Top 10 AI researchers and leaders
AI researchers and leaders are people who are advancing the field of artificial intelligence through their scientific discoveries, innovations, and applications. Some of them are also professors, entrepreneurs, or executives who influence the direction and impact of AI in academia, industry, and society.
He is the founder and CEO of Landing AI, a company that helps enterprises adopt AI solutions. He is also the founder of deeplearning.ai, an online education platform that offers courses and resources on deep learning.
He was a co-founder and head of Google Brain, a project that developed large-scale neural networks for various applications. He was also the chief scientist at Baidu, where he led the company’s AI group. He is a pioneer in online education as a co-founder of Coursera, the world’s largest MOOC platform.
He is also a professor at Stanford University, where he teaches machine learning, deep learning, computer vision, and natural language processing.
She is the inaugural Sequoia Professor in the Computer Science Department at Stanford University. She is also the co-director of the Stanford Institute for Human-Centered Artificial Intelligence (HAI), which aims to advance AI research, education, policy, and practice to improve the human condition.
She is a leading expert in computer vision, especially in image recognition and understanding. She is the creator of ImageNet, a large-scale image database that has been widely used for training and benchmarking deep neural networks.
She was also the chief scientist of AI/ML at Google Cloud, where she led the development and deployment of AI products and services.
He is the senior director of artificial intelligence at Tesla, where he oversees the development of computer vision and deep learning systems for autonomous driving. He is also an adjunct professor at Stanford University, where he teaches convolutional neural networks for visual recognition.
He is a co-founder of Distill, an online journal that publishes interactive articles on machine learning research. He is also known for his popular blog posts and videos on deep learning topics, such as recurrent neural networks, reinforcement learning, and generative models.
He is the co-founder and CEO of DeepMind, a research company that focuses on creating artificial intelligence systems that can learn from their own experience and achieve general intelligence. He is also a fellow of the Royal Society, the Royal Academy of Engineering, and the Association for the Advancement of Artificial Intelligence (AAAI).
He is a pioneer in reinforcement learning, deep learning, and artificial neural networks. He is the lead researcher behind AlphaGo, AlphaZero, and AlphaFold, which are AI systems that have achieved superhuman performance in complex domains such as Go, chess, shogi, and protein folding.
He is the director of machine learning at Apple, where he leads the development of core ML technologies and applications. He is also an adjunct professor at Stanford University, where he teaches deep learning.
He is one of the inventors of generative adversarial networks (GANs), which are a class of neural networks that can generate realistic images, sounds, texts, and other data. He is also the co-author of the textbook Deep Learning, which covers the theory and practice of modern deep learning methods.
He is the chief AI scientist at Facebook, where he oversees the research and development of AI technologies across the company. He is also a professor at New York University (NYU), where he directs the NYU Center for Data Science and the NYU Center for Neural Science.
He is one of the pioneers of convolutional neural networks (CNNs), which are widely used for image recognition and understanding. He is also one of the founders of ONNX (Open Neural Network Exchange), which is an open standard for representing and exchanging neural network models.
He is the founder and CEO of fast.ai, a research lab that aims to make deep learning more accessible and practical for everyone. He is also a distinguished research scientist at the University of San Francisco (USF), where he teaches deep learning for coders.
He is a co-author of fastai (a library that simplifies training neural nets using modern best practices) and fastbook (a book that introduces deep learning using fastai). He is also an influential speaker and writer on topics such as natural language processing (NLP), computer vision, medical imaging, and ethical AI.
He is a professor at Carnegie Mellon University (CMU), where he directs the CMU Machine Learning Department. He is also an associate director of research at Apple’s Special Projects Group (SPG), where he works on various AI projects. He is an expert in deep learning, probabilistic graphical models, Bayesian inference, optimization, and large-scale learning. He has made significant contributions to fields such as computer vision, natural language processing, speech recognition, information retrieval, and recommendation systems.
He is a professor emeritus at the University of Toronto (U of T), where he holds the Canada Research Chair in Machine Learning. He is also a vice president and engineering fellow at Google, where he leads the Google Brain team in Toronto.
He is one of the founders of deep learning, and one of the main architects of backpropagation, which is a key algorithm for training neural networks. He is also known for his work on neural network models such as Boltzmann machines, restricted Boltzmann machines, deep belief networks, and capsules.
He is the director of machine learning and deep learning at Amazon Web Services (AWS), where he leads the development of AI products and services such as Amazon SageMaker, Amazon Comprehend, Amazon Lex, Amazon Polly, and Amazon Rekognition. He is also a professor at Australian National University (ANU), where he teaches machine learning.
He is a co-author of the textbook Introduction to Machine Learning, which covers the fundamentals and applications of machine learning methods. He is also an expert in kernel methods, graphical models, optimization, and large-scale learning.