In this week’s roundup, learn how artificial intelligence (AI) is being used to tackle racial inequalities in healthcare and how machine learning (ML) is aiding in mental health diagnoses. Read how one woman used ML to understand desserts and develop new mashups. Explore the history of facial recognition and how the lack of consent presents serious consequences. Finally, consider how AI should be handled within the corporate boardroom.
By Cody Godwin, contributing writer for BBC.com
In a recent study, researchers used AI to analyze knee x-rays to predict the level of pain patients were experiencing as a result of osteoarthritis. The algorithm was able to explain more of the pain people were feeling, rather than relying on patients to self-report, which is subjective. When it comes to race, studies show that black patients are more likely to have their pain level underestimated, which can affect their treatment. And this algorithm is a tool for doctors to help find the causes for pain in everyone’s knees equally.
By University of Birmingham for MedicalXpress.com
Researchers have developed the ability to use ML to more accurately identify patients with a mix of psychotic and depressive symptoms in a recent study. They’ve created highly accurate models of “pure” forms of both illnesses to investigate the diagnostic accuracy of patients with mixed symptoms. The goal was to build a highly accurate disease profile for each patient and test it against their diagnosis to see how accurate the profile was.
by Christina Panos, contributing writer for Hackaday.com
During the pandemic, it’s no secret that many people have turned to baking as a way to pass the time. Sarah Robinson, a developer advocate for Google Cloud, built an ML model to answer some important questions. What separates baked goods from each other? What is the science behind the crunchiness of cookies, the sponginess of cake, and the fluffiness of bread? She created a cookie/cake/bread lineage with the model and it even came up with a 50/50 cookie-cake hybrid recipe (which she calls a “cakie”) that delivered a list of ingredients to use.
By Karen Hao, contributing writer for TechnologyReview.com
Facial recognition software is a powerful tool of surveillance, but it doesn’t come without a cost. A new study shows that the rapid progression of facial recognition technology has resulted in an erosion of our privacy and disrupted our norms of consent. Driven by the exploding data requirements of deep learning, researchers gradually abandoned asking for people’s consent for the use of their image into systems of surveillance. Learn about the history of this technology and the serious repercussions of this lack of consent.
By Anant Alon-Beck, contributing writer for Forbes.com
While AI is central to Google’s future, the company has dealt with several challenges concerning its top AI executives and researchers. Activists shareholders are also calling on boards to ensure proper AI governance. Board members have to be prepared for situations where AI affects and even disrupts their deliberations. This article is an interview with Sergio Alberto Gramitto Ricci to discuss his research on the use of AI in the boardroom.
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