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. Project Rocket platform—designed for easy, customizable live video analytics—is open source

    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

  2. Innovating in India with Dr. Sriram Rajamani

    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

  3. When bias begets bias: A source of negative feedback loops in AI systems

    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

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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

  11. 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

  12. 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