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
Yoshua Bengio: The Past, Present, and Future of Deep Learning
2018 ACM A.M. Turing Award recipient discusses his career, collaborations, deep learning's promise, and directions for the field.
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
Teaching Robots to Help People in Their Everyday Lives
An interview with Max Braun of Everyday Robots.
From ACM Opinion
Connecting Large Language Models to Human Values
AI researcher Connor Leahy talks about replicating GPT-2/GPT-3, superhuman AI, AI alignment, AI risk and research norms, and more
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
Self-Supervised Learning and Large Language Models
Stanford PhD student discusses recent research on understanding, building, and controlling pre-trained models
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