Artificial Intelligence
Building intelligent machines that complement human reasoning and extend what people can do.
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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…
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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…
