Artificial Intelligence
Building intelligent machines that complement human reasoning and extend what people can do.
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Panel: Causal ML at Microsoft
Causal reasoning and machine learning is widely deployed across Microsoft, to support high-stakes internal decision-making and to build products that help our customers make better use of their own data. This panel, moderated by AI…
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Research talk: WebQA: Multihop and multimodal
Web search is fundamentally multimodal and multihop. Often, even before asking a question, individuals go directly to image search to find answers. Further, rarely do we find an answer from a single source, opting instead…
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Tutorial: Create human-centered AI with the Human-AI eXperience (HAX) Toolkit
There’s been a push to build AI technologies that benefit people and society while also mitigating potential harm. To accomplish this, it’s important to take a holistic approach. We aim to help AI creators make…
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Research talk: Local factor models for large-scale inductive recommendation
In many domains, user preferences are similar locally within like-minded subgroups of users, but typically differ globally between those subgroups. Local recommendation models were shown to substantially improve top-k recommendation performance in such settings. However,…
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Research talk: Knowledgeable pre-trained language models
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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Panel: Large-scale neural platform models: Opportunities, concerns, and directions
Large-scale, pretrained neural models are driving significant research and development across multiple AI areas. They have played a major role in research efforts and have been at the root of leaps forward in capabilities in…
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Opening remarks: Responsible AI
As we’ve seen in countless media articles, AI systems can behave unfairly or unreliably. They can generate undesirable or harmful content, and they can reproduce or exacerbate existing social inequities. This track explores the complex…
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Research talk: Causality for medical image analysis
Machine learning has huge potential to augment medical image analysis workflows and improve patient care. However, two of its notorious real-world challenges are the difficulty in acquiring sufficient, high-quality annotated data and mismatches between the…
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Closing remarks: Empowering software developers and mathematicians with next-generation AI
Machine learning systems have become increasingly capable of making fast, plausible predictions in diverse situations. Combining this capability with the reliability of symbolic reasoning will produce next generation AI that will empower developers and mathematicians…
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Panel: Maximizing benefits and minimizing harms with language technologies
Language is one of the main ways in which people understand and construct the social world. Current language technologies can contribute positively to this process—by challenging existing power dynamics, or negatively—by reproducing or exacerbating existing…
