Discover low-code and pro-code tools that help you build agents specialized for healthcare. Streamline workflows, surface timely insights, and advance patient care with agentic AI powered by unified data and built-in security.

To explore how the University of Oxford leverages a unified approach to high-performance computing infrastructure and scalable data platforms across the Big Data Institute and the Centre for Human Genetics to advance biomedical research across the entire University.

This session will discuss:

• Breakthroughs enabled by HPC and secure data platforms in health research
• Infrastructure needs for biomedical innovation and large-scale data science
• Oxford’s partnership journey with Dell Technologies and NVIDIA and its real-world impact
• How scalable AI infrastructure is accelerating research outcomes

Careless speech is a new type of harm created by large language models (LLM) that poses cumulative, long-term risks to science, education, and the development of shared social truths in democratic societies. LLMs produce responses that are plausible, helpful, and confident but that contain factual inaccuracies, inaccurate summaries, misleading references, and biased information. These subtle mistruths are poised to cause a severe cumulative degradation and homogenisation of knowledge over time.

This talk examines the existence and feasibility of a legal duty for LLM providers to create models that “tell the truth.” LLM providers should be required to mitigate careless speech and better align their models with truth through open, democratic processes. Careless speech is defined and contrasted with the simplified concept of “ground truth” in LLMs and prior discussion of related truth-related risks in LLMs including hallucinations, misinformation, and disinformation. EU human rights law and liability frameworks contain some truth-related obligations for products and platforms, but they are relatively limited in scope and sectoral reach.

The talk concludes by proposing a pathway to create a legal truth duty applicable to providers of both narrow- and general-purpose LLMs, and discusses “zero-shot translation” as a prompting method to constrain LLMs and better align their outputs with verified, truthful information.