The Conference and Workshop on Neural Information Processing Systems is a machine learning and computational neuroscience conference held every December. Along with ICLR and ICML, it is one of the three primary conferences of high impact in machine learning and artificial intelligence research.
The International Conference on Machine Learning (ICML) is the leading international academic conference in machine learning. Along with NeurIPS and ICLR, it is one of the three primary conferences of high impact in machine learning and artificial intelligence research. It is supported by the International Machine Learning Society (IMLS). Precise dates vary year to year, but paper submissions are generally due at the end of January, and the conference is generally held the following July. The first ICML was held 1980 in Pittsburgh.
The AAAI Conference on Artificial Intelligence (AAAI) is a leading international academic conference in artificial intelligence held annually. It ranks 4th in terms of H5 Index in Google Scholar's list of top AI publications, after ICLR, NeurIPS, and ICML. It is supported by the Association for the Advancement of Artificial Intelligence. Precise dates vary from year to year, but paper submissions are generally due at the end of August to beginning of September, and the conference is generally held during the following February. The first AAAI was held in 1980 at Stanford University, Stanford California.
Empirical Methods in Natural Language Processing (EMNLP) is a leading conference in the area of natural language processing and artificial intelligence. Along with the Association for Computational Linguistics (ACL) and the North American Chapter of the Association for Computational Linguistics (NAACL), it is one of the three primary high impact conferences for natural language processing research. EMNLP is organized by the ACL special interest group on linguistic data (SIGDAT) and was started in 1996, based on an earlier conference series called Workshop on Very Large Corpora (WVLC).
Raquel Urtasun is a professor at the University of Toronto. Urtasun uses artificial intelligence, particularly deep learning, to make vehicles and other machines perceive the world more accurately and efficiently.
Yee-Whye Teh is a professor of statistical machine learning in the Department of Statistics, University of Oxford. Prior to 2012 he was a reader at the Gatsby Charitable Foundation computational neuroscience unit at University College London. His work is primarily in machine learning, artificial intelligence, statistics and computer science.
Deep reinforcement learning is a subfield of machine learning that combines reinforcement learning (RL) and deep learning. RL considers the problem of a computational agent learning to make decisions by trial and error. Deep RL incorporates deep learning into the solution, allowing agents to make decisions from unstructured input data without manual engineering of the state space. Deep RL algorithms are able to take in very large inputs and decide what actions to perform to optimize an objective. Deep reinforcement learning has been used for a diverse set of applications including but not limited to robotics, video games, natural language processing, computer vision, education, transportation, finance and healthcare.
Multi-agent reinforcement learning (MARL) is a sub-field of reinforcement learning. It focuses on studying the behavior of multiple learning agents that coexist in a shared environment. Each agent is motivated by its own rewards, and does actions to advance its own interests; in some environments these interests are opposed to the interests of other agents, resulting in complex group dynamics.
Sanja Fidler is an associate professor at the University of Toronto and Director of AI at Nvidia. She is also a co-founder of the Vector Institute, University of Toronto and Canada CIFAR AI Chair. Her research is in the areas of computer vision and artificial intelligence.
The ITU-WHO Focus Group on Artificial Intelligence for Health is an inter-agency collaboration between the World Health Organization and the ITU, which created a benchmarking framework to assess the accuracy of AI in health.
Yejin Choi is Wissner-Slivka Chair of Computer Science at the University of Washington. Her research considers natural language processing and computer vision.
Adji Bousso Dieng is a Senegalese Computer Scientist and Statistician working in the field of Artificial Intelligence. Her research bridges probabilistic graphical models and deep learning to discover meaningful structure from unlabelled data. She is currently an Artificial Intelligence Research Scientist at Google Brain in Mountain View, California. In 2021, she will start her tenure-track faculty position at Princeton University becoming the first Black female faculty member in the School of Engineering and Applied Science as well as the first Black faculty member ever in the Department of Computer Science. Dieng recently founded the non-profit “The Africa I Know” (TAIK) with the goal to inspire young Africans to pursue careers in STEM and AI by showcasing African role models, informing the general public about developments in STEM and AI by Africans, and educating the general public about the rich history of Africa.
Black in AI, formally called the Black in AI Workshop, is a technology research organization and affinity group, founded by computer scientists Timnit Gebru and Rediet Abebe in 2017. It started as a conference workshop, later pivoting into an organization. Black in AI increases the presence and inclusion of Black people in the field of artificial intelligence (AI) by creating space for sharing ideas, fostering collaborations, mentorship, and advocacy.
Samy Bengio is a Canadian computer scientist, Senior Director of AI and Machine Learning Research at Apple, and a former long-time scientist at Google known for leading a large group of researchers working in machine learning including adversarial settings. Bengio left Google shortly after the company fired his report, Timnit Gebru, without first notifying him. At the time, Bengio said that he had been "stunned" by what happened to Gebru. He is also among the three authors who developed Torch in 2002, the ancestor of PyTorch, one of today's two largest machine learning frameworks.
Abeba Birhane is an Ethiopian-born cognitive scientist who works at the intersection of complex adaptive systems, machine learning, algorithmic bias, and critical race studies. Birhane's work with Vinay Prabhu uncovered that large-scale image datasets commonly used to develop AI systems, including ImageNet and 80 Million Tiny Images, carried racist and misogynistic labels and offensive images. She has been recognized by VentureBeat as a top innovator in computer vision and named as one of the 100 most influential persons in AI 2023 by TIME magazine.
François Chollet is a French software engineer and artificial intelligence researcher currently working at Google. Chollet is the creator of the Keras deep-learning library, released in 2015. His research focuses on computer vision, the application of machine learning to formal reasoning, abstraction, and how to achieve greater generality in artificial intelligence.
Meta AI is an American company owned by Meta that develops artificial intelligence and augmented and artificial reality technologies. Meta AI deems itself an academic research laboratory, focused on generating knowledge for the AI community, and should not be confused with Meta's Applied Machine Learning (AML) team, which focuses on the practical applications of its products.
Mi Zhang is a computer scientist at Ohio State University, where he is an Associate Professor of Computer Science and Engineering and the director of AIoT and Machine Learning Systems Lab. He is best known for his work in Edge AI, Artificial Intelligence of Things (AIoT), machine learning systems, and mobile health.
Sébastien Bubeck is a French-American computer scientist and mathematician. He is currently Microsoft's Vice President of GenAI and leads the Machine Learning Foundations group at Microsoft Research Redmond. Bubeck was formerly professor at Princeton University and a researcher at the University of California, Berkeley. He is known for his contributions to online learning, optimization and more recently studying deep neural networks, and in particular transformer models.
AI safety is an interdisciplinary field focused on preventing accidents, misuse, or other harmful consequences arising from artificial intelligence (AI) systems. It encompasses machine ethics and AI alignment, which aim to ensure AI systems are moral and beneficial, as well as monitoring AI systems for risks and enhancing their reliability. The field is particularly concerned with existential risks posed by advanced AI models.
