Microsoft Research Podcast

Latest Episodes:

123 - Econ3: Understanding the media ecosystem and how it informs public opinion in the internet age featuring Hunt Allcott and David Rothschild Jun 10, 2021

Show notes

In the world of economics, researchers at Microsoft are examining a range of complex systems—from those that impact the technologies we use to those that inform the laws and policies we create—through the lens of a social science that goes beyond the numbers to better understand people and society.

Interviewed by Senior Principal Researcher Hunt Allcott, Economist David Rothschild discusses how the news media has evolved alongside social media and the internet, from story development to distribution of news via aggregators and wire services. Rothschild illuminates how and where people are consuming news and shares some of the strategies he’s seeing news outlets use to appeal to their audiences. He also covers research insights into media bias, misinformation, and how this knowledge could inform the future of news for the better. In addition, the researchers talk about Rothschild’s work with Project Ratio, which looks at how the news ecosystem impacts public opinion and political polarization while providing a multi-faceted approach to understanding these outcomes through data and infrastructure.

https://www.microsoft.com/research

122 - Econ2: Causal machine learning, data interpretability, and online platform markets featuring Hunt Allcott and Greg Lewis Jun 02, 2021

Show notes

In the world of economics, researchers at Microsoft are examining a range of complex systems—from those that impact the technologies we use to those that inform the laws and policies we create—through the lens of a social science that goes beyond the numbers to better understand people and society.

In this episode, Senior Principal Researcher Dr. Hunt Allcott speaks with Microsoft Research New England office mate and Senior Principal Researcher Dr. Greg Lewis. Together, they cover the connection between causal machine learning and economics research, the motivations of buyers and sellers on e-commerce platforms, and how ad targeting and data practices could evolve to foster a more symbiotic relationship between customers and businesses. They also discuss EconML, a Python package for estimating heterogeneous treatment effects that Lewis has worked on as part of the ALICE (Automated Learning and Intelligence for Causation and Economics) project at Microsoft Research.

https://www.microsoft.com/research

121 - Econ1: Using microeconomics to solve mass incarceration featuring Hunt Allcott and Evan Rose May 19, 2021

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In this episode, Dr. Hunt Allcott, Senior Principal Researcher at Microsoft Research New England, talks with Dr. Evan Rose, Postdoctoral Researcher, whom Allcott describes as “one of the most engaging and talented researchers in applied microeconomics today.” They’ll discuss how Rose’s experience teaching adult learners at San Quentin State Prison has resonated throughout his research, and they’ll delve into what his and others’ work is uncovering about the criminal justice system today, including the effects of incarceration and parole, impacts of ban-the-box hiring practices, and racial disparities and discrimination.

https://www.microsoft.com/research

120 - Advancing Excel as a programming language with Andy Gordon and Simon Peyton Jones May 05, 2021

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Today, people around the globe—from teachers to small-business owners to finance executives—use Microsoft Excel to make sense of the information that occupies their respective worlds, and whether they realize it or not, in doing so, they’re taking on the role of programmer.

In this episode, Senior Principal Research Manager Andy Gordon, who leads the Calc Intelligence team at Microsoft Research, and Senior Principal Researcher Simon Peyton Jones provide an inside account of the journey Excel has taken as a programming language, including the expansion of data types that has unlocked greater functionality and the release of the LAMBDA function, which makes the Excel formula language Turing-complete. They’ll talk specifically about how research has influenced Excel and vice versa, programming as a human-computer interaction challenge, and a future in which Excel is the first language for budding programmers and a tool for incorporating probabilistic reasoning into our decision-making.

https://www.microsoft.com/research

119 - Defending DRAM for data safety and security in the cloud Jul 08, 2020

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Dynamic random-access memory – or DRAM – is the most popular form of volatile computer memory in the world but it’s particularly susceptible to Rowhammer, an adversarial attack that can cause data loss and security exploits in everything from smart phones to the cloud.

Today, Dr. Stefan Saroiu, a Senior Principal Researcher in MSR’s Mobility and Networking group, explains why DRAM remains vulnerable to Rowhammer attacks today, even after several years of mitigation efforts, and then tells us how a new approach involving bespoke extensibility mechanisms for DRAM might finally hammer Rowhammer in the fight to keep data safe and secure.

118 - Accessible systems for sign language computation with Dr. Danielle Bragg Jun 17, 2020

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Many computer science researchers set their sights on building general AI technologies that could impact hundreds of millions – or even billions – of people. But Dr. Danielle Bragg, a senior researcher at MSR’s New England lab, has a slightly smaller and more specific population in mind: the some seventy million people worldwide who use sign languages as their primary means of communication.

Today, Dr. Bragg gives us an insightful overview of the field and talks about the unique challenges and opportunities of building systems that expand access to information in line with the needs and desires of the deaf and signing community.

https://www.microsoft.com/research

117 - Provably efficient reinforcement learning with Dr. Akshay Krishnamurthy Jun 03, 2020

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MSR’s New York City lab is home to some of the best reinforcement learning research on the planet but if you ask any of the researchers, they’ll tell you they’re very interested in getting it out of the lab and into the real world. One of those researchers is Dr. Akshay Krishnamurthy and today, he explains how his work on feedback-driven data collection and provably efficient reinforcement learning algorithms is helping to move the RL needle in the real-world direction.

https://www.microsoft.com/research

116 - Harvesting randomness, HAIbrid algorithms and safe AI with Dr. Siddhartha Sen May 27, 2020

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Dr. Siddhartha Sen is a Principal Researcher in MSR’s New York City lab, and his research interests are, if not impossible, at least impossible sounding: optimal decision making, universal data structures, and verifiably safe AI.

Today, he tells us how he’s using reinforcement learning and HAIbrid algorithms to tap the best of both human and machine intelligence and develop AI that’s minimally disruptive, synergistic with human solutions, and safe.

036r - A conversation with Microsoft CTO Kevin Scott May 20, 2020

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This episode originally aired in August, 2018.

Kevin Scott has embraced many roles over the course of his illustrious career in technology: software developer, engineering executive, researcher, angel investor, philanthropist, and now, Chief Technology Officer of Microsoft. But perhaps no role suits him so well – or has so fundamentally shaped all the others – as his self-described role of “all-around geek.”

Today, in a wide-ranging interview, Kevin shares his insights on both the history and the future of computing, talks about how his impulse to celebrate the extraordinary people “behind the tech” led to an eponymous non-profit organization and a podcast, and… reveals the superpower he got when he was in grad school.

115 - Diving into Deep InfoMax with Dr. Devon Hjelm May 13, 2020

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Dr. Devon Hjelm is a senior researcher at the Microsoft Research lab in Montreal, and today, he joins me to dive deep into his research on Deep InfoMax, a novel self-supervised learning approach to training AI models – and getting good representations – without human annotation. He also tells us how an interest in neural networks, first human and then machine, led to an inspiring career in deep learning research.

https://www.microsoft.com/research