Measuring the actual effects of using AI systems is no easy task.
Articles on Computer science
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It takes a supercomputer weeks to output the properties of one stellar binary. A new study shows AI can do it in a fraction of the time.
There’s a lot that AI can’t do, but hype and misinformation are driving prospective students away from Computer Science.
Today, people use complex computing networks to search for prime numbers with millions of digits. But early mathematicians were running these calculations by hand.
New research uses firefly flashing patterns to identify species and what they’re communicating.
In this course, eight professors share their disciplinary perspectives to help students analyze proposed interventions for increasing wildfire risks.
The psychological study of the mind was crucial to the creation of AI – and will remain an essential part of the technology’s future.
Writing computer code is helpful for people in many disciplines, but learning to program is hard. Large language models can help nonprogrammers skip the difficult details.
Women in these fields are almost at parity with men at the most selective schools, but the gender gap doubled at many of the least selective universities.
OpenAI’s o3 model scored at human level on a benchmark test for artificial general intelligence – far higher than any results before.
AI systems can appear to be black boxes – often, even experts don’t know how systems reach their conclusions. The nascent field of “explainable AI” aims to address this problem.
AI systems more intelligent than humans in some ways already exist – but general-purpose superhuman intelligence is probably still a long way off.
Two researchers whose work has led to the AI revolution won the 2024 Nobel Prize in physics. A materials physicist explains statistical mechanics, the physics field behind their discoveries.
John Hopfield and Geoffrey Hinton, the 2024 Physics Nobel laureates, developed computer systems that can memorise and learn from patterns in data.
We can celebrate the vision and ambition of the Dartmouth Conference. But we must also learn from its mistakes.
AI systems mass-producing cheap research would be bad news for an already struggling scientific ecosystem.
Early expectations for generative AI are deflating – but realistic applications are beginning to emerge.
These are some of the most complex and important pieces of software ever written.
Ethics is often neglected in engineering education, two researchers write, despite mounting questions about how to responsibly design artificial intelligence programs.
Microsoft Copilot can summarise meetings and even formulate arguments. But as good as that sounds, we shouldn’t blindly trust its accuracy.
Top contributors
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Professor of Policy Impact, University of Leeds
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Postdoctoral Researcher in Computer Science, Queen Mary University of London
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Professor of Education (Innovation in Schooling & Educational Technology), University of Newcastle
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Educator, Researcher, Mentor, Kenya Methodist University
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Professor of Risk and Information Management, Queen Mary University of London
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A/Prof, UTS Chancellor's Postdoctoral Research and ARC DECRA Fellow, University of Technology Sydney
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Professor of Psychology, University of Washington
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Assistant Professor of Education, University of Houston
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Associate professor in Computer Science, University of Cape Town
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Pro Vice-Chancellor, Graduate & International Research, The University of Melbourne
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Professor in Computer Science and Statistics, Queen Mary University of London
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Professor of Computer Science and Director of the Art & AI Lab, Rutgers University
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Adjunct professor, Swinburne University of Technology
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Professor of Mathematical and Evolutionary Computer Science, University of Hertfordshire
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Senior Scientist at the Center for Scientific Studies in the Arts and Research Associate Professor of Materials Science and Engineering, Northwestern University