Communications of the ACM
From ACM Opinion
Dense Visual Representations, NeRFs, and LLMs for Robotics
Google research scientist Pete Florence discusses how robotics can benefit from dense visual representations, neural radiance fields, and large language models....
From ACM Opinion
Open-Endedness and Evolution through Large Models
A conversation with Joel Lehman, machine-learning scientist formerly of OpenAI and Uber AI Labs.
From ACM Opinion
Linguistics and the Development of NLP
An interview with Christopher Manning, director of the Stanford University AI Lab and an associate director of Stanford's Human-Centered Artificial Intelligence...
From ACM Opinion
Interpretable Machine Learning
A conversation with Been Kim, staff research scientist at Google Brain.
From ACM Opinion
Teaching Robots to Help People in Their Everyday Lives
An interview with Max Braun of Everyday Robots.
From ACM Opinion
The Future of AI in Games
Making artificial intelligence a tool of freedom and creativity for everyone.
From ACM Opinion
Machine-Learning Robustness, Foundation Models, and Reproducibility
An interview with Percy Liang, associate professor of Computer Science at Stanford University
From ACM Opinion
Robot Learning at Google and Generalization via Language
Google Robotics research scientist Eric Jang talks about robotic manipulation and self-supervised robotic learning
From ACM Opinion
Human-Centered Explainable AI and Social Transparency
An interview with doctoral candidate Upol Ehsan, who researched human-centered explainable AI
From ACM Opinion
China's AI Dream and the AI 'Arms Race'
Stanford postdoctoral fellow discusses research exploring impact of AI in a possible U.S.-China power transition
From ACM Opinion
RL Benchmarking, Climate Impacts of AI, and AI for Law
Stanford JD-PhD candidate Peter Henderson talks about creating robust decision-making systems and ML methods that benefit society
From ACM Opinion
Meta Learning and Model-Based Reinforcement Learning
Stanford professor Chelsea Finn talks about robotics and meta learning research
From ACM Opinion