I'm embarrassed to admit this in my mid-forties, but I've never understood chess well enough to play a full game. My son and daughter both learned how to play in elementary school. I was glad they had that experience. I tried to pick up the game when they did, but, as a busy mom of three little kids, I just didn't have the time, the interest, or the stamina to really sit down and learn. Chess became more popular during the pandemic, and the boom has stuck around; according to a recent Yougov.com

In recent years, there has been a flurry of research focusing on the fairness of machine learning models, and in particular on quantifying and eliminating bias against protected subgroups. One line of work generalizes the notion of protected subgroups beyond simple discrete classes by introducing the notion of a "rich subgroup", and seeks to train models that are calibrated or equalize error rates with respect to these richer subgroup classes. Largely orthogonally, local model explanation methods have been developed that given a classifier h and test point x, attribute influence for the prediction h(x) to the individual features of x. This raises a natural question: Do local model explanation methods attribute different feature importance values on average across different protected subgroups, and can we detect these disparities efficiently? If the model places high weight on a given feature in a specific protected subgroup, but not on the dataset overall (or vice versa), this could be a potential indicator of bias in the predictive model or the underlying data generating process, and is at the very least a useful diagnostic that signals the need for a domain expert to delve deeper. In this paper, we formally introduce the notion of feature importance disparity (FID) in the context of rich subgroups, design oracle-efficent algorithms to identify large FID subgroups, and conduct a thorough empirical analysis that establishes auditing for FID as an important method to investigate dataset bias. Our experiments show that across 4 datasets and 4 common feature importance methods our algorithms find (feature, subgroup) pairs that simultaneously: (i) have subgroup feature importance that is often an order of magnitude different than the importance on the dataset as a whole (ii) generalize out of sample, and (iii) yield interesting discussions about potential bias inherent in these datasets.

Do you think we're gonna be replaced?

This technology has enabled doctors to better detect conditions like epilepsy and Alzheimer's disease, and may transform the field of oncology more than any prior advancement. Although there is still much work to be done before AI becomes mainstream within medicine, professionals are considering this opportunity to be a major step toward advancing cancer care in the future. A challenge oncologists often face is learning how to effectively treat tumors over time. In an effort to combat this issue, a team of scientists from the University of Edinburgh have recently developed an approach known as "REVOLVER," which directly addresses evolving tumors that can become resistant to treatment over time. Through the use of AI, they have discovered a connection between repeated tumor mutations and survival rate, suggesting that specific patterns of DNA mutations could predict how cancers may progress in the future.

Only 7 percent of patients live five years after diagnosis of pancreatic cancer, the lowest rate for any cancer, according to the American Cancer Society. Elliot K. Fishman, MD, a researcher and radiologist at Johns Hopkins, is on the forefront of trying to change this statistic, and he's using artificial intelligence to do it. Fishman aims to spot pancreatic cancers far sooner than humans alone can by applying GPU-accelerated deep learning artificial intelligence to the task. Johns Hopkins is suited to developing a deep learning system because it has the massive amounts of data on pancreatic cancer needed to teach a computer to detect the disease in a CT scan. Hospital researchers also have NVIDIA's DGX-1 AI Supercomputer.

Artificial intelligence, which is bringing us everything from self-driving cars to personalized ads on the web, is also invading the world of medicine. In radiology, this technology is increasingly helping doctors in their jobs. A computer program that assists doctors in diagnosing strokes garnered approval from the U.S. Food and Drug Administration earlier this year. Another that helps doctors diagnose broken wrists in X-ray images won FDA approval on May 24. One particularly intriguing line of research seeks to train computers to diagnose one of the deadliest of all malignancies, pancreatic cancer, when the disease is still readily treatable.

A team at Johns Hopkins Medicine in Baltimore is developing a tumor-detecting algorithm for detecting pancreatic cancer. But first, they have to train computers to distinguish between organs. A team at Johns Hopkins Medicine in Baltimore is developing a tumor-detecting algorithm for detecting pancreatic cancer. But first, they have to train computers to distinguish between organs. Artificial intelligence, which is bringing us everything from self-driving cars to personalized ads on the web, is also invading the world of medicine.

It's no wonder Dr. Elliot Fishman sounds frustrated when he talks about pancreatic cancer. As a diagnostic radiologist at Johns Hopkins Hospital, one of the world's largest centers for pancreatic cancer treatment, he has the grim task of examining pancreatic CT scans for signs of a disease that's usually too advanced to treat. Because symptoms seldom show up in the early stages of pancreatic cancer, most patients don't get CT scans or other tests until the cancer has spread. By then, the odds of survival are low: Just 7 percent of patients live five years after diagnosis, the lowest rate for any cancer. "Our goal is early detection of pancreatic cancer, and that would save lives," Fishman said.

Amid growing pressure from governments, Facebook says it has stepped up its efforts to address the spread of "terrorist propaganda" on its service by using artificial intelligence (AI). In a blog post on Thursday, the California-based company announced the introduction of AI, including image matching and language understanding, in conjunction with it already-existing human reviewers to better identify and remove content "quickly". "We know we can do better at using technology - and specifically artificial intelligence - to stop the spread of terrorist content on Facebook," Monika Bickert, Facebook's director of global policy management, and Brian Fishman, the company's counterterrorism policy manager, said in the post. "Although our use of AI against terrorism is fairly recent, it's already changing the ways we keep potential terrorist propaganda and accounts off Facebook. "We want Facebook to be a hostile place for terrorists."

Floral tributes to Spanish national Ignacio Echeverria, 39, lay on London Bridge in London. Facebook said Thursday it's cracking down on terrorist activity on its platform. SAN FRANCISCO -- With attacks on Western targets increasing pressure on Facebook, the giant social network says it's making a new push to crack down on terrorist activity by using sophisticated algorithms to mine words, images and videos to root out and remove extremists' propaganda and messages. Artificial intelligence can't do the job alone, so Facebook says it has amassed a team of 150, including counterterrorism experts, who are dedicated to tracking and taking down propaganda and other materials. It's also collaborating with fellow technology companies and consulting with researchers to keep up with the ever-changing social media tactics of the Islamic State and other terror groups.