A Game and a Game-Changer
In my previous post, I looked at the effects of a widely viewed game of Go, which pitted an expert human player against a supercomputer, on the drive to develop artificial intelligence. Shortly after this game demonstrated the remarkable ability of AI to adapt to complex gaming situations, Chinese venture-capital investors began to pour record sums into artificial intelligence startups and making up 48 percent of all AI venture funding globally, surpassing the U.S. for the first time.
Here, we look at the new paradigm underlying that surge in Chinese government support and its relationship between artificial intelligence and the economy. While the science of artificial intelligence made slow but steady progress for decades, only recently did progress rapidly accelerate, allowing these academic achievements to be translated into real-world use-cases.
The technical challenges of beating a human at the game of Go were already familiar to me. As a young Ph.D. student researching artificial intelligence at Carnegie Mellon University, I studied under pioneering AI researcher Raj Reddy. In 1986, I created the first software program to defeat a member of the world championship team for the game Othello, a simplified version of Go played on an eight-by-eight square board. It was quite an accomplishment at the time, but the technology behind it wasn’t ready to tackle anything but straightforward board games.
The same held true when IBM’s Deep Blue defeated world chess champion Garry Kasparov in a 1997 match dubbed “The Brain’s Last Stand.” That event had spawned anxiety about when our robot overlords would launch their conquest of humankind, but other than boosting IBM’s stock price, the match had no meaningful impact on life in the real world. Artificial intelligence still had few practical applications, and researchers had gone decades without making a truly fundamental breakthrough.
Deep Blue had essentially “brute forced” its way to victory—relying largely on hardware customized to rapidly generate and evaluate positions from each move. It had also required real- life chess champions to add guiding heuristics to the software. Yes, the win was an impressive feat of engineering, but it was based on long-established technology that worked only on very constrained sets of issues. Remove Deep Blue from the geometric simplicity of an eight-by-eight square chess board and it wouldn’t seem very intelligent at all. In the end, the only job it was threatening to take was that of the world chess champion.
This time, things are different. The Ke Jie versus AlphaGo match was played within the constraints of a Go board, but it is intimately tied up with dramatic changes in the real world. Those changes include the Chinese AI frenzy that AlphaGo’s matches sparked amid the underlying technology that powered it to victory.
AlphaGo runs on deep learning, a groundbreaking approach to artificial intelligence that has turbocharged the cognitive capabilities of machines. Deep-learning-based programs can now do a better job than humans at identifying faces, recognizing speech, and issuing loans. For decades, the artificial intelligence revolution always looked to be five years away. But with the development of deep learning over the past few years, that revolution has finally arrived. It will usher in an era of massive productivity increases but also widespread disruptions in labor markets—and profound sociopsychological effects on people—as artificial intelligence takes over human jobs across all sorts of industries.
During the Ke Jie match, it wasn’t the AI-driven killer robots some prominent technologists warn of that frightened me. It was the real-world demons that could be conjured up by mass unemployment and the resulting social turmoil. The threat to jobs is coming far faster than most experts anticipated, and it will not discriminate by the color of one’s collar, instead striking the highly trained and poorly educated alike. On the day of that remarkable match between AlphaGo and Ke Jie, deep learning was dethroning humankind’s best Go player. That same job-eating technology is coming soon to a factory and an office near you.
But there's a reason for hope, which I will begin to explore in my next post. In the meantime, I would love to hear from you. Have you seen areas today where artificial intelligence has affected employment and unemployment levels? What were the circumstances? Thank you for sharing.