ΑΙhub.org
 

Radical AI podcast: featuring Beth Singler


by
25 August 2020



share this:

Hosted by Dylan Doyle-Burke and Jessie J Smith, Radical AI is a podcast featuring the voices of the future in the field of artificial intelligence ethics. In this episode Jess and Dylan chat to Beth Singler about “Science fiction, science fact and AI consciousness”.

Science fiction, science fact and AI consciousness with Beth Singler

How can Science Fiction be used to get the public involved in the AI Ethics conversation? What are religious studies and how can they relate to AI? Why is it important to distinguish between Science Fiction and Science Fact when it comes to the future of AI? To answer these questions and more we welcome Dr Beth Singler to the show. Dr Beth Singler is a Junior Research Fellow in Artificial Intelligence at the University of Cambridge. Previously, Beth was the post-doctoral Research Associate on the “Human Identity in an age of Nearly-Human Machines” project at the Faraday Institute for Science and Religion. Through her research, Beth explores the social, ethical, philosophical, and religious implications of advances in Artificial Intelligence and robotics. Full show notes for this episode can be found at Radical AI.

Listen to the episode below:

About Radical AI:

Hosted by Dylan Doyle-Burke, a PhD student at the University of Denver, and Jessie J Smith, a PhD student at the University of Colorado Boulder, Radical AI is a podcast featuring the voices of the future in the field of Artificial Intelligence Ethics.

Radical AI lifts up people, ideas, and stories that represent the cutting edge in AI, philosophy, and machine learning. In a world where platforms far too often feature the status quo and the usual suspects, Radical AI is a breath of fresh air whose mission is “To create an engaging, professional, educational and accessible platform centering marginalized or otherwise radical voices in industry and the academy for dialogue, collaboration, and debate to co-create the field of Artificial Intelligence Ethics.”

Through interviews with rising stars and experts in the field we boldly engage with the topics that are transforming our world like bias, discrimination, identity, accessibility, privacy, and issues of morality.

To find more information regarding the project, including podcast episode transcripts and show notes, please visit Radical AI.




The Radical AI Podcast

            AUAI is supported by:



Subscribe to AIhub newsletter on substack



Related posts :

monthly digest

AIhub monthly digest: April 2026 – machine learning for particle physics, AI Index Report, and table tennis

  30 Apr 2026
Welcome to our monthly digest, where you can catch up with AI research, events and news from the month past.

The Machine Ethics podcast: organoid computing with Dr Ewelina Kurtys

In this episode, Ben chats to Ewelina about the uses of organoids and energy saving computing, differences between biological neurons and digital neural networks, and much more.

#AAAI2026 invited talk: Yolanda Gil on improving workflows with AI

  28 Apr 2026
Former AAAI president on using AI to help communities of scientists better streamline their research.

Maryna Viazovska’s proofs of sphere packing formalized with AI

  27 Apr 2026
Formalization achieved through a collaboration between mathematicians and artificial intelligence tools.

Interview with Deepika Vemuri: interpretability and concept-based learning

  24 Apr 2026
Find out more about Deepika's research bridging the gap between data-driven models and symbolic learning.

As a ‘book scientist’ I work with microscopes, imaging technologies and AI to preserve ancient texts

  23 Apr 2026
Using an array of technologies to recover, understand and preserve many valuable ancient texts.

Sony AI table tennis robot outplays elite human players

  22 Apr 2026
New robot and AI system has beaten professional and elite table tennis players.

Causal models for decision systems: an interview with Matteo Ceriscioli

  21 Apr 2026
How can we integrate causal knowledge into agents or decision systems to make them more reliable?



AUAI is supported by:







Subscribe to AIhub newsletter on substack




 















©2026.02 - Association for the Understanding of Artificial Intelligence