All the training data in the world can’t account for every possibility when AI systems interact with people and the environment.
Articles on AI training
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Boycotts, sabotage and other types of civil disobedience have long served collective action against injustice.
The age of AI is leading to barriers being put up across the open web. This could be the fix.
Low-cost Chinese-made AI could outcompete premium US models – and it won’t be an accident.
To overcome two challenges in training AI – scarce or hard-to-get data and data privacy – researchers have come up with a counterintuitive technique: fake it.
Even the most advanced AI tools are useless if employees don’t feel confident using them. Building trust and boosting workers’ belief in their abilities is the real key to successful adoption.
The ‘grey digital divide’ is artificial – created because designers of tech products are overwhelmingly young.
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.
AI systems reflect human values. However, the human values embedded in AI are skewed to the utilitarian and away from the greater good.
AI will feature in future Nobel prizes as scientists exploit the power of this technology for research.
Generative AI needs tons of data to learn. It also generates new data. So, what happens when AI starts training on AI-made content?
Human Rights Watch has sounded the alarm over Australian children’s images found in a huge data set used to train AI models. It could be a breach of our privacy law.
Many videos people upload to YouTube aren’t really meant for public consumption, but they’re available for AI companies to vacuum up. Many of these personal videos are posted by children.
With any AI system, we should match our expectations to its abilities – which have many potential limits.
AI needs careful monitoring and the right policies to ensure it can benefit the fight against climate change.
The effects of AI’s growth on global security could be difficult to predict.
When it comes to training high-performing AI models, the quality of the data is just as important as the quantity.
Top contributors
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Director of the Business Advice Centre for Post Graduate Students at UEL, Ambassador of the Centre for Innovation, Management and Enterprise, University of East London
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Professor of Management and Technology, University of East London
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Professor in Computational Intelligence, Australian Catholic University
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Associate Professor of Public Policy, Communication, and Information, UMass Amherst
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Associate Professor, Faculty of Law & Justice; Lead, UNSW Public Interest Law & Tech Initiative, UNSW
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Senior Research Fellow in AI Accountability, Queensland University of Technology
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Professor of Artificial Intelligence, University of Bath
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Ph.D. student in Computer and Information Technology, Purdue University
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Research Fellow, Deakin Institute for Citizenship and Globalisation, Deakin University
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Ph.D. Candidate in Information Science, Cornell University
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Ph.D. Student in Information Science, University of Washington
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Lecturer in Organisational Behaviour, Queen's Business School, Queen's University Belfast
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Adjunct Lecturer of English and Technical Communications, Missouri University of Science and Technology
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Professor of Statistics and Professor of EECS, University of Michigan
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Professor of Digital Communication, Director of QUT Digital Media Research Centre, Queensland University of Technology