Research asking AI models to describe and generate pictures reveals they see a bright, sensational world of generic images.
Articles on Large language models
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In stress-testing AI models, it’s not hard to push them to the brink and make them threaten to harm humans.
Threats to impose tariffs in retaliation for digital regulation are a line crossed.
Policy-makers must grapple with an uncomfortable truth: without deliberate action, AI will magnify global divides.
How do we make a complex piece of tech easy to understand? An analogy helps – as long as it’s not misleading.
AI models are not politically neutral nor free from bias. In fact, it may not even be possible for them to be unbiased.
Big tech wants generative AI systems to seem like neutral, reliable tools – but the reality is far more complicated.
A new study shows large language models find it much harder to understand the nuances of Indian, British and Australian English.
Language technologies are being adapted for health across Africa. But most of these tools never make it beyond the lab or they are limited in their language options.
‘Deep research’ AI agents combine large language models with sophisticated reasoning frameworks to conduct in-depth, multi-step analyses.
AI developers have many levers they can use to steer chatbots into certain behaviours.
What if instead of trying to detect and avoid AI glitches, we deliberately encouraged them instead?
Like calculators before them, AI tools can raise the bar for what people can achieve – if they’re used the right way.
The tools that are meant to help make AI safer could actually make it much more dangerous.
Neurosymbolic AI combines the learning of LLMs with teaching the machine formal rules that should make them more reliable and energy efficient.
On the internet, nobody knows you’re a chatbot.
AI models too often produce a monolithic version of English that erases variation.
It’s hard for ordinary people to distinguish good advice from decisively-voiced bad advice.
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.
Once errors creep into the AI knowledge base, they can be very hard to get out.
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