and your cousin Fred. Bring anyone or anything, just make your move within 60 minutes. The best human plus computer teams can easily crush any grandmaster, but, currently, they can beat the lone computer program, as well. Humans who have mastered working with smart machines can produce results at the highest levels, even if their own skills in the task at hand are less than expert. Strong freestyle chess teams often have human players with moderate chess playing skills.
The future of highly skilled work
“Average is Over” is the title of an interesting new book by economist Tyler Cowen. In it, he describes just what the title implies, if your skills are average, you’re in for a very rough road and decreasing opportunities, but there’s a way to adapt. Instead of simply welcoming your new computer overlords, learn to work with them. Become part of a computer and human team.
Think of it this way. A good mechanic may be very skilled, but team that mechanic with a computer that has complete knowledge of problems occurring in the particular model being repaired, feed in the symptoms and the most complex and tricky malfunction is likely to be found in short order. Take an average mechanic with the ability to work with a smart diagnostic machine, call him a “freestyle mechanic,” and he may very well match the expert in finding the really tough problems. If the expert rejects the computer’s help because he is, after all, an expert, he might find it difficult to distinguish his performance from the conscientious newcomer, at least in the diagnostic phase of the job.
Think of how many fields are subject to the “freestyle” method. This is more than checking Google for symptoms, this applies across the board. As the task at hand becomes more difficult, a human and computer team will always pull into the lead, just as they’ve done in freestyle chess. Computers are here, they’re getting faster and more competent. Your hard won knowledge and master level skills may be everything you think they are, but as we’ve mentioned before, the smart machine never tires, never sleeps, it just keeps coming, it’s probably best not to face it head on.
What is the lesson?
If you and your skills are a complement to the computer, your wage and labor market prospects are likely to be cheery. If your skills do not complement the computer, you may want to address that mismatch. Ever more people are starting to fall on one side of the divide or the other. That’s why average is over.
Adapt now
While some may worry this rapidly spreading smart machine world of work is not a good thing, it’s coming regardless, and you may as well decide now to adapt to it in a positive way. This applies to skilled knowledge workers at any level. Whether you’re a mechanic, doctor or field engineer, anything that requires diagnosis of disparate symptoms in a complex system is being rapidly taken over by smart machines that can perform better than you can alone, but as a team, you plus the computer can be unbeatable.
I think the possibilities are incredible, but an easy coast through a long career is probably a distant memory, no matter what your field of specialization.
Cowen covers a great many other aspects of the world of current and future work and, especially, if you’re young and looking ahead, gives some interesting insight into what’s coming. I found the whole book fascinating, but I’ve always loved this sort of thing. If you’re wondering how to re-skill for the coming years, it’s worth a read.
Link: Average Is Over: Powering America Beyond the Age of the Great Stagnation
GenWaylaid says
Intriguing. It might be instructive to know more about what is going on inside successful freestyle chess teams, how the players communicate and what each type of player contributes. I’ve read several accounts of the development of computer chess, and one of the interesting lessons learned is that while computers were trained to think more and more moves ahead, human grandmasters don’t think any further ahead than moderately skilled players. Instead, the grandmasters recognize many more patterns on the board, and draw from this large memory store to simplify the decision making around each move. (This also explains why many chess masters can play multiple games in parallel.) Does this mean that collaborative chess works by combining the collective pattern recognition of the human players with the look-ahead capabilities of the computer programs?
I think you’re right, Paul, that this model could be extended to various types of diagnosis, but I’m not convinced we have the right kind of computer partners yet. Most computer diagnostic programs still tend to be rules-based. Carrying the analogy from computer chess, what we need are computer programs that can run many “what-if” experiments to test the humans’ hypotheses. The computer would need a dynamic model of the system in question and the ability to introduce a variety of defects into the model and simulate the results. This, plus rules-based programs to narrow down the possibilities, plus one or more humans (who might not all be physically present) to provide an experience pool, plus the occasional use of measurement tools to check suggestions, should make a very powerful diagnostic team.
In fact, such a team might be too powerful for all but the most complex physical systems. In the aerospace industry, we do use human teams and simulations to find the causes of problems with aircraft and spacecraft, but the human-machine collaborative aspect has never been formally recognized. Some areas that could make full use of this model might be medical diagnosis and determining how to distribute supplies after a natural disaster.
Paul Crowe says
Cowen goes into considerable depth explaining how the freestyle teams work, what the programs bring and how the human contributes. It takes a particular type of analytical mind to work in this environment, but I found the fact that the chess players on the team didn’t need to be experts themselves to be valuable contributors an interesting point, though they were very capable in other ways.
I agree. This human-computer mashup has massive potential and would best be used in the most complex situations. In the average mechanical problem solving scenario, it’s probably overkill.
For those unwilling to change or adapt, this is an ominous development, but it presents great opportunity at the same time if you’re ready to accept the challenge. Now, more than ever, if you sit still, you’ll get run over.