The Arts AI Forum gives the Faculty of Arts community an opportunity to exchange views, learn from others, and generate new ideas and projects regarding Generative AI.
Join the second Arts AI Forum which gives the Faculty of Arts community an opportunity to exchange views, learn from others, and generate new ideas and projects regarding Generative AI.
Wikipedia has long been a major reference resource for many. With more and more people utilizing AI summaries and large language models like ChatGPT, where does Wikipedia fit in? This talk will explore Wikipedia's current place in the digital landscape as well as its relationship with automation and LLMs.
In this talk, Handman argues that sycophancy is both a poor diagnosis of what chatbots do and also a pathologization of everyday interactional routines among humans.
Dr. Caroline Running Wolf draws on the historical introduction of the horse to Indigenous communities as a powerful metaphor for the future of artificial intelligence. This talk offers a compelling perspective on AI governance, cultural preservation, and the importance of community-led innovation.
Dr. Jennifer Boger challenges us to consider a population that AI often overlooks: older adults. Through practical examples and candid discussion, Jennifer will highlight why inclusive design is no longer optional but essential as AI becomes increasingly embedded in our daily lives.
How should we study something like "AI"—a subject of social and capital investment that rests more on speculative promises than given technologies or use cases? Drawing on political economic analyses of the tech sector, as well as a broader set of speculative fabulation practices by fellow data centre scholars, the talk will place AI in the past tense in order to assess both what it was and what it can become.
Experts discuss AI’s capabilities, limitations, security risks and safeguards, exploring responsible use through presentations, a panel and audience Q&A. Topics include AI alignment, technical protections, public policy, geopolitics and the boundaries needed for safe AI deployment.
Associate Research Officer Terry Stewart explores why brains are far more energy-efficient than AI systems, highlighting differences in how they process information. It examines how brain-inspired algorithms and neuromorphic hardware could make AI more efficient by mimicking how the brain represents time and space.