Beyond Black Boxes: Meet AI that Justifies Its Choices

Unlock the secrets of AI innovation with our esteemed guest, Jem Davies, non-executive director at Literal Labs. Jem shares his transition from Arm to Literal Labs, revealing how the revolutionary Tsetlin machine sets new benchmarks in efficiency, power usage, and processing speed. 

Jem is a highly experienced business leader and technologist having previously served 18 years at Arm. He is an engineer and was an Arm Fellow holding multiple patents on CPU and GPU design. Latterly, Jem's career moved into business management and he became a General Manager first in Arm's Media Processing Groups, then the founding General Manager of their Machine Learning group. In addition to setting future technology roadmaps, he also worked on several acquisitions leading to building new businesses inside Arm, including the Mali GPU (the world's #1-shipping GPU) and Arm's AI processors. Jem left Arm in 2021 and currently is chair of NAG and a non-executive director of Literal Labs, BOW, CamAI, and Cambridge Future Tech.

Explore the crucial role of explainable AI and why it matters more than ever in today's regulated industries like healthcare and finance. Jem discusses Lietral Labs' Tsetlin Machine, which offers an intuitive audit trail of AI decision-making through propositional logic. This approach is breaking new ground by enhancing model efficiency without compromising on performance. We also tackle the challenge of unbiased training data and how tailored levels of explainability can make AI accessible to everyone, from everyday users to industry experts.

As we gaze into the future of AI, we tackle the pressing issues of bias, energy consumption, and the potential impact of quantum computing. Jem provides insight into how Literal Labs is pioneering tools to promote ethical AI development, mitigate biases, and democratize AI innovation. From practical applications like water leak monitoring to the potential for AI to evolve into a tool of unimaginable uses, we reflect on how the intersection of explainability, energy efficiency, and bias shapes a responsible AI future. Join us for an episode that promises to broaden your understanding of AI's profound societal impact.

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