This episode first aired in November, 2017 - Dr. Jaime Teevan has a lot to say about productivity in a fragmented culture, and some solutions that seem promising, if somewhat counterintuitive.

Dr. Teevan is a Microsoft researcher, University of Washington Affiliate Professor, and the mother of 4 young boys. Today she talks about what she calls the productivity revolution, and explains how her research in micro-productivity – making use of short fragments of time to help us accomplish larger tasks – could help us be more productive, and experience a better quality of life at the same time.

https://microsoft.com/research

This episode first aired in January, 2018. As the reality of artificial intelligence continues to capture our imagination, and critical AI systems enter our world at a rapid pace, Dr. Ece Kamar, a senior researcher in the Adaptive Systems and Interaction Group at Microsoft Research, is working to help us understand AI’s far-reaching implications, both as we use it, and as we build it.

Today, Dr. Kamar talks about the complementarity between humans and machines, debunks some common misperceptions about AI, reveals how we can overcome bias and blind spots by putting humans in the AI loop, and argues convincingly that, despite everything machines can do (and they can do a lot), humans are still “the real deal.”

https://www.microsoft.com/research

If you’re like me, you’re no longer amazed by how all your technologies can work for you. Rather, you’ve begun to take for granted that they simply should work for you. Instantly. All together. All the time. The fact that you’re not amazed is a testimony to the work that people like Dr. Lidong Zhou, Assistant Managing Director of Microsoft Research Asia, do every day. He oversees some of the cutting-edge systems and networking research that goes on behind the scenes to make sure you’re not amazed when your technologies work together seamlessly but rather, can continue to take it for granted that they will!

Today, Dr. Zhou talks about systems and networking research in an era of unprecedented systems complexity and what happens when old assumptions don’t apply to new systems, explains how projects like CloudBrain are taking aim at real-time troubleshooting to address cloud-scale, network-related problems like “gray failure,” and tells us why he believes now is the most exciting time to be a systems and networking researcher.

Dr. Chris Bishop is a Microsoft Technical Fellow and director of MSR Cambridge, where he oversees an impressive portfolio of research including machine learning, AI, healthcare and gaming. Phil Spencer is the Executive Vice President of Gaming at Microsoft where he oversees everything from the design of the next Xbox console to the creation and release of blockbuster properties like Halo, Gears of War and Forza Motorsport. These two powerhouse executives are pushing the boundaries of creativity, technical innovation and fun across the spectrum of gaming genres and devices for nearly 2 billion gamers around the world.

On today’s podcast, Chris and Phil discuss their respective roles in Microsoft’s gaming ecosystem, revealing a sort of “enrichment pipeline” that flows all the way from researcher to developer to gamer. They also give us an inside look at the close collaboration between the world-class research organization of MSR and the world-class gaming franchise of Xbox, highlighting Microsoft’s unique ability to deliver the tools, talent and resources that fuel innovation and help shape the future of gaming.

http://www.microsoft.com/research

You may not know who Dr. Andrew Fitzgibbon is, but if you’ve watched a TV show or movie in the last two decades, you’ve probably seen some of his work. An expert in 3D computer vision and graphics, and head of the new All Data AI group at Microsoft Research Cambridge, Dr. Fitzgibbon was instrumental in the development of Boujou, an Emmy Award-winning 3D camera tracker that lets filmmakers place virtual props, like the floating candles in Hogwarts School for Witchcraft and Wizardry, into live-action footage. But that was just his warm-up act.

On today’s podcast, Dr. Fitzgibbon tells us what he’s been working on since the Emmys in 2002, including body- and hand-tracking for powerhouse Microsoft technologies like Kinect for Xbox 360 and HoloLens, explains how research on dolphins helped build mathematical models for the human hand, and reminds us, once again, that the “secret sauce” to most innovation is often just good, old-fashioned hard work.

If you’ve recently found it more difficult to focus your attention for a lengthy stretch of time in order to get a complex task done… or worse, found it difficult even to find a lengthy stretch of time in which to try, you’re not alone. And actually, you’re in luck. Dr. Shamsi Iqbal, a senior researcher in the Information and Data Sciences group at Microsoft Research, wants to help you manage your attention better and be more productive at the same time. And she’s using technology to do it!

On today’s podcast, Dr. Iqbal tells us about her work in the field of micro-productivity, a line of research that takes aim at the short spurts of time she calls micro-moments that we otherwise might have considered too short to get anything useful done. She also explains why distraction can be good for us and gives us some advice on how to make the most of our cognitive resources, whether by setting aside time to tackle big tasks in the traditional way or by breaking them down into micro-tasks… and “outsourcing” them to ourselves!

Machine learning is a powerful tool that enables computers to learn by observing the world, recognizing patterns and self-training via experience. Much like humans. But while machines perform well when they can extract knowledge from large amounts of labeled data, their learning outcomes remain vastly inferior to humans when data is limited. That’s why Dr. Patrice Simard, Distinguished Engineer and head of the Machine Teaching group at Microsoft, is using actual teachers to help machines learn, and enable them to extract knowledge from humans rather than just data.

Today, Dr. Simard tells us why he believes any task you can teach to a human, you should be able to teach to a machine; explains how machines can exploit the human ability to decompose and explain concepts to train ML models more efficiently and less expensively; and gives us an innovative vision of how, when a human teacher and a machine learning model work together in a real-time interactive process, domain experts can leverage the power of machine learning without machine learning expertise.

If you’re in software development, Dr. Tom Zimmermann, a senior researcher at Microsoft Research in Redmond, wants you to be more productive, and he’s here to help. How, you might ask? Well, while productivity can be hard to measure, his research in the Empirical Software Engineering group is attempting to do just that by using insights from actual data, rather than just gut feelings, to improve the software development process.

On today’s podcast, Dr. Zimmermann talks about why we need to rethink productivity in software engineering, explains why work environments matter, tells us how AI and machine learning are impacting traditional software workflows, and reveals the difference between a typical day and a good day in the life of a software developer, and what it would take to make a good day typical!

When was the last time you had a meaningful conversation with your computer… and felt like it truly understood you? Well, if Dr. Xuedong Huang, a Microsoft Technical Fellow and head of Microsoft’s Speech and Language group, is successful, you will. And if his track record holds true, it’ll be sooner than you think!

On today’s podcast, Dr. Huang talks about his role as Microsoft’s Chief Speech Scientist, gives us some inside details on the latest milestones in speech and language technology, and explains how mastering speech recognition, translation and conversation will move machines further along the path from “perceptive AI” to “cognitive AI” and that much closer to truly human intelligence.

Dr. John Langford, a partner researcher in the Machine Learning group at Microsoft Research New York City, is a reinforcement learning expert who is working, in his own words, to solve machine learning. Rafah Hosn, also of MSR New York, is a principal program manager who’s working to take that work to the world. If that sounds like big thinking in the Big Apple, well, New York City has always been a “go big, or go home” kind of town, and MSR NYC is a “go big, or go home” kind of lab.

Today, Dr. Langford explains why online reinforcement learning is critical to solving machine learning and how moving from the current foundation of a Markov decision process toward a contextual bandit future might be part of the solution. Rafah Hosn talks about why it’s important, from a business perspective, to move RL agents out of simulated environments and into the open world, and gives us an under-the-hood look at the product side of MSR’s “research, incubate, transfer” process, focusing on real world reinforcement learning which, at Microsoft, is now called Azure Cognitive Services Personalizer.