I was chatting with a friend of mine about the advent of robotic surgery and he was lamenting the challenges associated with training younger doctors. Before robotic surgery, medical surgeons stood shoulder to shoulder alongside seasoned surgeons operating. Today, the head surgeon manipulates a robot independently while students watch through a window or video.
A lot has been written about training AI. But what about training humans?
Shouldn’t the same pattern reverberate through the work that we expect the next generation of AI to automate, including paralegal functions, accounting, computer programming, and sales development?
If other disciplines are any indication, the answer is no, at least not to the same extent.
The world of chess was transformed when DeepBlue beat Gary Kasparov in 1996. Since then, chess has thrived, driven by the interactive learning between computers and humans. AI has developed moves that humans hadn’t yet conceived – even developing a new opening.
There are at least four drivers of simulation racing games who have become professional drivers in motor sports.
Within education, computer-adaptive reading programs – programs that change their instruction to match student needs – produced statistically significantly higher gains in students than in a control group across reading vocabulary and reading comprehension scores.
It’s also true within the world of consulting where the most sophisticated consultants use AI to “to simulate a type of personality or character (e.g., a software buyer)” to help them improve in sales processes.
Scott Page, a professor from the University of Michigan, wrote a book The Diversity Bonus, championing the idea of cognitive diversity. The key idea is how small teams with differences in the way they reason, interpret, and solve problems are the most effective construct.
What if adding a machine learning agent to a decision-making process improves that cognitive diversity? After all, robots solve problems in different ways than we do.
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