London Futurists
Anticipating and managing exponential impact - hosts David Wood and Calum Chace
Calum Chace is a sought-after keynote speaker and best-selling writer on artificial intelligence. He focuses on the medium- and long-term impact of AI on all of us, our societies and our economies. He advises companies and governments on AI policy.
His non-fiction books on AI are Surviving AI, about superintelligence, and The Economic Singularity, about the future of jobs. Both are now in their third editions.
He also wrote Pandora's Brain and Pandora’s Oracle, a pair of techno-thrillers about the first superintelligence. He is a regular contributor to magazines, newspapers, and radio.
In the last decade, Calum has given over 150 talks in 20 countries on six continents. Videos of his talks, and lots of other materials are available at https://calumchace.com/.
He is co-founder of a think tank focused on the future of jobs, called the Economic Singularity Foundation. The Foundation has published Stories from 2045, a collection of short stories written by its members.
Before becoming a full-time writer and speaker, Calum had a 30-year career in journalism and in business, as a marketer, a strategy consultant and a CEO. He studied philosophy, politics, and economics at Oxford University, which confirmed his suspicion that science fiction is actually philosophy in fancy dress.
David Wood is Chair of London Futurists, and is the author or lead editor of twelve books about the future, including The Singularity Principles, Vital Foresight, The Abolition of Aging, Smartphones and Beyond, and Sustainable Superabundance.
He is also principal of the independent futurist consultancy and publisher Delta Wisdom, executive director of the Longevity Escape Velocity (LEV) Foundation, Foresight Advisor at SingularityNET, and a board director at the IEET (Institute for Ethics and Emerging Technologies). He regularly gives keynote talks around the world on how to prepare for radical disruption. See https://deltawisdom.com/.
As a pioneer of the mobile computing and smartphone industry, he co-founded Symbian in 1998. By 2012, software written by his teams had been included as the operating system on 500 million smartphones.
From 2010 to 2013, he was Technology Planning Lead (CTO) of Accenture Mobility, where he also co-led Accenture’s Mobility Health business initiative.
Has an MA in Mathematics from Cambridge, where he also undertook doctoral research in the Philosophy of Science, and a DSc from the University of Westminster.
London Futurists
Tsetlin Machines, Literal Labs, and the future of AI, with Noel Hurley
Our guest in this episode is Noel Hurley. Noel is a highly experienced technology strategist with a long career at the cutting edge of computing. He spent two decade-long stints at Arm, the semiconductor company whose processor designs power hundreds of billions of devices worldwide.
Today, he’s a co-founder of Literal Labs, where he’s developing Tsetlin Machines. Named after Michael Tsetlin, a Soviet mathematician, these are a kind of machine learning model that are energy-efficient, flexible, and surprisingly effective at solving complex problems - without the opacity or computational overhead of large neural networks.
AI has long had two main camps, or tribes. One camp works with neural networks, including Large Language Models. Neural networks are brilliant at pattern matching, and can be compared to human instinct, or fast thinking, to use Daniel Kahneman´s terminology. Neural nets have been dominant since the first Big Bang in AI in 2012, when Geoff Hinton and others demonstrated the foundations for deep learning.
For decades before the 2012 Big Bang, the predominant form of AI was symbolic AI, also known as Good Old Fashioned AI. This can be compared to logical reasoning, or slow learning in Kahneman´s terminology.
Tsetlin Machines have characteristics of both neural networks and symbolic AI. They are rule-based learning systems built from simple automata, not from neurons or weights. But their learning mechanism is statistical and adaptive, more like machine learning than traditional symbolic AI.
Selected follow-ups:
- Noel Hurley - Literal Labs
- A New Generation of Artificial Intelligence - Literal Labs
- Michael Tsetlin - Wikipedia
- Thinking, Fast and Slow - book by Daniel Kahneman
- 54x faster, 52x less energy - MLPerf Inference metrics
- Introducing the Model Context Protocol (MCP) - Anthropic
- Pioneering Safe, Efficient AI - Conscium
- Smartphones and Beyond - a personal history of Psion and Symbian
- The Official History of Arm - Arm
- Interview with Sir Robin Saxby - IT Archive
- How Spotify came to be worth billions - BBC
Music: Spike Protein, by Koi Discovery, available under CC0 1.0 Public Domain Declaration
