Microsoft Research Blog

The Microsoft Research blog provides in-depth views and perspectives from our researchers, scientists and engineers, plus information about noteworthy events and conferences, scholarships, and fellowships designed for academic and scientific communities.

  1. Provable guarantees come to the rescue to break attack-defense cycle in adversarial machine learning

    Artificial intelligence has evolved to become a revolutionary technology. It is rapidly changing the economy, both by creating new opportunities (it’s the backbone of the gig economy) and by bringing venerable institutions, like transportation, into the 21st century. Yet deep at its core something is amiss, and more and more experts are worried: the technology seems to be extremely brittle, a phenomenon epitomized by adversarial examples. Adversarial examples exploit weaknesses in modern AI. Today, most…

    December 10th, 2019

  2. Project Petridish: Efficient forward neural architecture search

    Having experience in deep learning doesn’t hurt when it comes to the often mysterious, time- and cost-consuming process of hunting down an appropriate neural architecture. But truth be told, no one really knows what works the best on a new dataset and task. Relying on well-known, top-performing networks provides few guarantees in a space where your dataset can look very different from anything those proven networks have encountered before. For example, a network that worked…

    December 9th, 2019

  3. Foundations of Real-World Reinforcement Learning Webinar

    Dec. 5, 2019 - In this webinar—led by Microsoft Researchers John Langford, Partner Research Manager with over a decade of experience in reinforcement learning-related research, and Alekh Agarwal, Principal Research Manager and leader of the Reinforcement Learning group in Redmond—learn how RL works to impact real-world problems across a variety of domains.

    December 5th, 2019

  4. Game of Drones at NeurIPS 2019: Simulation-based drone-racing competition built on AirSim

    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

  5. Microsoft Research Open Data Project: Evolving our standards for data access and reproducible research

    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

  6. Metalearned Neural Memory: Teaching neural networks how to remember

    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

  7. Going meta: learning algorithms and the self-supervised machine with Dr. Philip Bachman

    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

  8. The road less traveled: With Successor Uncertainties, RL agents become better informed explorers

    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

  9. Autonomous systems, aerial robotics and Game of Drones with Gurdeep Pall and Dr. Ashish Kapoor

    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

  10. Optimistic Actor Critic avoids the pitfalls of greedy exploration in reinforcement learning

    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

  11. Icebreaker: New model with novel element-wise information acquisition method reduces cost and data needed to train machine learning models

    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

  12. Logarithmic mapping allows for low discount factors by creating action gaps similar in size

    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