Drone racing has transformed from a niche activity sparked by enthusiastic hobbyists to an internationally televised sport. In parallel, computer vision and machine learning are making rapid progress, along with advances in agile trajectory planning, control, and state estimation for quadcopters. These advances enable increased autonomy and reliability for drones. More recently, the unmanned aerial vehicle (UAV) research community has begun to tackle the drone-racing problem. This has given rise to competitions, with the goal…
December 5th, 2019
Last summer we announced Microsoft Research Open Data—an Azure-based repository-as-a-service for sharing datasets—to encourage the reproducibility of research and make research data assets readily available in the cloud. Among other things, the project started a conversation between the community and Microsoft’s legal team about dataset licensing. Inspired by these conversations, our legal team developed a set of brand new data use agreements and released them for public comment on Github earlier this year. Today we’re…
December 5th, 2019
Memory is an important part of human intelligence and the human experience. It grounds us in the current moment, helping us understand where we are and, consequently, what we should do next. Consider the simple example of reading a book. The ultimate goal is to understand the story, and memory is the reason we’re able to do so. Memory allows us to efficiently store the information we encounter and later recall the details we’ve previously…
December 4th, 2019
Episode 101 | December 4, 2019 - Deep learning methodologies like supervised learning have been very successful in training machines to make predictions about the world. But because they’re so dependent upon large amounts of human-annotated data, they’ve been difficult to scale. Dr. Phil Bachman, a researcher at MSR Montreal, would like to change that, and he’s working to train machines to collect, sort and label their own data, so people don’t have to. On…
December 4th, 2019
Imagine moving to a new city. You want to get from your new home to your new job. Unfamiliar with the area, you ask your co-workers for the best route, and as far as you can tell ... they’re right! You get to work and back easily. But as you acclimate, you begin to wonder: Is there a more scenic route, perhaps, or a route that passes by a good coffee spot? The fundamental question…
December 2nd, 2019
Episode 100 | November 27, 2019 - There’s a lot of excitement around self-driving cars, delivery drones, and other intelligent, autonomous systems, but before they can be deployed at scale, they need to be both reliable and safe. That’s why Gurdeep Pall, CVP of Business AI at Microsoft, and Dr. Ashish Kapoor, who leads research in Aerial Informatics and Robotics, are using a simulated environment called AirSim to reduce the time, cost and risk of…
November 27th, 2019
One of the core directions of Project Malmo is to develop AI capable of rich interactions. Whether that means learning new skills to apply to challenging problems, understanding complex environments, or knowing when to enlist the help of humans, reinforcement learning (RL) is a core enabling technology for building these types of AI. In order to perform RL well, agents need to do exploration efficiently, which means understanding when to try new things out and…
November 26th, 2019
In many real-life scenarios, obtaining information is costly, and getting fully observed data is almost impossible. For example, in the recruiting world, obtaining relevant information (in other words, a feature value) for a company could mean performing time-consuming interviews. The same applies to many other scenarios, such as in education and the medical field, where each feature value is an often more complex answer to a question. Unfortunately, AI-aided decision making usually requires large amounts…
November 25th, 2019
While reinforcement learning (RL) has seen significant successes over the past few years, modern deep RL methods are often criticized for how sensitive they are with respect to their hyper-parameters. One such hyper-parameter is the discount factor, which controls how future rewards are weighted compared to immediate rewards. The objective that one wants to optimize in RL is often best described as an undiscounted sum of rewards (for example, maximizing the total score in a…
November 21st, 2019
Episode 99 | November 20, 2019 - Dr. Sumit Gulwani is a programmer’s programmer. Literally. A Partner Research Manager in the Program Synthesis, or PROSE, group at Microsoft Research, Dr. Gulwani is a leading researcher in program synthesis and the inventor of many intent-understanding, programming-by-example and programming-by-natural language technologies – aka, the automation of “what I meant to do and wanted to do, but my computer wouldn’t let me” tasks. Today, Dr. Gulwani gives us…
November 20th, 2019
Episode 98 | November 13, 2019 - Computer programming has often been perceived as the exclusive domain of computer scientists and software engineers. But that’s changing, thanks to the work of people like Dr. Thomas Ball, a Partner Researcher in the RiSE group at Microsoft Research, and Dr. Teddy Seyed, a post-doctoral researcher in the same group. Their goal is to make programming accessible to non-programmers in places like the classroom, the workshop… and even…
November 13th, 2019
Episode 97 | November 6, 2019 - Remember when a hard drive that could hold a terabyte of data was a big deal? Well, we’re now in an era where peta-, exa- and even zetta-bytes are the bytes of the day, and it turns out it’s hard to fit that many zeroes on a hard drive. That’s where Dr. Ant Rowstron, Deputy Lab Director of Microsoft Research Cambridge, and Mark Russinovich, Chief Technical Officer of Azure, come in. Their respective…
November 6th, 2019
