The room was packed at the annual Machine Learning and the Market for Intelligence conference in Toronto last week. Now in its fifth year, the lengthy name of the event matches the depth of the discussions. But one speaker and her talk stood out to me in particular: Marzyeh Ghassemi, who also happens to be a veteran of Alphabet's Verily, presented "Machine Learning From Our Mistakes." Ghassemi, an assistant professor at the University of Toronto, talked about the importance of predicting actionable insights in health care, the regulation of algorithms, and practice data versus knowledge data. But at the very end, saving the best for last, she emphasized the importance of treating health data as a resource.

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The Machine Learning Center at Georgia Tech (ML@GT) is home to many talented students from across campus, representing all six of Georgia Tech's colleges and the Georgia Tech Research Institute (GTRI). These students have diverse backgrounds and a wide variety of interests both inside and outside of the classroom. Today, we'd like you to meet James Smith, a second-year machine learning Ph.D. student. Smith is a unique combination of athlete and academic; he runs at least one marathon each year while also working on ways to design machine learning algorithms that positively impact the world. Other degrees earned and from what institution: B.S. and M.S. in Electrical Engineering, both from Auburn University (War Eagle!) Tell us about your research interests. Where might people be impacted by them in everyday life?

Royal Caribbean Cruises has begun using facial recognition systems to speed passengers on their way through security and ID checks. You and your family are at the pier, giddy to board the massive cruise ship docked nearby. Ahead lies a week of sunny beaches, indulgent buffet feasts and lounging around doing absolutely nothing. And then you see the long lines for security, baggage and ID checks. It often takes 75 minutes for passengers to check in, but the Pool Deck looks a lifetime away.

A new report estimates the cost of waste in the U.S. healthcare system alone ranges as high as $935 billion a year, about 25 percent of total healthcare spending. A growing army of startups and established practitioners sees the inefficiencies as a trillion-dollar opportunity to apply AI. The U.S. spends about 18 percent of its gross domestic product on healthcare, more than any other country. A report published online by the Journal of the American Medical Association surveyed 54 studies to estimate annual waste figures in six broad categories, including failures from choosing ineffective treatments (up to $166 billion), failures from coordinating multiple treatments ($78 billion), fraud and abuse ($84 billion) and administrative complexity ($266 billion). "Implementation of effective measures to eliminate waste represents an opportunity to reduce the continued increases in U.S. health care expenditures," the report concluded.

Micron Technology, Inc., today announced a powerful new set of high-performance hardware and software tools for deep learning applications with the acquisition of FWDNXT, a software and hardware startup. When combined with advanced Micron memory, FWDNXT's (pronounced "forward next") artificial intelligence (AI) hardware and software technology enables Micron to explore deep learning solutions required for data analytics, particularly in IoT and edge computing. With this acquisition, Micron is integrating compute, memory, tools and software into a comprehensive AI development platform. This platform in turn provides the key building blocks required to explore innovative memory optimized for AI workloads. "FWDNXT is an architecture designed to create fast-time-to-market edge AI solutions through an extremely easy to use software framework with broad modeling support and flexibility," said Micron Executive Vice President and Chief Business Officer Sumit Sadana.

India has just 144 police officers for every 100,000 citizens, compared to 318 per 100,000 citizens in the European Union. In recent years, authorities have turned to facial recognition technology to make up for the shortfall. New Delhi's law enforcement agencies adopted the technology in 2018, and it's also being used to police large events and fight crime in a handful of other states, including Andhra Pradesh and Punjab. But India's government now has a much more ambitious plan. It wants to construct one of the world's largest facial recognition systems.

The mission of the Future Concepts Directorate is to serve as the Judge Advocate General's Corps' (JAGC) subject matter expert on the application of the law to future conflict by assessing the legal requirements of the future operational environment, to propose and review Army doctrine, and to provide an intellectual foundation and disciplined approach to design, develop, and field a globally responsive future JAG Corps. Your browser does not support the video tag. Future Concepts Directorate (FCD) executes its mission along three lines of effort. RUSSIA'S NEW HYPERSONIC MISSILE TRAVELS NEARLY TWO MILES A SECOND WEAPONIZING BIOTECH: HOW CHINA'S MILITARY IS PREPARING FOR A'NEW DOMAIN OF WARFARE'

The term "artificial intelligence" (AI) was first coined in 1956, at a conference at Dartmouth College, in Hanover, New Hampshire. Since then, AI has had its ups and downs. The period between 1974 – 80 has become known as "AI Winter," because heavy criticism about its progress led to a reduction in both government interest and government funding. The field experienced another winter from 1987 – 93, which coincided with a collapsing market for early general-purpose computers. Things have changed significantly since then.