Artificial Intelligence
Building intelligent machines that complement human reasoning and extend what people can do.
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Research talk: Computationally efficient large-scale AI
Today’s AI is too big. Deep neural networks demand extraordinary levels of computation, and therefore power and carbon, for training and inference. In this research talk, Song Han, MIT, presents TinyML and efficient deep learning…
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Keynote: Unlocking exabytes of training data through privacy preserving machine learning
Speaker: Jim Kleewein, Technical Fellow, Microsoft Data is the lifeblood of AI and machine learning. The better the model, and the better the data that model is trained on, the better the outcome. Today’s models…
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Opening remarks: Causal Machine Learning
Causal machine learning is an increasingly important, but not well understood, technology. It’s a necessary precursor to building more human-like machine intelligence, and an integral factor in the fields of information, data and computer science.…
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Demo: Using network machine learning for organizational analytics
Recent developments in network machine learning have opened up new opportunities in the realm of organizational behavior. Learn more about these methods and techniques in this demonstration in the domain of organizational analytics. Learn more about…
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Research talks: Learning for interpretability
Speakers: Hanwang Zhang, Professor, Nanyang Technological University Yuwang Wang, Senior Researcher, Microsoft Research Asia Shujian Yu, Professor, UiT – The Arctic University of Norway One of the critical shortcomings of big data-driven deep learning is…
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Research talks: AI for software development
This session will showcase a future in which AI automates much of the tedious and error-prone collaborative software development tasks. We take a broad, end-to-end view of software development that consists of not only coding…
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Research talk: Challenges and opportunities in causal machine learning
This talk will highlight the big challenges in causal ML research and present our vision for development and use of causal ML technology for real-world decision making. Microsoft Researchers will focus on what’s needed to…
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Practical tips for productivity & wellbeing: Transitioning across the work-life boundary
Learn more about the 2021 Microsoft Research Summit: https://Aka.ms/researchsummit
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Research talk: Safe reinforcement learning using advantage-based intervention
Many sequential decision problems involve finding a policy that maximizes total reward while obeying safety constraints. Although much recent research has focused on the development of safe reinforcement learning (RL) algorithms that produce a safe…
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Research talk: Domain-specific pretraining for vertical search
Information overload is a prevalent challenge in many high-value domains. Search in biomedicine, and many other vertical domains, is challenging due to the scarcity of direct supervision from click logs. Self-supervised learning has emerged as…
