The Gradient is a digital magazine covering research and trends in artificial intelligence and machine learning. We provide accessible and technically informed overviews of the what's going on AI, as well as a platform for perspectives on recent developments and long-term trends. In short, The Gradient points in the direction of the field.
We are a non-profit and volunteer-run effort run by researchers in the AI community. We were founded in 2017 by a group of students and researchers at the Stanford Artificial Intelligence Laboratory (SAIL).
- For more on our mission, see our Editor’s Note.
- Keep up with us on Twitter.
- Contact us at editor [at] thegradient.pub.
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Hugh Zhang (he/him) is a graduate student at Harvard EconCS and a cofounder of the Gradient. His recent research interests include generative models, AI policy, game theory, and multi-agent reinforcement learning. In his spare time, he enjoys writing, playing Go and eating burgers at In-N-Out. Follow him on Twitter.
Andrey Kurenkov (he/him) is a PhD student with the Stanford Vision and Learning Lab. His work primarily focuses on applying deep reinforcement learning for robotic manipulation, with several publications utilizing supervised learning and imitation learning as well. Besides being a cofounder of The Gradient, he also founded the publication Skynet Today, created the Last Week in AI newsletter, and is a co-host of the Let's Talk AI podcast.
Justin Landay (they/them) completed their undergraduate and masters degrees from George Washington University, publishing numerous papers on machine learning applications in nuclear physics. They are now a Senior Data Scientist at Riot Games, focusing on using deep learning to identify and mitigate harmful behavior in online communities.
Kiran Vaidhya is a Ph.D. candidate at the Diagnostic Image Analysis Group of Radboudumc, and is supervised by Bram van Ginneken, Mathias Prokop and Colin Jacobs. He works on early lung cancer detection from chest CT images using deep learning and specifically works on the development of deep learning algorithms for estimating the malignancy risk of pulmonary nodules in lung cancer screening.
Bradly Alicea has a PhD from Michigan State University. With interests centered upon computational science, developmental biology, and cognitive systems, he is currently Head Scientist and Founder of Orthogonal Research and a Senior Contributor at the OpenWorm Foundation. Bradly is also the manager of open-source community activities at Rokwire and administrator of Synthetic Daisies blog.
Jessica Dai is a Machine Learning Engineer at Arthur AI. So far, her research interests have involved fairness (and more recently, explainability) in machine learning; more broadly, she is interested in bridging the gap between research and practice in these areas. In addition to her work at The Gradient, she is also on the editorial team at Reboot; in her spare time, she enjoys live music, ballet, and fiction. Follow her on Twitter.
Huazhe Xu is a postdoctoral researcher at Stanford. His recent research interests include reinforcement learning, robotics and computer vision. In his spare time, he enjoys playing the piano, composing and playing tennis.