Augmentation Fallacy?

Interesting piece, which uses the chess example to understand what AI can do now, and how it might be used to improve the workforce.   In the past the use of Chess as an 'unsolveable' problem for AI was common.  Now it rarely mentioned for business application.  Business does not need better chess playing.  But it is noted here that Chess was described to the solver by providing its relatively simple rules.   Can we the same for business by showing it business rules?   

We can model business rules with methods like BPM,  process modeling and process examples.  But there is still considerable probabilistic risk, dynamic and changing context involved.   So the complexity is considerable.  So it helps to understand process, use it as a goal setting direction, but not expecting a specific solution.  What is better now, is to augment the process, and thus the people involved, by inserting machine learning methods where they provide the most value,  into a business process model.

In past attempts at AI, we hoped to model businesses by large numbers of rules.   But discovered soon that these were brittle and needed frequent updating.  Thats my definition of near term view of near term 'augmentation', or people or people as part of process.  Perhaps some day we will solve the business process more broadly.

AI and the ‘Augmentation’ Fallacy
By Philipp Gerbert  in SloanMIT Review

The fundamental disruption introduced by AlphaZero’s hyperlearning in the chess world can teach business executives about AI.  Chess was often mention in past arguments about the use of AI in business, but is rarely used today.  

 Artificial Intelligence Lessons From the Chessboard

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. ... "