Episode 104 | January 29, 2020 - Dr. Jianfeng Gao is a veteran computer scientist, an IEEE Fellow and the current head of the Deep Learning Group at Microsoft Research. He and his team are exploring novel approaches to advancing the state-of-the-art on deep learning in areas like NLP, computer vision, multi-modal intelligence and conversational AI. Today, Dr. Gao gives us an overview of the deep learning landscape and talks about his latest work on…
January 29th, 2020
Consider a person who applies for a loan with a financial company, but their application is rejected by a machine learning algorithm used to determine who receives a loan from the company. How would you explain the decision made by the algorithm to this person? One option is to provide them with a list of features that contributed to the algorithm’s decision, such as income and credit score. Many of the current explanation methods provide…
January 28th, 2020
I am pleased to announce the winners of the new Microsoft Investigator Fellowship. This fellowship is designed to empower researchers of all disciplines who plan to make an impact with research and teaching using the Microsoft Azure cloud computing platform. Each fellowship provides $100,000 USD annually for two years and various training and community events. We received over 290 proposals from an impressive group of faculty researchers. Ultimately, 15 fellowships were awarded. “The unique value…
January 28th, 2020
In their second and thirteenth years respectively, the Ada Lovelace Fellowship and PhD Fellowship continue a Microsoft Research tradition of providing promising doctoral students in North America with funding to support their studies and research. This year, there were over 600 submissions between both fellowships. Microsoft Research is proud to announce the 2020 recipients for each of these awards. The breadth of fellows’ pursuits speaks to the increasingly broad impact of technology in the world…
January 23rd, 2020
Jan. 22, 2020 - In this webinar led by Microsoft researcher Jenn Wortman Vaughan, explore how to best incorporate transparency into the machine learning life cycle. Here, we will explore these three components of transparency (with examples): traceability, communication, and intelligibility.
January 22nd, 2020
Thanks to advances in computer vision and deep neural networks (DNNs) in what can arguably be described as the golden age of vision, AI, and machine learning, video analytics systems—systems performing analytics on live camera streams—are becoming more accurate. This accuracy offers opportunities to support individuals and society in exciting ways, like informing homeowners when a package has been delivered outside their door, allowing people to give their pets the attention they need when out…
January 22nd, 2020
Episode 103 | January 22, 2020 - Dr. Sriram Rajamani is a Distinguished Scientist and the Managing Director of the Microsoft Research lab in Bangalore. He’s dedicated his career to advancing globally applicable science in the testbed that is India. He is, by any measure, a world-class researcher and leader. He’s also, as you’ll find out shortly, a world-class storyteller! On the podcast, Dr. Rajamani talks about the unique challenges and opportunities of leading MSR’s…
January 22nd, 2020
Is bias in AI self-reinforcing? Decision-making systems that impact criminal justice, financial institutions, human resources, and many other areas often have bias. This is especially true of algorithmic systems that learn from historical data, which tends to reflect existing societal biases. In many high-stakes applications, like hiring and lending, these decision-making systems may even reshape the underlying populations. When the system is retrained on future data, it may become not less but more detrimental to…
January 21st, 2020
You’re in a field. In front of you, there’s a white house. The door is boarded shut. The immediate challenge—investigate the house. The game—Zork I: The Great Underground Empire, a treasure-seeking adventure in which you’ll encounter monsters, a thief, and other interesting characters along the way. As a player of this text-based game, you string together simple commands of only several words, like “walk to the house.” Once there, you type a series of commands,…
January 16th, 2020
Jan. 15, 2020 - In this webinar led by Microsoft researcher Dr. Katja Hofmann, a Principal Researcher in the Game Intelligence group at Microsoft Research Cambridge, learn about the foundations of RL—elegant ideas giving rise to agents that can learn extremely complex behaviors in a wide range of settings. In the broader perspective, gain an overview of where we currently stand in terms of what is possible in RL from the researcher's perspective. The webinar concludes with…
January 15th, 2020
There is a growing interest in the use of deep generative models for sampling high-dimensional data; examples include high-resolution natural images, long-form text generation, designing pharmaceutical drugs, and creating new materials at the molecular level. Training these models is, however, an arduous task. Even state-of-the-art models have noticeable deficiencies in some of the generated samples: image models of faces have artifacts in the hair textures and makeup, text models often require repeated attempts at generating…
January 14th, 2020
Research is about achieving long-term goals, often through incremental progress. As the year comes to an end, it’s a good time to step back and reflect on the work that researchers at Microsoft and their collaborators have done to advance the state of the art in computing, particularly by increasing the capabilities and reach of AI and delivering technology experiences that are more inclusive, secure, and accessible. This covers only a sliver of all the…
December 23rd, 2019
