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# AlphaGo and the Future of Work

In March of this year, Google’s DeepMind computer program AlphaGo defeated world Go champion Lee Sedol. This was hailed as a great triumph of artificial intelligence, and signaled to many the beginning of the new age when machines take over. I believe this is true but the real lesson of AlphaGo's win is not the greatness of machine learning algorithms, but the suboptimal nature of human Go players. Experts believed that machines would not be able to defeat humans at Go for a long time because the number of possible games is astronomically large, with $$\sim 250^{150}$$ possible moves, in contrast to chess with a paltry $$\sim 35^{80}$$ moves. Additionally, unlike chess, it is not clear when the stones are in a good position and who is winning during intermediate stages of a game. Thus, any direct enumeration and evaluation of possible next moves—as chess computers do, like IBM's Deep Blue that defeated Gary Kasparov—seemed to be impossible. It was thought that humans had some sort of inimitable intuition to play Go that machines were decades away from emulating. It turns out that this was wrong. It took remarkably little training for AlphaGo to defeat a human. All the algorithms used were fairly standard – supervised and reinforcement backpropagation learning in multilayer neural networks.1 DeepMind just put them together in a clever way and had the (in retrospect, appropriate) audacity to try.

Google's DeepMind program AlphaGo defeats world Go champion Lee Sedol. Photo credit: Buster Benson.
The take-home message of AlphaGo's success is that humans are very, very far away from optimally playing Go. Uncharitably, we simply stink at Go. However, this probably also means that we stink at almost everything we do. Machines are going to take over our jobs not because they are sublimely awesome, but because we are stupendously inept. It is like the old joke about two hikers encountering a bear, prompting one to put on running shoes. The other hiker says: "Why are you doing that? You can't outrun a bear." to which she replies, "I only need to outrun you!" In fact, the more difficult a job seems to be for humans, the easier it is for a machine to do better. This concept, called the Moravec's Paradox, was noticed a long time ago in AI research. Tasks that require a lot of high-level abstract thinking—like chess or the prediction of what movie you will like—are easy for computers to do, while seemingly trivial tasks that a child can do—like folding laundry or getting a cookie out of a jar on an unreachable shelf—are really hard. Thus, high-paying professions in medicine, accounting, finance, and law could be replaced by machines sooner than lower-paying jobs in lawn care and house cleaning.

There are those who are not worried about a future of mass unemployment because they believe people will just shift to other professions. They point out that a century ago the majority of Americans worked in agriculture, and now the sector comprises less than 2 percent of the population. Jobs lost to technology were replaced by ones that didn't exist before. I think this might be true, but in the future not everyone will be a software engineer or a media star or a CEO of his/her own company of robot employees. The increase in productivity provided by machines ensures this. When the marginal cost of production goes to zero (i.e. cost to make one more item), as it has for software or recorded media, the whole supply-demand curve is upended. There is infinite supply for any amount of demand, so the only way to make money is to increase demand.

The rate-limiting step for demand is the attention span of humans. In a single day, a person can at most attend to a few hundred independent tasks such as thinking, reading, writing, walking, cooking, eating, driving, exercising, or consuming entertainment. I can stream any movie I want yet I only watch at most twenty a year, and almost all of them on long flights. My three-year-old can watch the same Wild Kratts episode (great children's show about animals) ten times in a row without getting bored. Even though everyone could be a video or music star on YouTube, superstars such as Beyoncé and Adele are viewed much more often than anyone else. Even with infinite choice, we tend to do what our peers do. Thus, for a population of ten billion people, I doubt there can be more than a few million able to make a decent living as a media star with our current economic model. The same goes for writers. This idea will also generalize to manufactured goods. Toasters and coffee makers essentially cost nothing compared to three decades ago, and I will only buy one every few years, if that. Robots will only make things cheaper, and I doubt there will be a billion brands of TVs or toasters in the future. Most likely, a few companies will dominate the market as they do now. Even if we could optimistically assume that a tenth of the population could be engaged in producing goods and services necessary for keeping the world functioning, that still leaves the rest with little to do.

Machines could also eventually replace much of what scientists do. Biology labs could consist of a principle investigator and robot technicians. Although it seems like science is endless, the amount of new science required for sustaining the modern world could diminish. We could eventually have an understanding of biology sufficient to treat most diseases and injuries and develop truly sustainable energy technologies.  In this case, machines could be tasked to keep the modern world up and running with little need for human input. Science would mostly be devoted to abstract and esoteric concerns.

Thus, I believe the future for humankind is in low-productivity occupations – basically a return to pre-industrial endeavors like small plot farming, blacksmithing, carpentry, painting, dancing, and pottery making, with an economic system in place to adequately live off of this labor. Machines can provide us with the necessities of life while we engage in a simulated 18th century world lacking the poverty, diseases, and mass famines that made life so harsh back then. We can make candles or bread and sell them to our neighbors for a living wage.  We can walk or get in self-driving cars to see live performances of music, drama, and dance by local artists. There will be philosophers and poets with their small followings as they have now.  However, even when machines can do everything humans can do, there will still be a capacity to sustain as many mathematicians as there are people because mathematics is infinite. As long as P is not NP, proving theorems can never be automated and unsolved math problems will always exist. That is not to say that machines won’t be able to do mathematics. They will. It’s just that they won’t ever be able to do all of it. Thus, the future of work could also be mathematics.

1  Silver, D. et al. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529, 484–489.

Carson Chow is a Senior Investigator at the National Institutes of Health. He blogs at sciencehouse.wordpress.com.