Prof. Gil Alterovitz is a faculty at Harvard Medical School in the Center for Biomedical Informatics. He is Director of the Biomedical Cybernetics Laboratory and core faculty member of the Children's Hospital Informatics Program. He is also affiliated with the Department of Electrical...

Students who graduated from kindergarten welcome their up-coming study from September at the Minhang Qiangwei Primary School affiliated to Shanghai University of Traditional Medicine in May.

Students who graduated from kindergarten welcome their up-coming study from September at the Minhang Qiangwei Primary School affiliated to Shanghai University of Traditional Medicine in May.

Interpretability is one of the most difficult challenges in modern machine learning solutions. While building sophisticated machine learning models is getting easier, understanding how models develop knowledge and arrive to conclusions remains a very difficult challenge. Typically, the more accurate the models the harder they are to interpret. The release of AI Explainability 360 is the first practical implementation of ideas outlined in dozens of research papers in the last few years. In the same way traditional software applications incorporate instrumentation code to help its runtime monitoring, machine learning models need to add interpretability techniques to facilitate debugging, troubleshooting and versioning.

The age of automation, and on the near horizon, artificial intelligence (AI) technologies offer new job opportunities and avenues for economic advancement, but women face new challenges overlaid on long-established ones. Between 40 million and 160 million women globally may need to transition between occupations by 2030, often into higher-skilled roles. To weather this disruption, women (and men) need to be skilled, mobile, and tech-savvy, but women face pervasive barriers on each, and will need targeted support to move forward in the world of work. A new McKinsey Global Institute (MGI) report, The future of women at work: Transitions in the age of automation (PDF–2MB), finds that if women make these transitions, they could be on the path to more productive, better-paid work. If they cannot, they could face a growing wage gap or be left further behind when progress toward gender parity in work is already slow. This new research explores potential patterns in "jobs lost" (jobs displaced by automation), "jobs gained" (job creation driven by economic growth, investment, demographic changes, and technological innovation), and "jobs changed" (jobs whose activities and skill requirements change from partial automation) for women by exploring several scenarios of how automation adoption and job creation trends could play out by 2030 for men and women given current gender patterns in the global workforce. These scenarios are not meant to predict the future; rather, they serve as a tool to understand a range of possible outcomes and identify interventions needed.

Sophia is a social humanoid robot developed by Hong Kong-based company Hanson Robotics. It is the first non-human to receive citizenship in a country. When it received citizenship in Saudi Arabia in October 2016 as the first non-human ever in any country. Artificial intelligence is developing fast. Something which is manifested in a new market research report about AI development for education.

Lolita Taub is a force of nature - or perhaps more accurately, a force in technology. Growing up, she was told "tech is not for girls," and in recent years, "VC is not for women." However, these comments only further drove Taub to demonstrate that women, in fact, belong everywhere. Taub recently joined the Catalyte team as Chief of Staff. Catalyte uses artificial intelligence to identify individuals, regardless of background, who have the innate potential and cognitive ability to be great software developers - which is perfectly aligned with Taub's vision of a world "where tech is pioneered by the diversity of our population." I spoke with Taub about her work in AI, commitment to diversity in technology and how AI is already impacting the way we work.

Smartphones will soon be able to read sign language using a new artificial intelligence (AI) based algorithm designed to track the movement of hands. The algorithm, designed by Google's AI Labs, will provide a smartphone with the ability to perceive hand movements and shapes across a variety of platforms. The company stated in its blog on the Google AI website on Monday, that its algorithm will let smartphone users read sign language using Augmented Reality. Real-time hand perception is a hard task since human actions are generally unpredictable. Hands block each other and lack patterns with high contrast.

Law is a constantly evolving industry, and few things have brought about as much change as the rise of legal tech. From my days at Harvard Law School, to BigLaw, to my current role leading a legal tech company, I've seen first-hand how technology, and AI in particular, have played a critical role in bringing a risk-averse industry into the next wave of the digital era. When I arrived at Harvard Law School in 2005, artificial intelligence was little more than a theoretical concept in the legal industry. Practical applications of AI, machine learning, and natural language processing were still things of the future. It would be years before IBM's Watson would beat Ken Jennings on Jeopardy!

When it comes to educational dialogue, there is nothing more entertaining than a great TED talk. They provide insight into fascinating subjects in an entertaining way often filled with stories, mind-blowing facts and first-hand experiences of those giving the talks. With AI and machine learning at the forefront of so many questions and topics now, what better way to get the scoop on it than by enjoying some great speeches from those on the cutting-edge of innovation. Here are 7 of the best AI and machine learning TED talks you should watch. This talk is an insightful look into what people want to know about AI, their worries and addressing those concerns.