Podcast: The IDEMS Podcast

Continuing their examination of the assumptions underlying today’s dominant AI narrative, David and Kate explore the role of human expertise in building effective AI systems. They discuss the often-overlooked human work that underpins current AI, from reinforcement learning and quality assurance to research, teaching, and domain expertise. The conversation highlights how diverse forms of human…

Continuing their examination of the assumptions underlying today’s dominant AI narrative, David and Kate explore what makes data useful, trustworthy, and meaningful. They discuss the limitations of extraction-based approaches to AI, the importance of local context and data ownership, and the challenges of building systems that can learn across diverse communities without centralising control. The…

Continuing their examination of the assumptions underlying today’s dominant AI narrative, David and Kate explore the distinction between AI as a product and AI as a sociotechnical system. They reflect on the often-invisible infrastructure, labour, resources, and governance structures that sit behind AI technologies, and discuss why understanding these systems is essential for making informed…

Continuing their discussion on the future of AI, David and Kate explore the economic and institutional forces shaping today’s dominant AI models. They discuss the roles of investment, monopoly power, research funding, and commercial incentives in driving ever-larger AI systems, and consider how these pressures influence both technological development and public narratives around AI. The…

Continuing their discussion on the future of AI, David and Kate explore how advances in large language models could enable a new generation of smaller, more specialised AI systems. They discuss why the next wave of innovation may come from building tools that are more efficient, focused, and responsive to real-world needs rather than simply…

David and Kate explore the historical divide between Symbolist and Connectionist approaches to AI, reflecting on how today’s dominant AI narratives emerged and what may have been lost along the way. They discuss the difference between expert systems built on structured human knowledge and data-driven learning systems based on neural networks, and consider the implications…

In the second part of their discussion, David and Kate reflect more deeply on the Earthkeepers versus AI Empires convening in Zambia, exploring the diverse perspectives and tensions that emerged during the event. They discuss questions of power, governance, indigenous knowledge, and technological futures, as well as the growing recognition that current AI trajectories are…

In the first of a two-part discussion, David and Kate reflect on a recent convening in Zambia that brought together activists, technologists, researchers, and civil society groups concerned with the impacts of AI infrastructure and large-scale data centres. They discuss the influence of Karen Hao’s book Empire of AI, the emergence of global resistance movements…

Lily and David discuss the challenges of working with rainfall and climate data, exploring ideas of data quality, data rescue, and data accreditation. They reflect on different sources of climate data—from weather stations and satellites to reanalysis products—and examine how these can be evaluated for specific applications such as agriculture. The conversation also highlights ongoing…

Lucie and David continue their discussion on Farmer Research Networks (FRNs), focusing on the idea of embedded scaling and its implications. They explore how scaling out, scaling up, and scaling deep each change the nature of the data and the research itself, and reflect on the challenge of designing systems where farmers collect and use…