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Climate change is one of the most pressing issues of our time, requiring rapid transformation across virtually every sector of society. In this talk, I describe what this means for research and practice in AI. AI has a multi-faceted relationship with climate change, through a combination of its direct environmental footprint, the impacts of its applications (both good and bad), and the broader systemic shifts it induces. Ultimately, most work in AI has significant implications for climate action, whether or not it is viewed as traditionally “climate-relevant.” Given this, I discuss how the AI community can better align its work with climate action: through the kinds of methods we develop, the kinds of applications we work on, the choices we make while working on these applications, and the ways we communicate with the public about our work.
Tiny Papers Poster Session 1
Blog Track Session 1
GLM-4 is a large language model with billions of parameters, capable of processing vast amounts of natural language text data, thereby enabling more accurate and natural language generation and understanding.
Compared with the previous generation, GLM-4 introduces significant improvements on various benchmarks, such as MMLU, GSM8K and MATH. It also supports a context length of 128k tokens and achieves almost 100% accuracy even with lengthy text inputs. The model incorporates GLM-4 All Tools, an intelligent agent feature capable of autonomously understanding and executing complex instructions, enabling interactions with web browsers, code interpreters, and multimodal text-generation models.
Surrounding GLM-4, we have also developed a series of models, forming a relatively complete large model full-stack technology system that covers multimodal, code generation, search enhancement, and dialogue.
Recent advances on diffusion and GAN
The rapid rise of deep generative models signifies a fundamental shift in AI research and applications, prompting significant questions worthy of exploration. There is a resurgence of interest in integrating Generative Adversarial Networks (GANs) with the DM to achieve rapid and high-fidelity generation. This event seeks to spark interactive discussions focused on improving training and sampling efficiency within the domain of DM and GAN.
Priya Donti is an Assistant Professor and the Silverman (1968) Family Career Development Professor at MIT EECS and LIDS. Her research focuses on machine learning for forecasting, optimization, and control in high-renewables power grids. Methodologically, this entails exploring ways to incorporate relevant physics, hard constraints, and decision-making procedures into deep learning workflows. Priya is also the co-founder and Chair of Climate Change AI, a global nonprofit initiative to catalyze impactful work at the intersection of climate change and machine learning. Priya received her Ph.D. in Computer Science and Public Policy from Carnegie Mellon University, and is a recipient of the MIT Technology Review’s 2021 “35 Innovators Under 35” award, the ACM SIGEnergy Doctoral Dissertation Award, the Siebel Scholarship, the U.S. Department of Energy Computational Science Graduate Fellowship, and best paper awards at ICML (honorable mention), ACM e-Energy (runner-up), PECI, the Duke Energy Data Analytics Symposium, and the NeurIPS workshop on AI for Social Good.
Dr. Lili Mou is an Assistant Professor at the Department of Computing Science, University of Alberta. He is also an Alberta Machine Intelligence Institute (Amii) Fellow and a Canada CIFAR AI (CCAI) Chair. Lili received his BS and PhD degrees in 2012 and 2017, respectively, from School of EECS, Peking University. After that, he worked as a postdoctoral fellow at the University of Waterloo. His research focuses on developing novel machine learning methods and frameworks for natural language processing. Examples include neuro-symbolic reasoning, edit-based local search for text generation, and controllable non-autoregressive text generation. He has more than 50 publications at top-tier conferences and journals, including AAAI, ACL, EMNLP, ICLR, ICML, IJCAI, NAACL-HLT, NeruIPS, and TACL (in alphabetic order). He also presented tutorials at EMNLP-IJCNLP'19 and ACL'20. He received a AAAI New Faculty Highlight Award in 2021.
Tiny Papers Oral Session 1
Anna Rumshisky is an Associate Professor at the Department of Computer Science at University of Massachusetts Lowell where she leads The Text Machine Lab for natural language processing. She is a Research Affiliate in the Clinical Decision-Making Group within the Computer Science and Artificial Intelligence Laboratory at MIT.
She was previously a Postdoctoral Associate in the Clinical Decision-Making Group within the Computer Science and Artificial Intelligence Laboratory at MIT, as well as a Visiting Research Scientist and Lecturer at the Laboratory for Linguistics and Computation (LLC) within the Computer Science Department at the Volen National Center for Complex Systems at Brandeis University.
Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge and a Fellow at The Alan Turing Institute in London. In addition to leading the van der Schaar Lab, Mihaela is founder and director of the Cambridge Centre for AI in Medicine (CCAIM).
Mihaela was elected IEEE Fellow in 2009. She has received numerous awards, including the Oon Prize on Preventative Medicine from the University of Cambridge (2018), a National Science Foundation CAREER Award (2004), 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award.
Mihaela is personally credited as inventor on 35 USA patents (the majority of which are listed here), many of which are still frequently cited and adopted in standards. She has made over 45 contributions to international standards for which she received 3 ISO Awards. In 2019, a Nesta report determined that Mihaela was the most-cited female AI researcher in the U.K.
Furong Huang is an Assistant Professor of the Department of Computer Science at University of Maryland. She received her Ph.D. in electrical engineering and computer science from UC Irvine in 2016, after which she spent one year as a postdoctoral researcher at Microsoft Research NYC. She works on statistical and trustworthy machine learning, foundation models and reinforcement learning, with specialization in domain adaptation, algorithmic robustness and fairness. With a focus on high-dimensional statistics and sequential decision-making, she develops efficient, robust, scalable, sustainable, ethical and responsible machine learning algorithms. She is recognized for her contributions with awards including best paper awards, the MIT Technology Review Innovators Under 35 Asia Pacific, the MLconf Industry Impact Research Award, the NSF CRII Award, the Microsoft Accelerate Foundation Models Research award, the Adobe Faculty Research Award, three JP Morgan Faculty Research Awards and Finalist of AI in Research - AI researcher of the year for Women in AI Awards North America.
Copyright Fundamentals for AI Researchers
This talk will cover fundamental legal principles all AI researchers should understand about copyright law. This talk will explore the current state of copyright law with respect to AI in the U.S., potential claims and defenses, as well as practical tips for minimizing legal risk.