Summary: According to a recent study, humans and artificial intelligence systems can perform better when both of them work together to tackle problems.
Original author and publication date: Arooj Ahmed – May 20, 2020
Futurizonte Editor’s Note: It is good that we can work together with AI to solve our problems. It is not good that we can’t work together with other humans to solve our problems.
From the article:
According to a recent study, humans and artificial intelligence systems can perform better when both of them work together to tackle problems. The research was done by Eric Horvitz Chief scientist Microsoft, Ece Kamar the Microsoft Research principal researcher, and Bryan Wilder, a student at Harvard University and Microsoft Research intern.
It seems that Eric Horvitz first published the research paper. He was hired as Microsoft principal researcher back in the year 1993 and the company named him Microsoft Chief Scientist officer during March. He led the company’s research programs from the year 2017 to 2020.
The research paper was published earlier this month and it studies the performance of artificial intelligence teams and humans operating together on two PC vision projects namely breast cancer metastasis recognition and Galaxy categorization. With this proposed approach, the artificial intelligence (AI) model evaluates which tasks humans can perform best and what type of tasks AI systems can handle better.
In this approach, the learning procedure is developed to merge human contributions and machine predictions. The artificial intelligence systems work to tackle problems that can be difficult for humans while humans focus on solving issues that can be tough for AI systems to figure out.
Basically, AI system predictions generated with lower accuracy levels are routed to human teams in this system.
According to researchers, combined training of human and artificial intelligence systems can enhance the galaxy classification model for us. It can improve the performance of ‘Galaxy Zoo’ with a 21 to 73% decrease in loss. This system can also deliver an up to 20% better performance for CAMELYON16.
The research paper states that the performance of machine learning in segregation overcomes the circumstances where human skills can add integral context, although human teams have their own restrictions including systematic biases.
Researchers stated in the paper that they have developed methods focused on training the AI learning model to supplement human strengths.
It also accounts for the expense of inquiring an expert. Human and AI system teamwork can take various forms but the researchers focused on settings where machines would decide which instances required human absorption and then merging human and machine judgments.