Large language model AIs can ingest long documents and answer questions about them, but a key question is how well they ‘understand’ individual sentences in the documents.
Articles on Large language models
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Once errors creep into the AI knowledge base, they can be very hard to get out.
Aligning AIs with people’s goals and values is tricky. A new technique quantifies how far off human and machine are from each other.
As a computer scientist, I would hope that human creativity is more than regurgitating what others have written.
Studio Ghibli’s founder Hayao Miyazaki has been critical of AI previously. Now, ChatGPT is generating images in his world-famous animation style.
To make sure search engines serve us well, it’s helpful to imagine these tools having different roles – whether it’s a “librarian” or a “teacher”.
When AI systems try to bridge gaps in their training data, the results can be wildly off the mark: fabrications and non sequiturs researchers call hallucinations.
Handing over the tasks once done by human developers comes with some major risks.
Nearly 20 years after it was launched, machine translation is still a long way from replacing translators.
The idea that AI ‘learns’ like humans do is one of many misconceptions about the technology.
Humans understand that ‘red ball’ makes sense but ‘ball red’ does not. Large language models? Not so much.
Having AI models say how confident they are in their answers could help minimize inaccurate responses. Just don’t be overconfident about their confidence scores.
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.
A machine learning expert breaks down where the money goes in building big AIs, and how DeepSeek found ways to do it far more cheaply.
More efficient AI models may make research easier – and raise questions about the value of investments in huge datacentres.
AI’s potential needs to be explored through experimentation. But this works best if managed under controlled environments.
AI safety has taken a back seat in the competition for ever more powerful large language models.
The rubber met the road for language AIs in 2024. The hard realities led to new, smaller models and safety measures for the big ones. 2024’s R&D also set the stage for the next big thing: AI agents.
Language models are likely to have an important impact on how we live and work in future, but they’ve also been around longer than many realise.
Borges imagined an endless library that contained every possible permutation of letters. The truth is out there, but it’s embedded among hordes of lies and gibberish.
Top contributors
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Director of Sydney Executive Plus, Business School, University of Sydney
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Professor of Information Technology and Organisation, University of Sydney
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Professor and Associate Director of the Centre for Change and Complexity in Learning (C3L), Education Futures, Adelaide University
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Associate Professor of Linguistics and Director of Language Lab, The University of Western Australia
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Professor of AI, Research Group Leader, UNSW
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Professor of Statistics and Professor of EECS, University of Michigan
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Research Assistant Professor of Industrial & Systems Engineering, University of Southern California
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Lecturer in Business Analytics, University of Sydney
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Senior Research Fellow in AI Accountability, Queensland University of Technology
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Associate Professor of Computer Science, Affiliate Professor of Information Science, University of Colorado Boulder
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Professor of Philosophy and Director of Applied Ethics Center, UMass Boston
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Doctoral Student in Cognitive Science, University of California, San Diego
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Emeritus Professor of Psychology, Arizona State University
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Associate Professor of Computer Science, Florida International University
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Assistant Professor, Oregon State University