Like any proud father, Gary Marcus is only too happy to talk about the latest achievements of his two-year-old son. More unusually, he believes that the way his toddler learns and reasons may hold the key to making machines much more intelligent. Sitting in the boardroom of a bustling Manhattan startup incubator, Marcus, a 45-year-old professor of psychology at New York University and the founder of a new company called Geometric Intelligence, describes an example of his boy's ingenuity. From the backseat of the car, his son had seen a sign showing the number 11, and because he knew that other double-digit numbers had names like "thirty-three" and "seventy-seven," he asked his father if the number on the sign was "onety-one." "He had inferred that there is a rule about how you put your numbers together," Marcus explains with a smile.

Last year Microsoft and Google both showed that their image-recognition algorithms had learned to best humans. They independently created software that could exceed the average human score on a standard test that challenges software to recognize images of a thousand different objects, from mosques to mosquitoes. But to get good enough to defeat humanity, each company's software scrutinized 1.2 million labeled images. A child can learn to recognize a new kind of object or animal using only one example. Startup Geometric Intelligence said Monday that it has developed machine-learning software that is a much quicker study.

Congratulations are in order for the folks at Google Deepmind (https://deepmind.com) who have mastered Go (https://deepmind.com/alpha-go.html). However, some of the discussion around this seems like giddy overstatement. Wired says, "machines have conquered the last games" (http://bit.ly/200O5zG) The truth is nowhere close. For Go itself, it has been well known for a decade that Monte Carlo tree search (MCTS, http://bit.ly/1YbLm4M; that is, valuation by assuming randomized playout) is unusually effective in Go.

It was time to begin teaching my class. The children were in their seats, laptops turned on, ready to begin. I scanned the doorway, hoping for one more girl to arrive: there were nine boys in my class and just two girls. I was conducting free coding classes, but young girls were still reluctant to attend. As a 15-year-old computer enthusiast, I was baffled by this lack of interest.

Professor Yale Patt, the Ernest Cockrell, Jr. Centennial Chair in Engineering at The University of Texas at Austin has been named the 2016 recipient of the Benjamin Franklin Medal in Computer and Cognitive Science by the Franklin Institute. Patt is a renowned computer architect, whose research has resulted in transformational changes to the nature of high-performance microprocessors, including the first complex logic gate implemented on a single piece of silicon. He has received ACM's highest honors both in computer architecture (the 1996 Eckert-Mauchly Award) and in education (the 2000 Karl V. Karlstrom Award). He is a Fellow of the ACM and the IEEE and a member of the National Academy of Engineering. Derek Chiou, an associate professor of Electrical and Computer Engineering at The University of Texas at Austin, conducted an extensive interview of Patt, covering his formative years to his Ph.D. in 1966, his career since then, and his views on a number of issues. Presented here are excerpts from that interview; the full interview is available via the link appearing on the last page of this interview. DEREK CHIOU: Let's start with the influences that helped shape you into who you are. I have often heard you comment on your actions as, "That's the way my mother raised me." YALE PATT: In my view my mother was the most incredible human being who ever lived. Born in Eastern Europe, with her parents' permission, at the age of 20, she came to America by herself. A poor immigrant, she met and married my father, also from a poor immigrant family, and they raised three children. We grew up in one of the poorer sections of Boston. Because of my mother's insistence, I was the first from that neighborhood to go to college. My brother was the second. My sister was the third. You have often said that as far as your professional life is concerned, she taught you three important lessons. Almost everyone in our neighborhood quit school when they turned 16 and went to work in the Converse Rubber factory, which was maybe 100 yards from our apartment. She would have none of it.

For decades, science fiction authors, futurists, and movie makers alike have been predicting the amazing (and sometimes catastrophic) changes that will arise with the advent of widespread artificial intelligence. So far, AI hasn't made any such crazy waves, and in many ways has quietly become ubiquitous in numerous aspects of our daily lives. From the intelligent sensors that help us take perfect pictures, to the automatic parking features in cars, to the sometimes frustrating personal assistants in smartphones, artificial intelligence of one kind of another is all around us, all the time. While we've yet to create self-aware robots like those that pepper popular movies like 2001: A Space Odyssey and Star Wars, we have made smart and often significant use of AI technology in a wide range of applications that, while not as mind-blowing as androids, still change our day-to-day lives. One place where artificial intelligence is poised to make big changes (and in some cases already is) is in education.

SeaDrone, the underwater robot coming out of a new company founded by two Stanford AI lab veterans, is aiming to make fish farming a lot easier--particularly for smaller aquaculture operations--by making underwater inspection cheaper and easier. The ocean ROV's story is not an unusual one for Silicon Valley: two Stanford students meet over a lab bench, get an idea that something they'd been tinkering around with for themselves could be turned into a product and the basis of a company. It's a story Silicon Valley loves. Eduardo Moreno met Shuyun Chung in the Stanford AI lab in 2013. Moreno, in the thick of his studies for a master's degree in mechanical engineering, was working on underwater robot hardware in collaboration with King Abdullah University of Science and Technology in Saudi Arabia.

Watch this webinar, presented by Kris Hammond, Chief Scientist of Narrative Science, to learn about the different subfields of technologies that fall under the umbrella of AI such as machine learning, advanced analytics, and advanced natural language generation. Viewers will finish the webinar understanding how the different AI technologies emulate human reasoning and how they may be able to apply these technologies to their own business.

The University of Washington School of Law is delighted to announce a public workshop on the law and policy of artificial intelligence, co-hosted by the White House and UW's Tech Policy Lab. The event places leading artificial intelligence experts from academia and industry in conversation with government officials interested in developing a wise and effective policy framework for this increasingly important technology. The event is free and open to the public but requires registration. Jack M. Balkin is Knight Professor of Constitutional Law and the First Amendment at Yale Law School. He is the founder and director of Yale's Information Society Project, an interdisciplinary center that studies law and new information technologies.

The University of Washington School of Law is delighted to announce a public workshop on the law and policy of artificial intelligence, co-hosted by the White House and UW's Tech Policy Lab. The event places leading artificial intelligence experts from academia and industry in conversation with government officials interested in developing a wise and effective policy framework for this increasingly important technology. The event is free and open to the public but requires registration.