AI and the ‘Augmentation’ Fallacy

Many pundits, academics, and economists advise business executives on how artificial intelligence (AI) will augment human performance in the workplace. Some conclude that human-machine interactions will involve machines providing scale and speed with humans offering insights and training data.

Despite its broad appeal, the assessment that human-machine interactions are, and will continue to be, exclusively about augmenting humans or teams of humans and machines is shortsighted and underestimates the transformative potential of AI.

Some machines are already beginning to learn in virtualized (at least partially) environments with neither human training nor data input from the real world. This process, known as hyperlearning, allows systems to learn at machine speed and develop novel solutions in specific settings, frequently involving unsupervised learning and reinforcement learning algorithms. Often these systems use adversarial or complementary AI engines that play off against each other, generating virtual training data in the process. Companies in different industries are already creating the environment for such hyperlearning systems, raising the question: What should executives expect from human-machine interactions in the coming years?

When IBM’s Deep Blue defeated chess grandmaster Garry Kasparov in 1997, it was the first time a machine beat a human world champion in a chess match. It is also an example of how human-machine dynamics can evolve, providing interesting insights for business applications. Chess players first began using AI systems to enhance their own performance, using computers to train for tournaments. Then, advanced chess tournaments emerged, which allowed players to use computers during otherwise conventional competitions. The computational firepower of machines — enhanced by libraries of openings and endgames — complemented the strong strategic planning and refined position assessment of humans, augmenting existing approaches to playing chess.

Then freestyle chess developed, which allowed any kind of interaction between humans and computers without focusing on augmenting human-machine performance. The winners of the first PAL/CSS Freestyle Chess Tournament in 2005 were not the strongest humans, assisted by machines, or the strongest machines, assisted by humans. Instead, two amateurs, Steven Cramton and Zackary Stephen, using three standard PCs, won the competition. They had optimized an entirely new process in which either one of the humans or the machines took the lead depending on patterns of positions and play of opponents.