Microsoft Research Podcast

This episode first aired in August of 2018. You know those people who work behind the scenes to make sure nothing bad happens to you, and if they’re really good, you never know who they are because nothing bad happens to you? Well, meet one of those people. Dr. Brian LaMacchia is a Distinguished Engineer and he heads up the Security and Cryptography Group at Microsoft Research. It’s his job to make sure – using up-to-the-minute math – that you’re safe and secure online, both now, and in the post-quantum world to come.Today, Dr. LaMacchia gives us an inside look at the world of cryptography and the number theory behind it, explains what happens when good algorithms go bad, and tells us why, even though cryptographically relevant quantum computers are still decades away, we need to start developing quantum-resistant algorithms right now.  

If you’ve ever wondered why, in the age of the internet, we still don’t hold our elections online, you need to spend more time with Dr. Josh Benaloh, Senior Cryptographer at Microsoft Research in Redmond. Josh knows a lot about elections, and even more about homomorphic encryption, the mathematical foundation behind the end-to-end verifiable election systems that can dramatically improve election integrity today and perhaps move us toward wide-scale online voting in the future. Today, Dr. Benaloh gives us a brief but fascinating history of elections, explains how the trade-offs among privacy, security and verifiability make the relatively easy math of elections such a hard problem for the internet, and tells the story of how the University of Michigan fight song forced the cancellation of an internet voting pilot.

Humans are unique in their ability to learn from, understand the world through and communicate with language… Or are they? Perhaps not for long, if Dr. Layla El Asri, a Research Manager at Microsoft Research Montreal, has a say in it. She wants you to be able to talk to your machine just like you’d talk to another person. That’s the easy part. The hard part is getting your machine to understand and talk back to you like it’s that other person. Today, Dr. El Asri talks about the particular challenges she and other scientists face in building sophisticated dialogue systems that lay the foundation for talking machines. She also explains how reinforcement learning, in the form of a text game generator called TextWorld, is helping us get there, and relates a fascinating story from more than fifty years ago that reveals some of the safeguards necessary to ensure that when we design machines specifically to pass the Turing test, we design them in an ethical and responsible way.

If every question in life could be answered by choosing from just a few options, machine learning would be pretty simple, and life for machine learning researchers would be pretty sweet. Unfortunately, in both life and machine learning, things are a bit more complicated. That’s why Dr. Manik Varma, Principal Researcher at MSR India, is developing extreme classification systems to answer multiple-choice questions that have millions of possible options and help people find what they are looking for online more quickly, more accurately and less expensively. On today’s podcast, Dr. Varma tells us all about extreme classification (including where in the world you might actually run into 10 or 100 million options), reveals how his Parabel and Slice algorithms are making high quality recommendations in milliseconds, and proves, with both his life and his work, that being blind need not be a barrier to extreme accomplishment.

Haiyan Zhang is a designer, technologist and maker of things (really cool technical things) who currently holds the unusual title of Innovation Director at the Microsoft Research lab in Cambridge, England. There, she applies her unusual skillset to a wide range of unusual solutions to real-life problems, many of which draw on novel applications of gaming technology in serious areas like healthcare. On today’s podcast, Haiyan talks about her unique “brain hack” approach to the human-centered design process, and discusses a wide range of projects, from the connected play experience of Zanzibar, to Fizzyo, which turns laborious breathing exercises for children with cystic fibrosis into a video game, to Project Emma, an application of haptic vibration technology that, somewhat curiously, offsets the effects of tremors caused by Parkinson’s disease.

From his deep technical roots as a principal researcher and founder of the Communications, Collaboration and Signal Processing group at MSR, through his tenure as Managing Director of the lab in Redmond, to his current role as Distinguished Engineer, Chief Scientist for Microsoft Research and manager of the MSR NExT Enable group, Dr. Rico Malvar has seen – and pretty well done – it all. Today, Dr. Malvar recalls his early years at a fledgling Microsoft Research, talks about the exciting work he oversees now, explains why designing with the user is as important as designing for the user, and tells us how a challenge from an ex-football player with ALS led to a prize winning hackathon project and produced the core technology that allows you to type on a keyboard without your hands and drive a wheelchair with your eyes.

You never know how an incident in your own life might inspire a breakthrough in science, but Dr. Cecily Morrison, a researcher in the Human Computer Interaction group at Microsoft Research Cambridge, can attest to how even unexpected events can cause us to see things through a different – more inclusive – lens and, ultimately, give rise to innovations in research that impact everyone. On today’s podcast, Dr. Morrison gives us an overview of what she calls the “pillars” of inclusive design, shares how her research is positively impacting people with health issues and disabilities, and tells us how having a child born with blindness put her in touch with a community of people she would otherwise never have met, and on the path to developing Project Torino, an inclusive physical programming language for children with visual impairments.

The entertainment industry has long offered us a vision of the perfect personal assistant: one that not only meets our stated needs but anticipates needs we didn’t even know we had. But these uber-assistants, from the preternaturally prescient Radar O’Reilly in the TV show M.A.S.H. to Tony Stark’s digital know-and-do-it-all Jarvis in Iron Man, have always lived in the realm of fiction or science fiction. That could all change, if Dr. Paul Bennett, Principal Researcher and Research Manager of the Information and Data Sciences group at Microsoft Research, has anything to say about it. He and his team are working to make machines “calendar and email aware,” moving intelligent assistance into the realm of science and onto your workstation. Today, Dr. Bennett brings us up to speed on the science of contextually intelligent assistants, explains how what we think our machines can do actually shapes what we expect them to do, and shares how current research in machine learning and data science is helping machines reason on our behalf in the quest to help us find the right information effortlessly.

When people first started making software, computers were relatively rare and there was no internet, so programming languages were designed to get the job done quickly and run efficiently, with little thought for security. But software is everywhere now, from our desktops to our cars, from the cloud to the internet of things. That’s why Dr. Jonathan Protzenko, a researcher in the RiSE – or Research in Software Engineering – group at Microsoft Research, is working on designing better software tools in order to make our growing software ecosystem safer and more secure. Today, Dr. Protzenko talks about what’s wrong with software (and why it’s vitally important to get it right), explains why there are so many programming languages (and tells us about a few he’s been working on), and finally, acts as our digital Sherpa for Project Everest, an assault on software integrity and confidentiality that aims to build and deploy a verified HTTPS stack.

The episode first aired in May, 2018.In the world of machine learning, there’s been a notable trade-off between accuracy and intelligibility. Either the models are accurate but difficult to make sense of, or easy to understand but prone to error. That’s why Dr. Rich Caruana, Principal Researcher at Microsoft Research, has spent a good part of his career working to make the simple more accurate and the accurate more intelligible.Today, Dr. Caruana talks about how the rise of deep neural networks has made understanding machine predictions more difficult for humans, and discusses an interesting class of smaller, more interpretable models that may help to make the black box nature of machine learning more transparent.