Categories
Machine Learning

The downside of machine learning in health care | MIT

Ghassemi and her collaborator, Boston University’s Elaine Okanyene Nsoesie, wrote a warning note on the future of AI in medicine in a piece published on Jan. 14 in the journal Patterns. According to Ghassemi, if utilized properly, technology may enhance healthcare performance and perhaps eliminate disparities, but if we aren’t vigilant, it might really impair treatment. Given that the AI systems in issue train themselves by processing and analyzing massive amounts of data, it all boils down to data. The goal of the research is not to prevent engineers from applying their machine learning knowledge in the medical field. 

Source: https://news.mit.edu/2022/marzyeh-ghassemi-explores-downside-machine-learning-health-care-0201 

Categories
Robots

Here Come the Underdogs of the Robot Olympics | Wired

What appears to be ordinary robotics student work is really a team called Coordinated Robotics’ fevered build-up to a major event in the realm of autonomy: the last round of the Subterranean Challenge, held by the US government’s Defense Advanced Research Projects Agency, or Darpa. The SubT Challenge, which began in 2018 and will end in the Mega Cavern, challenges both robots and roboticists to overcome the perilous set of challenges that exist underground—low vision, inadequate connection, and concealed terrain. The virtual contests are based on the idea that anybody with enough intelligence and access to a computer may make a significant contribution to the study. A total of $5 million in prize money is on the line. 

Source: https://www.wired.com/story/darpa-challenge-robot-olympics-underdog/