Microsoft Research Podcast

Artificial intelligence has captured our imagination and made many things we would have thought impossible only a few years ago seem commonplace today. But AI has also raised some challenging issues for society writ large. Enter Dr. Kate Crawford, a principal researcher at the New York City lab of Microsoft Research. Dr. Crawford, along with an illustrious group of colleagues in computer science, engineering, social science, business and law, has dedicated her research to addressing the social implications of AI, including big topics like bias, labor and automation, rights and liberties, and ethics and governance.

Today, Dr. Crawford talks about both the promises and the problems of AI; why— when it comes to data – bigger isn’t necessarily better; and how – even in an era of increasingly complex technological advances – we can adopt AI design principles that empower people to shape their technical tools in ways they’d like to use them most.

With all the sensational headlines about artificial intelligence, it’s reassuring to know that some of the world’s most brilliant minds are developing AI systems for entirely practical reasons. One of those minds belongs to Dr. Antonio Criminisi, a Principal Researcher at Microsoft Research in Cambridge, England. And one of those reasons is to help medical professionals provide better healthcare to their patients.

Today, Dr. Criminisi talks about Project InnerEye, an innovative machine learning tool that helps radiologists identify and analyze 3-D images of cancerous tumors. He also gives us some insight into his work on deep neural decision forests and tells us how gaming algorithms made their way into medical technology, moving from gamer to patient, and turning outside-in imaging… inside-out.

If you’ve ever wondered if you could find the perfect combination of computer scientist… and Macgyver, look no further than Dr. Peli de Halleux, principal Research Software Design Engineer at Microsoft Research. A key member of the MSR RiSE team, Peli is part of the MakeCode initiative that brings physical computing to classrooms around the country and around the world. Today, Peli talks about the Maker Movement in K-12 education, the hard work that goes on behind the scenes to deliver a “seamless” user experience for both kids and teachers, and how to get children excited about coding through hands on experience in early computer science education.

Big data is a big deal, and if you follow the popular technical press, you’ll have heard all the metaphors: data is the new oil, the new bacon, the new currency, the new electricity. It’s even been called the new black. While data may not actually be any of these things, we can say this: in today’s networked world, data is increasingly valuable and it is essential to research, both basic and applied.

Today, we welcome a special guest to the podcast. Dr. Igor Perisic is the Vice President of Engineering and Chief Data Officer at LinkedIn, the social network for business and employment. Today, Dr. Perisic talks about the key attributes of a data scientist, how AI and machine learning are helping personalize member experiences, why we should all be big open source fans, and how LinkedIn is partnering with other researchers through their innovative Economic Graph program to create economic opportunity for every member of the global workforce.

All this and much more on this episode of the Microsoft Research Podcast.

Every day, computers take on more and more of our daily tasks. Fill in a few cells on your spreadsheet? It’ll fill in the rest. Ask your car for directions? It’ll get you there. Anymore, we can program computers to do almost anything. But what about programming computers to… program computers? That’s a task that Dr. Rishabh Singh, and the team in the Cognition group at Microsoft Research, are tackling with Neural Program Synthesis, also known as artificial programming.

Today, Dr. Singh explains how deep neural networks are already training computers to do things like take classes and grade assignments, shares how programmers can perform complicated, high-level debugging through the delightfully named process of neural fuzzing, and lays out his vision to democratize computer programming in the brave new world of Software 2.0.