After IBM’s Watson supercomputer beat two Jeopardy! champions in 2011, artificial intelligence seemed ready to take on the world’s greatest challenges. Indeed, following Watson’s widely publicized quiz show victory, IBM teamed up with some of America’s foremost medical institutions to use Watson’s algorithms to tackle the scourge of cancer. The hope was that Watson’s A.I. capabilities would analyze the vast amounts of cancer data the institutions had amassed, develop data-driven insights, and help care providers make more effective treatment decisions.
The initiative didn’t go as planned. Oncologists turned to A.I. for answers, but Watson couldn’t deliver for various reasons, including gaps and messiness in the data and A.I.’s inability to pick up textual cues in medical documents that were clear to doctors. Several of the cancer initiative’s projects eventually shut down.
But the oncologists and engineers in some of the projects took a different tack. Instead of blaming A.I. for not delivering results, they redesigned the respective roles of human and algorithm. They realized that A.I. could rapidly cross-reference a patient’s genetic profile against the gene mutations mentioned in thousands of digitized academic papers and identify treatments that could have been overlooked. Instead of asking Watson for a solution, they asked it for solution alternatives. In reframing the respective roles of the oncologists and A.I., both were able to play to their strengths: The A.I. shrank the time and effort needed to identify a comprehensive list of potential treatments, while the oncologists used their experience to choose among those treatments and deliver them to patients.
As business increasingly adopts A.I., company leaders should keep the lesson of Watson in mind. To generate the optimum results from their investments in A.I., they must understand the different ways in which employees and algorithms can be combined and choose the most effective human-A.I. combination for the challenge at hand. Here are three principles for doing so.
Continue reading: https://fortune.com/2022/05/06/ai-artificial-intelligence-human-collaboration-watson-dominos-pizza/
The initiative didn’t go as planned. Oncologists turned to A.I. for answers, but Watson couldn’t deliver for various reasons, including gaps and messiness in the data and A.I.’s inability to pick up textual cues in medical documents that were clear to doctors. Several of the cancer initiative’s projects eventually shut down.
But the oncologists and engineers in some of the projects took a different tack. Instead of blaming A.I. for not delivering results, they redesigned the respective roles of human and algorithm. They realized that A.I. could rapidly cross-reference a patient’s genetic profile against the gene mutations mentioned in thousands of digitized academic papers and identify treatments that could have been overlooked. Instead of asking Watson for a solution, they asked it for solution alternatives. In reframing the respective roles of the oncologists and A.I., both were able to play to their strengths: The A.I. shrank the time and effort needed to identify a comprehensive list of potential treatments, while the oncologists used their experience to choose among those treatments and deliver them to patients.
As business increasingly adopts A.I., company leaders should keep the lesson of Watson in mind. To generate the optimum results from their investments in A.I., they must understand the different ways in which employees and algorithms can be combined and choose the most effective human-A.I. combination for the challenge at hand. Here are three principles for doing so.
Continue reading: https://fortune.com/2022/05/06/ai-artificial-intelligence-human-collaboration-watson-dominos-pizza/
