Artificial Intelligence
Building intelligent machines that complement human reasoning and extend what people can do.
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Demo: Enabling end-to-end causal inference at scale
This session will present the two popular open-source tools for causal inference, DoWhy and EconML, developed by Microsoft Research. In this demo, researchers Amit Sharma and Eleanor Dillon will describe how the integrated toolkit (DoWhy+EconML)…
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Research talk: Large-scale, self-supervised pretraining: From language to vision
Over the past years, large-scale pretrained models with billions of parameters have improved the state of the art in nearly every natural language processing (NLP) task. These models are fundamentally changing the research and development…
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Technology demo: Using technology to combat human trafficking
Microsoft is a founding member of Tech Against Trafficking (TAT) – a coalition of organizations working to combat human trafficking with technology. In this session, we will introduce partnerships with the Counter Trafficking Data Collaborative…
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Panel: Computer vision in the next decade: Deeper or broader
Deep learning plus huge training data is a popular paradigm in computer vision. However, after a decade of growth, it’s time to revisit its strengths and weaknesses. Will there be a new trend in computer…
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Research talk: Evaluating human-like navigation in 3D video games
On the path to developing agents that learn complex human-like behavior, a key challenge is the need to quickly and accurately quantify human-likeness. While human assessments of such behavior can be highly accurate, speed and…
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Research talk: Post-contextual-bandit inference
Contextual bandit algorithms are increasingly replacing non-adaptive A/B tests in e-commerce, healthcare, and policymaking because they can both improve outcomes for study participants and increase the chance of identifying good or even best policies. To…
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Panel: Causal ML Research at Microsoft
Causal machine learning is poised to be the next AI revolution, providing a firm foundation for robust predictions, efficient decisions and human-interpretable explanations. This panel brings together a subset of experts across the Microsoft Research…
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Opening remarks: Towards Human-Like Visual Learning and Reasoning
Big data-driven deep learning has helped significantly improve the performance of visual tasks in the past few years, but it has also exhibited limitations in scalability and adaptation to real-world scenarios. Researchers and practitioners are…
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Panel: Cloud Intelligence/AIOps across academia and industry
Cloud service plays an increasingly important role in our daily life, and the quality of cloud platforms, including reliability, availability, performance, capacity efficiency, security, sustainability, etc., has become immensely important. “Cloud Intelligence/AIOps” is designed to…
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Research talk: Towards efficient generalization in continual RL using episodic memory
Reinforcement learning (RL) is a powerful, brain-inspired framework to train agents for making sequential decisions in artificial intelligence. In this talk, the researchers consider two scenarios wherein RL can be challenging. The first is when…
