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.

Most recent

  1. By making text-based games more accessible to RL agents, Jericho framework opens up exciting natural language challenges

    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

  2. Are all samples created equal?: Boosting generative models via importance weighting

    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

  3. Microsoft Research 2019 reflection—a year of progress on technology’s toughest challenges

    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

  4. Finding the best learning targets automatically: Fully Parameterized Quantile Function for distributional RL

    Reinforcement learning has achieved great success in game scenarios, with RL agents beating human competitors in such games as Go and poker. Distributional reinforcement learning, in particular, has proven to be an effective approach for training an agent to maximize reward, producing state-of-the-art results on Atari games, which are widely used as benchmarks for testing RL algorithms. Because of the intrinsic randomness of game environments—with the roll of the dice in Monopoly, for example, you…

    December 18th, 2019

  5. Making machines recognize and transcribe conversations in meetings using audio and video

    The ability to perceive communication signals and make sense of them played an essential role in the evolution of human intelligence. Computing technology is following the same trajectory. Now, computer vision and automatic speech recognition (ASR) technologies have enabled the advent of many artificial intelligence (AI) applications and virtual assistants by allowing machines to see and hear in the physical world. However, we have a long path ahead of us before machines are able to…

    December 13th, 2019

  6. Next-generation architectures bridge gap between neural and symbolic representations with neural symbols

    In both language and mathematics, symbols and their mutual relationships play a central role. The equation x = 1/y asserts the symbols x and y—that is, what they stand for—are related reciprocally; Kim saw the movie asserts that Kim and the movie are perceiver and stimulus. People are extremely adept with the symbols of language and, with training, become adept with the symbols of mathematics. For many decades, cognitive science explained these human abilities by…

    December 12th, 2019

  7. FastSpeech: New text-to-speech model improves on speed, accuracy, and controllability

    Text to speech (TTS) has attracted a lot of attention recently due to advancements in deep learning. Neural network-based TTS models (such as Tacotron 2, DeepVoice 3 and Transformer TTS) have outperformed conventional concatenative and statistical parametric approaches in terms of speech quality. Neural network-based TTS models usually first generate a mel-scale spectrogram (or mel-spectrogram) autoregressively from text input and then synthesize speech from the mel-spectrogram using a vocoder. (Note: the Mel scale is used…

    December 11th, 2019

  8. Adaptive systems, machine learning and collaborative AI with Dr. Besmira Nushi

    Episode 102 | December 11, 2019 - With all the buzz surrounding AI, it can be tempting to envision it as a stand-alone entity that optimizes for accuracy and displaces human capabilities. But Dr. Besmira Nushi, a senior researcher in the Adaptive Systems and Interaction group at Microsoft Research, envisions AI as a cooperative entity that enhances human capabilities and optimizes for team performance. On the podcast, Dr. Nushi talks about what it takes to…

    December 11th, 2019

  9. 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

  10. 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

  11. 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

  12. 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