Microsoft Research Podcast

This episode first aired in January, 2018.When we look at a skyscraper or a suspension bridge, a simple search engine box on a screen looks tiny by comparison. But Dr. Simon Peyton Jones would like to remind us that computer programs, with hundreds of millions of lines of code, are actually among the largest structures human beings have ever built. A principle researcher at the Microsoft Research Lab in Cambridge, England, co-developer of the programming language Haskell, and a Fellow of Britain’s Royal Society, Simon Peyton Jones has dedicated his life to this very particular kind of construction work. Today, Dr. Peyton Jones shares his passion for functional programming research, reveals how a desire to help other researchers write and present better turned him into an unlikely YouTube star, and explains why, at least in the world of programming languages, purity is embarrassing, laziness is cool, and success should be avoided at all costs.

This episode first aired in March, 2018.Learning to read, think and communicate effectively is part of the curriculum for every young student. But Dr. Adam Trischler, Research Manager and leader of the Machine Comprehension team at Microsoft Research Montreal, would like to make it part of the curriculum for your computer as well. And he’s working on that, using methods from machine learning, deep neural networks, and other branches of AI to close the communication gap between humans and computers.Today, Dr. Trischler talks about his dream of making literate machines, his efforts to design meta-learning algorithms that can actually learn to learn, the importance of what he calls “few-shot learning” in that meta-learning process, and how, through a process of one-to-many mapping in machine learning, our computers not may not only be answering our questions, but asking them as well.

Amos Miller is a product strategist on the Microsoft Research NeXT Enable team, and he’s played a pivotal role in bringing some of MSR’s most innovative research to users with disabilities. He also happens to be blind, so he can appreciate, perhaps in ways others can’t, the value of the technologies he works on, like Soundscape, an app which enhances mobility independence through audio and sound. On today’s podcast, Amos Miller answers burning questions like how do you make a microwave accessible, what’s the cocktail party effect, and how do you hear a landmark? He also talks about how researchers are exploring the untapped potential of 3D audio in virtual and augmented reality applications, and explains how, in the end, his work is not so much about making technology more accessible, but using technology to make life more accessible.

Dr. Sebastien Bubeck is a mathematician and a senior researcher in the Machine Learning and Optimization group at Microsoft Research. He’s also a self-proclaimed “bandit” who claims that, despite all the buzz around AI, it’s still a science in its infancy. That’s why he’s devoted his career to advancing the mathematical foundations behind the machine learning algorithms behind AI. Today, Dr. Bubeck explains the difficulty of the multi-armed bandit problem in the context of a parameter- and data-rich online world. He also discusses a host of topics from randomness and convex optimization to metrical task systems and log n competitiveness to the surprising connection between Gaussian kernels and what he calls some of the most beautiful objects in mathematics.

Dr. Christopher Bishop is quite a fellow. Literally. Fellow of the Royal Academy of Engineering. Fellow of Darwin College in Cambridge, England. Fellow of the Royal Society of Edinburgh. Fellow of The Royal Society. Microsoft Technical Fellow. And one of the nicest fellows you’re likely to meet! He’s also Director of the Microsoft Research lab in Cambridge, where he oversees a world-class portfolio of research and development endeavors in machine learning and AI. Today, Dr. Bishop talks about the past, present and future of AI research, explains the No Free Lunch Theorem, talks about the modern view of machine learning (or how he learned to stop worrying and love uncertainty), and tells how the real excitement in the next few years will be the growth in our ability to create new technologies not by programming machines but by teaching them to learn.

This episode first aired in March (2018)One of the most intriguing areas of machine learning research is affective computing, where scientists are working to bridge the gap between human emotions and computers. It is here, at the intersection of psychology and computer science, that we find Dr. Daniel McDuff, who has been designing systems, from hardware to algorithms, that can sense human behavior and respond to human emotions. Today, Dr. McDuff talks about why we need computers to understand us, outlines the pros and cons of designing emotionally sentient agents, explains the technology behind CardioLens, a pair of augmented reality glasses that can take your heartrate by looking at your face, and addresses the challenges of maintaining trust and privacy when we’re surrounded by devices that want to know not just what we’re doing, but how we’re feeling.  

After decades of research in processing audio signals, we’ve reached the point of so-called performance saturation. But recent advances in machine learning and signal processing algorithms have paved the way for a revolution in speech recognition technology and audio signal processing. Dr. Ivan Tashev, a Partner Software Architect in the Audio and Acoustics Group at Microsoft Research, is no small part of the revolution, having both published papers and shipped products at the forefront of the science of sound. On today’s podcast, Dr. Tashev gives us an overview of the quest for better sound processing and speech enhancement, tells us about the latest innovations in 3D audio, and explains why the research behind audio processing technology is, thanks to variations in human perception, equal parts science, art and craft.

In 1998, Microsoft Research opened a small lab in Beijing to expand its research efforts and draw on the immense high-tech talent pool in China. No one expected that only twenty years later, MSR Asia would become the dynamic organization it is today, with innovative research contributing to nearly every part of Microsoft’s business. Dr. Hsiao-Wuen Hon has watched it grow from the beginning and this year, celebrates the lab’s 20th anniversary as Managing Director, Corporate Vice President and Chairman of Microsoft’s Asia-Pacific R&D Group. On today’s podcast, Dr. Hon gives us a brief history of MSR Asia, from its humble beginnings to its significant role in the AI boom today, talks about MSR Asia’s unique talent pipeline, shares his vision for the complementary roles of machine intelligence and human wisdom, and explains why, he believes, the more progress we make in AI, the better we understand ourselves.

As traditional semiconductor technologies for computer storage scale down, everyone is looking for alternative solutions to the growing gap between the amount of data we’re capable of producing and the amount of data we’re capable of storing. While some have focused on hardware accelerators for machine learning, and others are investigating new memory technologies, Dr. Karin Strauss, a Senior Researcher at Microsoft Research in Redmond, has been exploring the role of biotechnology in IT via an end-to-end system that stores digital data in DNA. On today’s podcast, Dr. Strauss talks about life at the intersection of computer science and biology which, for many, is more like the intersection of science fiction and science, and explains how the unique properties of DNA could eventually enable us to store really big data in really small places for a really long time.

  The ancient Chinese philosopher Confucius famously exhorted his pupils to study the past if they would divine the future. In 2018, we get the same advice from a decidedly more modern, but equally philosophical Bill Buxton, Principal Researcher in the HCI group at Microsoft Research. In addition to his pioneering work in computer science and design, Bill Buxton has spent the past several decades amassing a collection of more than a thousand artifacts that chronicle the history of human computer interaction for the very purpose of informing the future of human computer interaction. Today, in a wide-ranging interview, Bill Buxton explains why Marcel Proust and TS Eliot can be instructive for computer scientists, why the long nose of innovation is essential to success in technology design, why problem-setting is more important than problem-solving, and why we must remember, as we design our technologies, that every technological decision we make is an ethical decision as well.