From Communications of the ACM
Digital innovation is not working in the interest of the whole of society. It is time to radically rethink its purpose without…
Filippo Gualtiero Blancato| March 1, 2024
AI systems without common sense will make mistakes when they reach the limits of where they've been trained.
TechTalks From ACM Opinion | August 9, 2022
Neuroscientist and author Daeyeol Lee talks reinforcement learning in humans and animals, AI and natural intelligence, and more.
TechTalks From ACM Opinion | June 21, 2022
Technologies such as AlphaCode cannot think about and design their own problems, but they are very good problem solvers
TechTalks From ACM Opinion | February 15, 2022
Human programmers are in control but they must learn to harness the power and limits of AI-generated code
TechTalks From ACM Opinion | February 8, 2022
Can large language models teach us about the nature of language, understanding, intelligence, sociality, and personhood?
TechTalks From ACM Opinion | December 27, 2021
While helpful in comparing AI performance, benchmarks are often taken out of context, sometimes to harmful results
TechTalks From ACM Opinion | December 7, 2021
While larger deep neural networks can incrementally improveme specific tasks, they aren't fit for general natural language understanding.
TechTalks From ACM Opinion | July 12, 2021
AI-powered code generator provides some interesting hints about the business of large language models and the future of the software industry.
TechTalks From ACM Opinion | July 8, 2021
Harvard Medical University Professor Gabriel Kreiman provides an account of how humans and animals process visual data and how far techno come toward replicating...TechTalks From ACM Opinion | May 10, 2021