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Post-labour economics and the future of capitalism, with Ted Shelton
09 May 2026
00:45:03
This episode continues our investigation into the potential wide-ranging implications of advanced AI for economics.
Traditionally, value is said to be created by a combination of capital, which covers the cost of materials and equipment, and labour, whereby humans exercise skills, ingenuity, diligence, attention, and more. What has been a constant debate is the appropriate division of rewards between capital and labour. Critics of the operation of capitalism have often predicted that an accumulation of value within small groups of owners of capital will cause economic instabilities and a subsequent collapse. Despite these forecasts, capitalism has, so far, demonstrated great resilience, defying predictions of its collapse. But if human labour is increasingly displaced by advanced automation, the balance of labour and capital will be fundamentally changed, and capitalism will come under unprecedented pressures.
That’s the thesis of our guest today, Ted Shelton. David first met Ted about 25 years ago, when Ted was Chief Strategy Officer of the software development tools company Borland, and David was an executive within the early smartphone industry. Since that time, Ted has worked for a variety of companies in and around Silicon Valley, including PwC, Cognizant Technology Solutions, Catalytic, Bain, and Inflection AI. Recently, he has been giving a great deal of thought to where AI is taking the economy.
Windfall Trust and the Economic Singularity, with Adrian Brown
22 Apr 2026
00:45:36
What happens if AI delivers major advances in capability and productivity, but also creates significant disruption to jobs, incomes, and public finances? That question sits at the heart of today’s episode.
Our guest is Adrian Brown, the Founder and Chief Executive of Windfall Trust, a nonprofit focused on helping governments and societies prepare for the economic consequences of advanced AI. Windfall describes itself not as a think tank, but as a “policy accelerator for the age of artificial intelligence”.
Their work starts from a simple premise: if AI systems significantly reshape the economy, then the question is not only how we build them, but how we prepare for their impacts, and how the gains are ultimately shared.
Before founding Windfall Trust, Adrian was the founding Executive Director of the Centre for Public Impact, worked as a policy advisor in the UK Cabinet Office, and held roles at McKinsey and the Boston Consulting Group.
Intellectual dark matter? A reputation trap? The case of cold fusion, with Jonah Messinger
05 Aug 2025
00:40:49
Could the future see the emergence and adoption of a new field of engineering called nucleonics, in which the energy of nuclear fusion is accessed at relatively low temperatures, producing abundant clean safe energy? This kind of idea has been discussed since 1989, when the claims of cold fusion first received media attention. It is often assumed that the field quickly reached a dead-end, and that the only scientists who continue to study it are cranks. However, as we’ll hear in this episode, there may be good reasons to keep an open mind about a number of anomalous but promising results.
Our guest is Jonah Messinger, who is a Winton Scholar and Ph.D. student at the Cavendish Laboratory of Physics at the University of Cambridge. Jonah is also a Research Affiliate at MIT, a Senior Energy Analyst at the Breakthrough Institute, and previously he was a Visiting Scientist and ThinkSwiss Scholar at ETH Zürich. His work has appeared in research journals, on the John Oliver show, and in publications of Columbia University. He earned his Master’s in Energy and Bachelor’s in Physics from the University of Illinois at Urbana-Champaign, where he was named to its Senior 100 Honorary.
ChatGPT raises old and new concerns about AI, with Francesca Rossi
08 Mar 2023
00:35:52
Our guest in this episode is Francesca Rossi. Francesca studied computer science at the University of Pisa in Italy, where she became a professor, before spending 20 years at the University of Padova. In 2015 she joined IBM's T.J. Watson Research Lab in New York, where she is now an IBM Fellow and also IBM's AI Ethics Global Leader.
Francesca is a member of numerous international bodies concerned with the beneficial use of AI, including being a board member at the Partnership on AI, a Steering Committee member and designated expert at the Global Partnership on AI, a member of the scientific advisory board of the Future of Life Institute, and Chair of the international conference on Artificial Intelligence, Ethics, and Society which is being held in Montreal in August this year.
From 2022 until 2024 she holds the prestigious role of the President of the AAAI, that is, the Association for the Advancement of Artificial Intelligence. The AAAI has recently held its annual conference, and in this episode, Francesca shares some reflections on what happened there.
*) How a one-year sabbatical at the Harvard Radcliffe Institute changed the trajectory of Francesca's life *) New generative AI systems such as ChatGPT expand previous issues involving bias, privacy, copyright, and content moderation - because they are trained on very large data sets that have not been curated *) Large language models (LLMs) have been optimised, not for "factuality", but for creating language that is syntactically correct *) Compared to previous AIs, the new systems impact a wider range of occupations, and they also have major implications for education *) Are the "AI ethics" and "responsible AI" approaches that address the issues of existing AI systems also the best approaches for the "AI alignment" and "AI safety" issues raised by artificial general intelligence? *) Different ideas on how future LLMs could acquire mastery, not only over language, but also over logic, inference, and reasoning *) Options for combining classical AI techniques focussing on knowledge and reasoning, with the data-intensive approaches of LLMs *) How "foundation models" allow training to be split into two phases, with a shorter supervised phase customising the output from a prior longer unsupervised phase *) Even experts face the temptation to anthropomorphise the behaviour of LLMs *) On the other hand, unexpected capabilities have emerged within LLMs *) The interplay of "thinking fast" and "thinking slow" - adapting, for the context of AI, insights from Daniel Kahneman about human intelligence *) Cross-fertilisation of ideas from different communities at the recent AAAI conference *) An extension of that "bridge" theme to involve ideas from outside of AI itself, including the use of methods of physics to observe and interpret LLMs from the outside *) Prospects for interpretability, explainability, and transparency of AI - and implications for trust and cooperation between humans and AIs *) The roles played by different international bodies, such as PAI and GPAI *) Pros and cons of including China in the initial phase of GPAI *) Designing regulations to be future-proof, with parts that can change quickly *) A
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ChatGPT has woken up the House of Commons, with Tim Clement-Jones
01 Mar 2023
00:37:12
In this episode, Tim Clement-Jones brings us up to date on the reactions by members of the UK's House of Commons to recent advances in the capabilities of AI systems, such as ChatGPT. He also looks ahead to larger changes, in the UK and elsewhere.
Lord Clement-Jones CBE, or Tim, as he prefers to be known, has been a very successful lawyer, holding senior positions at ITV and Kingfisher among others, and later becoming London Managing Partner of law firm DLA Piper.
He is better known as a politician. He became a life peer in 1998, and has been the Liberal Democrats’ spokesman on a wide range of issues. The reason we are delighted to have him as a guest on the podcast is that he was the chair of the AI Select Committee, Co-Chair of the All-Party Parliamentary Group on AI, and is now a member of a special inquiry on the use of AI in Weapons Systems.
Tim also has multiple connections with universities and charities in the UK.
*) Does "the Westminster bubble" understand the importance of AI? *) Evidence that "the tide is turning" - MPs are demonstrating a spirit of inquiry *) The example of Sir Peter Bottomley, the Father of the House (who has been an MP continuously since 1975) *) New AI systems are showing characteristics that had not been expected to arrive for another 5 or 10 years, taking even AI experts by surprise *) The AI duopoly (the US and China) and the possible influence of the UK and the EU *) The forthcoming EU AI Act and the risk-based approach it embodies *) The importance of regulatory systems being innovation-friendly *) How might the EU support the development of some European AI tech giants? *) The inevitability(?) of the UK needing to become "a rule taker" *) Cynical and uncynical explanations for why major tech companies support EU AI regulation *) The example of AI-powered facial recognition: benefits and risks *) Is Brexit helping or hindering the UK's AI activities? *) Complications with the funding of AI research in the UK's universities *) The risks of a slow-down in the UK's AI start-up ecosystem *) Looking further afield: AI ambitions in the UAE and Saudi Arabia *) The particular risks of lethal autonomous weapons systems *) Future conflicts between AI-controlled tanks and human-controlled tanks *) Forecasts for the arrival of artificial general intelligence: 10-15 years from now? *) Superintelligence may emerge from a combination of separate AI systems *) The case for "technology-neutral" regulation
Music: Spike Protein, by Koi Discovery, available under CC0 1.0 Public Domain Declaration
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Advanced AI is currently pretty much a duopoly between the USA and China. The US is the clear leader, thanks largely to its tech giants – Google, Meta, Microsoft, Amazon, and Apple. China also has a fistful of tech giants – Baidu, Alibaba, and Tencent are the ones usually listed, but the Chinese government has also taken a strong interest in AI since Deep Mind’s Alpha Go system beat the world’s best Go player in 2016.
People in the West don’t know enough about China’s current and future role in AI. Some think its companies just copy their Western counterparts, while others think it is an implacable and increasingly dangerous enemy, run by a dictator who cares nothing for his people. Both those views are wrong.
One person who has been trying to provide a more accurate picture of China and AI in recent years is Jeff Ding, the author of the influential newsletter ChinAI.
Jeff grew up in Iowa City and is now an Assistant Professor of Political Science at George Washington University. He earned a PhD at Oxford University, where he was a Rhodes Scholar, and wrote his thesis on how past technological revolutions influenced the rise and fall of great powers, with implications for U.S.-China competition. After gaining his doctorate he worked at Oxford’s Future of Humanity Institute and Stanford’s Institute for Human-Centered Artificial Intelligence.
*) The Thucydides Trap: Is conflict inevitable as a rising geopolitical power approaches parity with an established power? *) Different ways of trying to assess how China's AI industry compares with that of the U.S. *) Measuring innovations in creating AI is different from measuring adoption of AI solutions across multiple industries *) Comparisons of papers submitted to AI conferences such as NeurIPS, citations, patents granted, and the number of data scientists *) The biggest misconceptions westerners have about China and AI *) A way in which Europe could still be an important player alongside the duopoly *) Attitudes in China toward data privacy and facial recognition *) Government focus on AI can be counterproductive *) Varieties of government industrial policy: the merits of encouraging decentralised innovation *) The Titanic and the origin of Silicon Valley *) Mariana Mazzucato's question: "Who created the iPhone?" *) Learning from the failure of Japan's 5th Generation Computers initiative *) The evolution of China's Social Credit systems *) Research by Shazeda Ahmed and Jeremy Daum *) Factors encouraging and discouraging the "splinternet" separation of US and Chinese tech ecosystems *) Connections that typically happen outside of the public eye *) Financial interdependencies *) Changing Chinese government attitudes toward Chinese Internet giants *) A broader tension faced by the Chinese government *) Future scenarios: potential good and bad developments *) Transnational projects to prevent accidents or unauthorised use of powerful AI systems
Music: Spike Protein, by Koi Discovery, available under CC0 1.0 Public Domain Declaration
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Peter James, best-selling crime-writer and transhumanist
15 Feb 2023
00:33:01
Peter James is one of the world’s most successful crime writers. His "Roy Grace" series, about a detective in Brighton, England, near where Peter lives, has produced a remarkable 19 consecutive Sunday Times Number One bestsellers. His legions of devoted fans await each new release eagerly. The books have been televised, with the third series of "Grace", starting John Simm, being commissioned for next year.
Peter has worked in other genres too, having written 36 novels altogether. When Calum first met Peter in the mid-1990s, Peter's science fiction novel “Host” was generating rave reviews. It was the world’s first electronically published novel, and a copy of its floppy disc version is on display in London’s Science Museum.
Peter is also a self-confessed petrol-head, with an enviable collection of classic cars, and a pretty successful track record of racing some of them. The discussion later in the episode addresses the likely arrival of self-driving cars. But we start with the possibility of mind uploading, which is the subject of “Host”.
*) Peter's passion for the future *) The transformative effect of the 1990 book "Great Mambo Chicken and the Transhuman Condition" *) A Christmas sojourn at MIT and encounters with AI pioneer Marvin Minsky *) The origins of the ideas behind "Host" *) Meeting Alcor, the cryonics organisation, in Riverside California *) How cryonics has evolved over the decades *) "The first person to live to 200 has already been born" *) Quick summaries of previous London Futurists Podcast episodes featuring Aubrey de Grey and Andrew Steele *) The case for doing better than nature *) Peter's novel "Perfect People" and the theme of "designer babies" *) Possible improvements in the human condition from genetic editing *) The risk of a future "genetic underclass" *) Technology divides often don't last: consider the "fridge divide" and the "smartphone divide" *) Calum's novel "Pandora's Brain" *) Why Peter is comfortable with the label "transhumanist" *) Various ways of reading (many) more books *) A thought experiment involving a healthy 99 year old *) If people lived a lot longer, we might take better care of our planet *) Peter's views on technology assisting writers *) Strengths and weaknesses of present-day ChatGPT as a writer *) Prospects for transhumans to explore space *) The "bunker experiments" into the circadian cycle, which suggest that humans naturally revert to a daily cycle closer to 26 hours than 24 hours *) Possible answers to Fermi's question about lack of any sign of alien civilisations *) Reflections on "The Pale Blue Dot of Earth" (originally by Carl Sagan) *) The likelihood of incredible surprises in the next few decades *) Pros and cons of humans driving on public roads (especially when drivers are using mobile phones) *) Legal and ethical issues arising from autonomous cars *) Exponential change often involves a frustrating slow phase before fast breakthroughs *) Anticipating the experience of driving inside immersive virtual reality *) The tragic background to Peter's book "Possession" *) A concluding message from the science fiction writer Kurt Vonnegut
Music: Spike Protein, by Koi Discovery, available under CC0 1.0 Public Domain Declaration
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Our guest in this episode is a Briton who is based in Berlin, namely Andrew Steele. Earlier in his life Andrew spent nine years at the University of Oxford where, among other accomplishments, he gained a PhD in physics. His focus switched to computational biology, and he held positions at Cancer Research UK and the Francis Crick Institute.
Along the way, Andrew decided that aging was the single most important scientific challenge of our time. This led him to write the book "Ageless: The New Science of Getting Older Without Getting Old". There are a lot of books these days about the science of slowing, stopping, and even reversing aging, but Andrew's book is perhaps the best general scientific introduction to this whole field.
Topics in this conversation include: *) The background that led Andrew to write his book "Ageless" *) A graph that changed a career *) The chance of someone dying in the next year doubles every eight years they live *) For tens of thousand of years, human life expectancy didn't change *) In recent centuries, the background mortality rate has significantly decreased, but the eight year "Gompertz curve" doubling of mortality remains unchanged *) Some animals do not have this mortality doubling characteristic; they are said to be "negligibly senescent", "biologically immortal", or "ageless" *) An example: Galapagos tortoises *) The concept of "hallmarks of aging" - and different lists of these hallmarks *) Theories of aging: wear-and-tear vs. programmed obsolescence *) Evolution and aging: two different strategies that species can adopt *) Wear-and-tear of teeth - as seen from a programmed aging point-of-view *) The case for a pragmatic approach *) Dietary restriction and healthier aging *) The potential of computational biology system models to generate better understanding of linkages between different hallmarks of aging *) Might some hallmarks, for example telomere shortening or epigenetic damage, prove more fundamental than others? *) Special challenges posed by damage in the proteins in the scaffolding between cells *) What's required to accelerate the advent of "longevity escape velocity" *) Excitement and questions over the funding available to Altos Labs *) Measuring timescales in research dollars rather than years *) Reasons for optimism for treatments of some of the hallmarks, for example with senolytics, but others aren't being properly addressed *) Breakthrough progress with the remaining hallmarks could be achieved with $5-10B investment each *) Adding some extra for potential unforeseen hallmarks, that sums to a total of around $100B before therapies for all aspects of aging could be in major clinical trials *) Why such an expenditure is in principle relatively easily affordable *) Reflections on moral and ethical objections to treatments against aging *) Overpopulation, environmental strains, resource sustainability, and net zero impact *) Aging as the single largest cause of death in the world - in all countries *) Andrew's current and forthcoming projects, including a book on options for funding science with the biggest impact *) Looking forward to "being more tortoise".
Music: Spike Protein, by Koi Discovery, available under CC0 1.0 Public Domain Declaration
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It is nearly 40 years since our guest in this episode, pioneering transhumanist Natasha Vita-More, created the first version of the Transhumanist Manifesto. Since that time, Natasha has established numerous core perspectives, values, and actions in the global transhumanist family.
Natasha joins us in this episode to share her observations on how transhumanism has evolved over the decades, and to reflect on her work in building the movement—from practice-based approaches, scientific contributions, and theoretical innovations.
Areas we explore include: How has Natasha's work seeded the global growth of transhumanism? What are the main advances over the years that she particularly values? And what are the disappointments?
We also look to the future: What are her hopes and expectations for the next ten years of transhumanism?
Music: Spike Protein, by Koi Discovery, available under CC0 1.0 Public Domain Declaration
Selected follow-up reading:
https://natashavita-more.com/ https://www.fightaging.org/archives/2004/02/vital-progress-summit/ http://www.extropy.org/proactionaryprinciple.htm https://metanexus.net/transhumanism-and-its-critics/ https://whatistranshumanism.org/ https://www.alcor.org/library/persistence-of-long-term-memory-in-vitrified-and-revived-simple-animals/ https://waitbutwhy.com/2016/03/cryonics.html F. M. Esfandiary: https://archives.nypl.org/mss/4846 https://www.maxmore.com/ The World’s Most Dangerous Idea? https://nickbostrom.com/papers/dangerous https://theconversation.com/the-end-of-history-francis-fukuyamas-controversial-idea-explained-193225 https://www.humanityplus.org/ https://transhumanist-studies.teachable.com/ Anyone Can Code, Ethiopia: https://icogacc.com/ https://afrolongevity.taffds.org/
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Our guest in this episode is the scientist and science fiction author Davin Brin, whose writings have won the Hugo, Locus, Campbell, and Nebula Awards. His style is sometimes called 'hard science fiction'. This means his narratives feature scientific or technological change that is plausible rather than purely magical. The scenarios he creates are thought-provoking as well as entertaining. His writing inspires readers but also challenges them, with important questions not just about the future, but also about the present.
Perhaps his most famous non-fiction work is his book "The Transparent Society: Will Technology Force Us to Choose Between Privacy and Freedom?", first published in 1998. With each passing year it seems that the questions and solutions raised in that book are becoming ever more pressing. One aspect of this has been called Brin's Corollary to Moore's Law: Every year, the cameras will get smaller, cheaper, more numerous and more mobile.
David also frequently writes online about topics such as space exploration, attempts to contact aliens, homeland security, the influence of science fiction on society and culture, the future of democracy, and much more besides.
Topics discussed in this conversation include:
*) Reactions to reports of flying saucers *) Why photographs of UFOs remain blurry *) Similarities between reports of UFOs and, in prior times, reports of elves *) Replicating UFO phenomena with cat lasers *) Changes in attitudes by senior members of the US military *) Appraisals of the Mars Rovers *) Pros and cons of additional human visits to the moon *) Why alien probes might be monitoring this solar system from the asteroid belt *) Investigations of "moonlets" in Earth orbit *) Looking for pi in the sky *) Reasons why life might be widespread in the galaxy - but why life intelligent enough to launch spacecraft may be rare *) Varieties of animal intelligence: How special are humans? *) Humans vs. Neanderthals: rounds one and two *) The challenges of writing about a world that includes superintelligence *) Kurzweil-style hybridisation and Mormon theology *) Who should we admire most: lone heroes or citizens? *) Benefits of reciprocal accountability and mutual monitoring (sousveillance) *) Human nature: Delusions, charlatans, and incantations *) The great catechism of science *) Two levels at which the ideas of a transparent society can operate *) "Asimov's Laws of Robotics won't work" *) How AIs might be kept in check by other AIs *) The importance of presenting gedanken experiments
Fiction mentioned (written by David Brin unless noted otherwise): The Three-Body Problem (Liu Cixin) Existence The Sentinel (Arthur C. Clarke) Startide Rising The Uplift War Kiln People The Culture Series (Iain M. Banks) The Expanse (James S.A. Corey) The Postman (the book and the film) Stones of Significance Fahrenheit 451 (Ray Bradbury)
Music: Spike Protein, by Koi Discovery, available under CC0 1.0 Public Domain Declaration
Inventing the future of computing, with Alessandro Curioni
18 Jan 2023
00:35:38
OpenAI's ChatGPT and picture generating AI systems like MidJourney and Stable Diffusion have got a lot more people interested in advanced AI and talking about it. Which is a good thing. It will not be pretty if the transformative changes that will happen in the next two or three decades take most of us by surprise.
A company that has been pioneering advanced AI for longer than most is IBM, and we are very fortunate to have with us in this episode one of IBM’s most senior executives.
Alessandro Curioni has been with the company for 25 years. He is an IBM Fellow, Director of IBM Research, and Vice President for Europe and Africa.
Topics discussed in this conversation include:
*) Some background: 70 years of inventing the future of computing *) The role of grand challenges to test and advance the world of AI *) Two major changes in AI: from rules-based to trained, and from training using annotated data to self-supervised training using non-annotated data *) Factors which have allowed self-supervised training to build large useful models, as opposed to an unstable cascade of mistaken assumptions *) Foundation models that extend beyond text to other types of structured data, including software code, the reactions of organic chemistry, and data streams generated from industrial processes *) Moving from relatively shallow general foundation models to models that can hold deep knowledge about particular subjects *) Identification and removal of bias in foundation models *) Two methods to create models tailored to the needs of particular enterprises *) The modification by RLHF (Reinforcement Learning from Human Feedback) of models created by self-supervised learning *) Examples of new business opportunities enabled by foundation models *) Three "neuromorphic" methods to significantly improve the energy efficiency of AI systems: chips with varying precision, memory and computation co-located, and spiking neural networks *) The vulnerability of existing confidential data to being decrypted in the relatively near future *) The development and adoption of quantum-safe encryption algorithms *) What a recent "quantum apocalypse" paper highlights as potential future developments *) Changing forecasts of the capabilities of quantum computing *) IBM's attitude toward Artificial General Intelligence and the Turing Test *) IBM's overall goals with AI, and the selection of future "IBM Grand Challenges" in support of these goals *) Augmenting the capabilities of scientists to accelerate breakthrough scientific discoveries.
Music: Spike Protein, by Koi Discovery, available under CC0 1.0 Public Domain Declaration
Quantum computing is a tough subject to explain and discuss. As Niels Bohr put it, “Anyone who is not shocked by quantum theory has not understood it”. Richard Feynman helpfully added, “I think I can safely say that nobody understands quantum mechanics”.
Quantum computing employs the weird properties of quantum mechanics like superposition and entanglement. Classical computing uses binary digits, or bits, which are either on or off. Quantum computing uses qubits, which can be both on and off at the same time, and this characteristic somehow makes them enormously more computationally powerful.
Co-hosts Calum and David knew that to address this important but difficult subject, we needed an absolute expert, who was capable of explaining it in lay terms. When Calum heard Dr Ignacio Cirac give a talk on the subject in Madrid last month, he knew we had found our man.
Ignacio is director of the Max Planck Institute of Quantum Optics in Germany, and holds honorary and visiting professorships pretty much everywhere that serious work is done on quantum physics. He has done seminal work on the trapped ion approach to quantum computing and several other aspects of the field, and has published almost 500 papers in prestigious journals. He is spoken of as a possible Nobel Prize winner.
Topics discussed in this conversation include:
*) A brief history of quantum computing (QC) from the 1990s to the present *) The kinds of computation where QC can out-perform classical computers *) Likely timescales for further progress in the field *) Potential quantum analogies of Moore's Law *) Physical qubits contrasted with logical qubits *) Reasons why errors often arise with qubits - and approaches to reducing these errors *) Different approaches to the hardware platforms of QC - and which are most likely to prove successful *) Ways in which academia can compete with (and complement) large technology companies *) The significance of "quantum supremacy" or "quantum advantage": what has been achieved already, and what might be achieved in the future *) The risks of a forthcoming "quantum computing winter", similar to the AI winters in which funding was reduced *) Other comparisons and connections between AI and QC *) The case for keeping an open mind, and for supporting diverse approaches, regarding QC platforms *) Assessing the threats posed by Shor's algorithm and fault-tolerant QC *) Why companies should already be considering changing the encryption systems that are intended to keep their data secure *) Advice on how companies can build and manage in-house "quantum teams"
Music: Spike Protein, by Koi Discovery, available under CC0 1.0 Public Domain Declaration
Questioning the Fermi Paradox, with Anders Sandberg
04 Jan 2023
00:36:55
In the summer of 1950, the physicist Enrico Fermi and some colleagues at the Los Alamos Lab in New Mexico were walking to lunch, and casually discussing flying saucers, when Fermi blurted out “But where is everybody?” He was not the first to pose the question, and the precise phrasing is disputed, but the mystery he was referring to remains compelling.
We appear to live in a vast universe, with billions of galaxies, each with billions of stars, mostly surrounded by planets, including many like the Earth. The universe appears to be 13.7 billion years old, and even if intelligent life requires an Earth-like planet, and even if it can only travel and communicate at the speed of light, we ought to see lots of evidence of intelligent life. But we don’t. No beams of light from stars occluded by artificial satellites spelling out pi. No signs of galactic-scale engineering. No clear evidence of little green men demanding to meet our leaders.
Numerous explanations have been advanced to explain this discrepancy, and one man who has spent more brainpower than most exploring them is the always-fascinating Anders Sandberg. Anders is a computational neuroscientist who got waylaid by philosophy, which he pursues at Oxford University, where he is a senior research fellow.
Topics in this episode include: * The Drake equation for estimating the number of active, communicative extraterrestrial civilizations in our galaxy * Changes in recent decades in estimates of some of the factors in the Drake equation * The amount of time it would take self-replicating space probes to spread across the galaxy * The Dark Forest hypothesis - that all extraterrestrial civilizations are deliberately quiet, out of fear * The likelihood of extraterrestrial civilizations emitting observable signs of their existence, even if they try to suppress them * The implausibility of all extraterrestrial civilizations converging to the same set of practices, rather than at least some acting in ways where we would notice their existence - and a counter argument * The possibility of civilisations opting to spend all their time inside virtual reality computers located in deep interstellar space * The Aestivation hypothesis, in which extraterrestrial civilizations put themselves into a "pause" mode until the background temperature of the universe has become much lower * The Quarantine or Zoo hypothesis, in which extraterrestrial civilizations are deliberately shielding their existence from an immature civilization like ours * The Great Filter hypothesis, in which life on other planets has a high probability, either of failing to progress to the level of space-travel, or of failing to exist for long after attaining the ability to self-destruct * Possible examples of "great filters" * Should we hope to find signs of life on Mars? * The Simulation hypothesis, in which the universe is itself a kind of video game, created by simulators, who had no need (or lacked sufficient resources) to create more than one intelligent civilization * Implications of this discussion for the wisdom of the METI project - Messaging to Extraterrestrial Intelligence
Selected follow-up reading: * Anders' website at FHI Oxford: https://www.fhi.ox.ac.uk/team/anders-sandberg/ * The Great Filter, by Robin Hanson: http://mason.gmu.edu/~rhanson/greatfilter.html * "Seventy-Five Solutions to the Fermi Paradox and the Problem of Extraterrestrial Life" - a book by Stephen Webb: https://link.spring
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AI agents, AI safety, and AI boycotts, with Peter Scott
29 Jul 2025
00:54:17
This episode of London Futurists Podcast is a special joint production with the AI and You podcast which is hosted by Peter Scott. It features a three-way discussion, between Peter, Calum, and David, on the future of AI, with particular focus on AI agents, AI safety, and AI boycotts.
Peter Scott is a futurist, speaker, and technology expert helping people master technological disruption. After receiving a Master’s degree in Computer Science from Cambridge University, he went to California to work for NASA’s Jet Propulsion Laboratory. His weekly podcast, “Artificial Intelligence and You” tackles three questions: What is AI? Why will it affect you? How do you and your business survive and thrive through the AI Revolution?
Peter’s second book, also called “Artificial Intelligence and You,” was released in 2022. Peter works with schools to help them pivot their governance frameworks, curricula, and teaching methods to adapt to and leverage AI.
An area of technology that has long been anticipated is Extended Reality (XR), which includes Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR). For many decades, researchers have developed various experimental headsets, glasses, gloves, and even immersive suits, to give wearers of these devices the impression of existing within a reality that is broader than what our senses usually perceive. More recently, a number of actual devices have come to the market, with, let's say it, mixed reactions. Some enthusiasts predict rapid improvements in the years ahead, whereas other reviewers focus on disappointing aspects of device performance and user experience.
Our guest in this episode of London Futurists Podcast is someone widely respected as a wise guide in this rather turbulent area. He is Steve Dann, who among other roles is the lead organiser of the highly popular Augmenting Reality meetup in London.
Topics discussed in this episode include: *) Steve's background in film and television special effects *) The different forms of Extended Reality *) Changes in public understanding of virtual and augmented reality *) What can be learned from past disappointments in this field *) Prospects for forthcoming tipping points in market adoption *) Comparisons with the market adoption of smartwatches and of smartphones *) Forecasting incremental improvements in key XR technologies *) Why "VR social media" won't be a sufficient reason for mass adoption of VR *) The need for compelling content *) The particular significance of enterprise use cases *) The potential uses of XR in training, especially for medical professionals *) Different AR and VR use cases in medical training - and different adoption timelines *) Why an alleged drawback of VR may prove to be a decisive advantage for it *) The likely forthcoming battle over words such as "metaverse" *) Why our future online experiences will increasingly be 3D *) Prospects for open standards between different metaverses *) Reasons for companies to avoid rushing to purchase real estate in metaverses *) Movies that portray XR, and the psychological perception of "what is real" *) Examples of powerful real-world consequences of VR experiences.
Music: Spike Protein, by Koi Discovery, available under CC0 1.0 Public Domain Declaration
Governing the transition to AGI, with Jerome Glenn
21 Dec 2022
00:33:58
Our guest on this episode is someone with excellent connections to the foresight departments of governments around the world. He is Jerome Glenn, Founder and Executive Director of the Millennium Project.
The Millennium Project is a global participatory think tank established in 1996, which now has over 70 nodes around the world. It has the stated purpose to "Improve humanity's prospects for building a better world". The organisation produces regular "State of the Future" reports as well as updates on what it describes as "the 15 Global Challenges". It recently released an acclaimed report on three scenarios for the future of work. One of its new projects is the main topic in this episode, namely scenarios for the global governance of the transition from Artificial Narrow Intelligence (ANI) to Artificial General Intelligence (AGI).
Topics discussed in this episode include: *) Why many futurists are jealous of Alvin Toffler *) The benefits of a decentralised, incremental approach to foresight studies *) Special features of the Millennium Project compared to other think tanks *) How the Information Revolution differs from the Industrial Revolution *) What is likely to happen if there is no governance of the transition to AGI *) Comparisons with regulating the use of cars - and the use of nuclear materials *) Options for licensing, auditing, and monitoring *) How the development of a technology may be governed even if it has few visible signs *) Three options: "Hope", "Control", and "Merge" - but all face problems; in all three cases, getting the initial conditions right could make a huge difference *) Distinctions between AGI and ASI (Artificial Superintelligence), and whether an ASI could act in defiance of its initial conditions *) Controlling AGI is likely to be impossible, but controlling the companies that are creating AGI is more credible *) How actions taken by the EU might influence decisions elsewhere in the world *) Options for "aligning" AGI as opposed to "controlling" it *) Complications with the use of advanced AI by organised crime and by rogue states *) The poor level of understanding of most political advisors about AGI, and their tendency to push discussions back to the issues of ANI *) Risks of catastrophic social destabilisation if "the mother of all panics" about AGI occurs on top of existing culture wars and political tribalism *) Past examples of progress with technologies that initially seemed impossible to govern *) The importance of taking some initial steps forward, rather than being overwhelmed by the scale of the challenge.
Music: Spike Protein, by Koi Discovery, available under CC0 1.0 Public Domain Declaration
Selected follow-up reading: https://en.wikipedia.org/wiki/Jerome_C._Glenn https://www.millennium-project.org/ https://www.millennium-project.org/first-steps-for-artificial-general-intelligence-governance-study-have-begun/ The 2020 book "After Shock: The World's Foremost Futurists Reflect on 50 Years of Future Shock - and Look Ahead to the Next 50"
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Introducing Decision Intelligence, with Steven Coates
14 Dec 2022
00:29:56
This episode features the CEO of Brainnwave, Steven Coates, who is a pioneer in the field of Decision Intelligence.
Decision Intelligence is the use of AI to enhance the ability of companies, organisations, or individuals to make key decisions - decisions about which new business opportunities to pursue, about evidence of possible leakage or waste, about the allocation of personnel to tasks, about geographical areas to target, and so on.
What these decisions have in common is that they can all be improved by the analysis of large sets of data that defy attempts to reduce them to a single dimension. In these cases, AI systems that are suited to multi-dimensional analysis can make all the difference between wise and unwise decisions.
Topics discussed in this episode include: *) The ideas initially pursued at Brainnwave, and how they evolved over time *) Real-world examples of Decision Intelligence - in the mining industry, the supply of mobile power generators, and in the oil industry *) Recommendations for businesses to focus on Decision Intelligence as they adopt fuller use of AI, on account of the direct impact on business outcomes *) Factors holding up the wider adoption of AI *) Challenges when "data lakes" turn into "data swamps" *) Challenges with the limits of trust that can be placed in data *) Challenges with the lack of trust in algorithms *) Skills in explaining how algorithms are reaching their decisions *) The benefits of an agile mindset in introducing Decision Intelligence.
Music: Spike Protein, by Koi Discovery, available under CC0 1.0 Public Domain Declaration
Some follow-up reading: https://brainnwave.ai/
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As AI automates larger portions of the activities of companies and organisations, there's a greater need to think carefully about questions of privacy, bias, transparency, and explainability. Due to scale effects, mistakes made by AI and the automated analysis of data can have wide impacts. On the other hand, evidence of effective governance of AI development can deepen trust and accelerate the adoption of significant innovations.
One person who has thought a great deal about these issues is Ray Eitel-Porter, Global Lead for Responsible AI at Accenture. In this episode of the London Futurist Podcast, he explains what conclusions he has reached.
Topics discussed include: *) The meaning and importance of "Responsible AI" *) Connections and contrasts with "AI ethics" and "AI safety" *) The advantages of formal AI governance processes *) Recommendations for the operation of an AI ethics board *) Anticipating the operation of the EU's AI Act *) How different intuitions of fairness can produce divergent results *) Examples where transparency has been limited *) The potential future evolution of the discipline of Responsible AI.
Music: Spike Protein, by Koi Discovery, available under CC0 1.0 Public Domain Declaration
Some follow-up reading: https://www.accenture.com/gb-en/services/applied-intelligence/ai-ethics-governance
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Anticipating Longevity Escape Velocity, with Aubrey de Grey
30 Nov 2022
00:31:05
One area of technology that is frequently in the news these days is rejuvenation biotechnology, namely the possibility of undoing key aspects of biological aging via a suite of medical interventions. What these interventions target isn't individual diseases, such as cancer, stroke, or heart disease, but rather the common aggravating factors that lie behind the increasing prevalence of these diseases as we become older.
Our guest in this episode is someone who has been at the forefront for over 20 years of a series of breakthrough initiatives in this field of rejuvenation biotechnology. He is Dr Aubrey de Grey, co-founder of the Methuselah Foundation, the SENS Research Foundation, and, most recently, the LEV Foundation - where 'LEV' stands for Longevity Escape Velocity.
Topics discussed include: *) Different concepts of aging and damage repair; *) Why the outlook for damage repair is significantly more tangible today than it was ten years ago; *) The role of foundations in supporting projects which cannot receive funding from commercial ventures; *) Questions of pace of development: cautious versus bold; *) Changing timescales for the likely attainment of robust mouse rejuvenation ('RMR') and longevity escape velocity ('LEV'); *) The "Less Death" initiative; *) "Anticipating anticipation" - preparing for likely sweeping changes in public attitude once understanding spreads about the forthcoming available of powerful rejuvenation treatments; *) Various advocacy initiatives that Aubrey is supporting; *) Ways in which listeners can help to accelerate the attainment of LEV.
Music: Spike Protein, by Koi Discovery, available under CC0 1.0 Public Domain Declaration
Some follow-up reading: https://levf.org https://lessdeath.org
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Expanding humanity's moral circle, with Jacy Reese Anthis
23 Nov 2022
00:32:50
A Venn diagram of people interested in how AI will shape our future, and members of the effective altruism community (often abbreviated to EA), would show a lot of overlap. One of the rising stars in this overlap is our guest in this episode, the polymath Jacy Reese Anthis.
Our discussion picks up themes from Jacy's 2018 book “The End of Animal Farming”, including an optimistic roadmap toward an animal-free food system, as well as factors that could alter that roadmap.
We also hear about the work of an organisation co-founded by Jacy: the Sentience Institute, which researches - among other topics - the expansion of moral considerations to non-human entities. We discuss whether AIs can be sentient, how we might know if an AI is sentient, and whether the design choices made by developers of AI will influence the degree and type of sentience of AIs.
The conversation concludes with some ideas about how various techniques can be used to boost personal effectiveness, and considers different ways in which people can relate to the EA community.
Music: Spike Protein, by Koi Discovery, available under CC0 1.0 Public Domain Declaration
Some follow-up reading: https://www.sentienceinstitute.org/ https://jacyanthis.com/
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In the 4th century BC, the Greek philosopher Plato theorised that humans do not perceive the world as it really is. All we can see is shadows on a wall.
In 2003, the Swedish philosopher Nick Bostrom published a paper which formalised an argument to prove Plato was right. The paper argued that one of the following three statements is true: 1. We will go extinct fairly soon 2. Advanced civilisations don’t produce simulations containing entities which think they are naturally-occurring sentient intelligences. (This could be because it is impossible.) 3. We are in a simulation.
The reason for this is that if it is possible, and civilisations can become advanced without exploding, then there will be vast numbers of simulations, and it is vanishingly unlikely that any randomly selected civilisation (like us) is a naturally-occurring one.
Some people find this argument pretty convincing. As we will hear later, some of us have added twists to the argument. But some people go even further, and speculate about how we might bust out of the simulation.
One such person is our friend and our guest in this episode, Roman Yampolskiy, Professor of Computer Science at the University of Louisville.
Music: Spike Protein, by Koi Discovery, available under CC0 1.0 Public Domain Declaration
Further reading:
"How to Hack the Simulation" by Roman Yampolskiy: https://www.researchgate.net/publication/364811408_How_to_Hack_the_Simulation
"The Simulation Argument" by Nick Bostrom: https://www.simulation-argument.com/
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Pioneering AI drug development, with Alex Zhavoronkov
09 Nov 2022
00:39:11
This episode discusses progress at Insilico Medicine, the AI drug development company founded by our guest, longevity pioneer Alex Zhavoronkov.
1.20 In Feb 2022, Insilico got an IPF drug into phase 1 clinical trials: a first for a wholly AI-developed drug 1.50 Insilico is now well-funded; its software is widely used in the pharma industry 2.30 How drug development works. First you create a hypothesis about what causes a disease 4.00 Pandaomics is Insilico’s software to generate hypotheses. It combines 20+ AI models, and huge public data repositories 6.00 This first phase is usually done in academia. It usually costs $ billions to develop a hypothesis. 95% of them fail 6.50 The second phase is developing a molecule which might treat the disease 7.15 This is the job of Insilico’s Chemistry 42 platform 7.30 The classical approach is to test thousands of molecules to see if they bind to the target protein 7.50 AI, by contrast, is able to "imagine" a novel molecule which might bind to it 8.00 You then test 10-15 molecules which have the desired characteristics 8.20 This is done with a variety of genetic algorithms, Generative Adversarial Networks (GANs), and some Transformer networks 8.35 Insilico has a “zoo” of 40 validated models 10.40 Given the ten-fold improvement, why hasn’t the whole drug industry adopted this process? 10.50 They do all have AI groups and they are trying to change, but they are huge companies, and it takes time 11.50 Is it better to invent new molecules, or re-purpose old drugs, which are already known to be safe in humans? 13.00 You can’t gain IP with re-purposed drugs: either somebody else “owns” them, or they are already generic 15.00 The IPF drug was identified during aging research, using aging clocks, and a deep neural net trained on longitudinal data 17.10 The third phase is where Insilico’s other platform, InClinico, comes into play 17.35 InClinico predicts the results of phase 2 (clinical efficacy) trials 18.15 InClinico is trained on massive data sets about previous trials 19.40 InClinico is actually Insilico’s oldest system. Its value has only been ascertained now that some drugs have made it all the way through the pipeline 22.05 A major pharma company asked Insilico to predict the outcome of ten of its trials 22.30 Nine of these ten trials were predicted correctly 23.00 But the company decided that adopting this methodology would be too much of an upheaval; it was unwilling to rely on outsiders so heavily 24.15 Hedge funds and banks have no such qualms 24.25 Insilico is doing pilots for their investments in biotech startups 26.30 Alex is from Latvia originally, studied in Canada, started his career in the US, but Insilico was established in Hong Kong. Why? 27.00 Chinese CROs, Contract Research Organisations, enable you to do research without having your own wetlab 28.00 Like Apple, Insilico designs in the US and does operations in China. You can also do clinical studies there 28.45 They needed their own people inside those CROs, so had to be co-located 29.10 Hong Kong still has great IP protection, financial expertise, scientific resources, and is a beautiful place to live 29.40 Post-Covid, Insilico also had to set up a site in Shanghai 30.35 It is very frustrating how much opposition has built up against international co-operation 32.00 Anti-globalisation ideas and attitudes are bad for longevity research, and all of biotech 33.20 In
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Co-hosts Calum and David dig deep into aspects of David's recent new book "The Singularity Principles". Calum (CC) says he is, in part, unconvinced. David (DW) agrees that the projects he recommends are hard, but suggests some practical ways forward.
0.25 The technological singularity may be nearer than we think 1.10 Confusions about the singularity 1.35 “Taking back control of the singularity” 2.40 The “Singularity Shadow”: over-confident predictions which repulse people 3.30 The over-confidence includes predictions of timescale… 4.00 … and outcomes 4.45 The Singularity as the Rapture of the Nerds? 5.20 The Singularity is not a religion … 5.40 .. although if positive, it will confer almost godlike powers 6.35 Much discussion of the Singularity is dystopian, but there could be enormous benefits, including… 7.15 Digital twins for cells and whole bodies, and super longevity 7.30 A new enlightenment 7.50 Nuclear fusion 8.10 Humanity’s superpower is intelligence 8.30 Amplifying our intelligence should increase our power 9.50 DW’s timeline: 50% chance of AGI by 2050, 10% by 2030 10.10 The timeline is contingent on human actions 10.40 Even if AGI isn’t coming until 2070, we should be working on AI alignment today 11.10 AI Impact’s survey of all contributors to NeurIPS 11.35 Median view: 50% chance of AGI in 2059, and many were pessimistic 12.15 This discussion can’t be left to AI researchers 12.40 A bad beta version might be our last invention 13.00 A few hundred people are now working on AI alignment, and tens of thousands on advancing AI 13.35 The growth of the AI research population is still faster 13.40 CC: Three routes to a positive outcome 13.55 1. Luck. The world turns out to be configured in our favour 14.30 2. Mathematical approaches to AI alignment succeed 14.45 We either align AIs forever, or manage to control them. This is very hard 14.55 3. We merge with the superintelligent machines 15.40 Uploading is a huge engineering challenge 15.55 Philosophical issues raised by uploading: is the self retained? 16.10 DW: routes 2 and 3 are too binary. A fourth route is solving morality 18.15 Individual humans will be augmented, indeed we already are 18.55 But augmented humans won’t necessarily be benign 19.30 DW: We have to solve beneficence 20.00 CC: We can’t hope to solve our moral debates before AGI arrives 20.20 In which case we are relying on route 1 – luck 20.30 DW: Progress in philosophy *is* possible, and must be accelerated 21.15 The Universal Declaration of Human Rights shows that generalised moral principles can be agreed 22.25 CC: That sounds impossible. The UDHR is very broad and often ignored 23.05 Solving morality is even harder than the MIRI project, and reinforces the idea that route 3 is our best hope 23.50 It’s not unreasonable to hope that wisdom correlates with intelligence 24.00 DW: We can proceed step by step, starting with progress on facial recognition, autonomous weapons, and such intermediate questions 25.10 CC: We are so far from solving moral questions. Americans can’t even agree if a coup against their democracy was a bad thing 25.40 DW: We have to make progress, and quickly. AI might help us. 26.50 The essence of transhumanism is that we can use technology to improve ourselves 27.20 CC: If you had a magic wand, your first wish should probably be to make all humans see each other as members of
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How likely is it that, by 2030, someone will build artificial general intelligence (AGI)?
Ross Nordby is an AI researcher who has shortened his AGI timelines: he has changed his mind about when AGI might be expected to exist. He recently published an article on the LessWrong community discussion site, giving his argument in favour of shortening these timelines. He now identifies 2030 as the date by which it is 50% likely that AGI will exist. In this episode, we ask Ross questions about his argument, and consider some of the implications that arise.
Article by Ross: https://www.lesswrong.com/posts/K4urTDkBbtNuLivJx/why-i-think-strong-general-ai-is-coming-soon
MIRI (Machine Intelligence Research Institution): https://intelligence.org/
00.57 Ross’ background: real-time graphics, mostly in video games 02.10 Increased familiarity with AI made him reconsider his AGI timeline 02.37 He submitted a grant request to the Effective Altruism Long-Term Future Fund to move into AI safety work 03.50 What Ross was researching: can we make an AI intrinsically interpretable? 04.25 The AGI Ross is interested in is defined by capability, regardless of consciousness or sentience 04.55 An AI that is itself "goalless" might be put to uses with destructive side-effects 06.10 The leading AI research groups are still DeepMind and OpenAI 06.43 Other groups, like Anthropic, are more interested in alignment 07.22 If you can align an AI to any goal at all, that is progress: it indicates you have some control 08.00 Is this not all abstract and theoretical - a distraction from more pressing problems? 08.30 There are other serious problems, like pandemics and global warming, but we have to solve them all 08.45 Globally, only around 300 people are focused on AI alignment: not enough 10.05 AGI might well be less than three decades away 10.50 AlphaGo surprised the community, which was expecting Go to be winnable 10-15 years later 11.10 Then AlphaGo was surpassed by systems like AlphaZero and MuZero, which were actually simpler, and more flexible 11.20 AlphaTensor frames matrix multiplication as a game, and becomes superhuman at it 11.40 In 2018, the Transformer paper was published, but no-one forecast GPT-3’s capabilities 12.00 This year, Minerva (similar to GPT-3) got 50% correct on the math dataset: high school competition math problems 13.16 Illustrators now feel threatened by systems like Dall-E, Stable Diffusion, etc 13.30 The conclusion is that intelligence is easier to simulate than we thought 13.40 But these systems also do stupid things. They are brittle 18.00 But we could use transformers more intelligently 19.20 They turn out to be able to write code, and to explain jokes, and do maths reasoning 21:10 Google's Gopher AI 22.05 Machines don’t yet have internal models of the world, which we call common sense 24.00 But an early version of GPT-3 demonstrated the ability to model a human thought process alongside a machine’s 27.15 Ross’ current timeline is 50% probability of AGI by 2030, and 90+% by 2050 27:35 Counterarguments? 29.35 So what is to be done? 30.55 If convinced that AGI is coming soon, most lay people would probably demand that all AI research stops immediately. Which isn’t possible 31.40 Maybe publicity would be good in order to generate resources for AI alignment. And t
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The remarkable potential of hydrogen cars, with Hugo Spowers
18 Jul 2025
00:44:24
The guest in this episode is Hugo Spowers. Hugo has led an adventurous life. In the 1970s and 80s he was an active member of the Dangerous Sports Club, which invented bungee jumping, inspired by an initiation ceremony in Vanuatu. Hugo skied down a black run in St.Moritz in formal dress, seated at a grand piano, and he broke his back, neck and hips when he misjudged the length of one of his bungee ropes.
Hugo is a petrol head, and done more than his fair share of car racing. But if he’ll excuse the pun, his driving passion was always the environment, and he is one of the world’s most persistent and dedicated pioneers of hydrogen cars.
He is co-founder and CEO of Riversimple, a 24 year-old pre-revenue startup, which have developed 5 generations of research vehicles. Hydrogen cars are powered by electric motors using electricity generated by fuel cells. Fuel cells are electrolysis in reverse. You put in hydrogen and oxygen, and what you get out is electricity and water.
There is a long-standing debate among energy experts about the role of hydrogen fuel cells in the energy mix, and Hugo is a persuasive advocate. Riversimple’s cars carry modest sized fuel cells complemented by supercapacitors, with motors for each of the four wheels. The cars are made of composites, not steel, because minimising weight is critical for fuel efficiency, pollution, and road safety. The cars are leased rather than sold, which enables a circular business model, involving higher initial investment per car, and no built-in obsolescence. The initial, market entry cars are designed as local run-arounds for households with two cars, which means the fuelling network can be built out gradually. And Hugo also has strong opinions about company governance.
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The terabrain is near, with Simon Thorpe
19 Oct 2022
00:32:51
Why do human brains consume much less power than artificial neural networks? Simon Thorpe, Research Director of CNRS, explains his view that the key to artificial general intelligence is a "terabrain" that copies from human brains the sparse-firing networks with spiking neurons.
00.11 Recapping "the AI paradox" 00.28 The nervousness of CTOs regarding AI 00.43 Introducing Simon 01.43 45 years since Oxford, working out how the brain does amazing things 02.45 Brain visual perception as feed-forward vs. feedback 03.40 The ideas behind the system that performed so well in the 2012 ImageNet challenge 04.20 The role of prompts to alter perception 05.30 Drawbacks of human perceptual expectations 06.05 The video of a gorilla on the basketball court 06.50 Conjuring tricks and distractions 07.10 Energy consumption: human neurons vs. artificial neurons 07.26 The standard model would need 500 petaflops 08.40 Exaflop computing has just arrived 08.50 30 MW vs. 20 W (less than a lightbulb) 09.34 Companies working on low-power computing systems 09.48 Power requirements for edge computing 10.10 The need for 86,000 neuromorphic chips? 10.25 Dense activation of neurons vs. sparse activation 10.58 Real brains are event driven 11.16 Real neurons send spikes not floating point numbers 11.55 SpikeNET by Arnaud Delorme 12.50 Why are sparse networks studied so little? 14.40 A recent debate with Yann LeCun of Facebook and Bill Dally of Nvidia 15.40 One spike can contain many bits of information 16.24 Revisiting an experiment with eels from 1927 (Lord Edgar Adrian) 17.06 Biology just needs one spike 17.50 Chips moved from floating point to fixed point 19.25 Other mentions of sparse systems - MoE (Mixture of Experts) 19.50 Sparse systems are easier to interpret 20.30 Advocacy for "grandmother cells" 21.23 Chicks that imprinted on yellow boots 22.35 A semantic web in the 1960s 22.50 The Mozart cell 23.02 An expert system implemented in a neural network with spiking neurons 23.14 Power consumption reduced by a factor of one million 23.40 Experimental progress 23.53 Dedicated silicon: Spikenet Technology, acquired by BrainChip 24.18 The Terabrain Project, using standard off-the-shelf hardware 24.40 Impressive recent simulations on GPUs and on a MacBook Pro 26.26 A homegrown learning rule 26.44 Experiments with "frozen noise" 27.28 Anticipating emulating an entire human brain on a Mac Studio M1 Ultra 28.25 The likely impact of these ideas 29.00 This software will be given away 29.17 Anticipating "local learning" without the results being sent to Big Tech 30.40 GPT-3 could run on your phone next year 31.12 Our interview next year might be, not with Simon, but with his Terabrain 31.22 Our phones know us better than our spouses do
This episode features Daniel Hulme, founder of Satalia and chief AI officer at WPP. What is AI good at today? And how can organisations increase the likelihood of deploying AI successfully?
02.55 What is AI good at today? 03.25 Deep learning isn’t yet being widely used in companies. Executives are wary of self-adapting systems 04.15 Six categories of AI deployment today 04.20 1. Automation. Using “if … then …” statements 04.50 2. Generative AI, like Dall-E 05.15 3. Humanisation, like DeepFake technology and natural language models 05.40 4. Machine learning to extract insights from data – finding correlations that humans could not 06.05 5. Complex decision making, aka operations research, or optimisation. “Companies don’t have ML problems, they have decision problems” 06.25 6. Augmenting humans physically or cognitively 06.50 Aren’t the tech giants using true AI systems in their operations? 07.15 A/B testing is a simple form of adaptation. Google A/B tested the colours of their logo 08 .00 Complex adaptive systems with many moving parts are much riskier. If they go wrong, huge damage can occur 08.30 CTOs demand consistency from operational systems, and can’t tolerate the mistakes that are essential to learning 09.25 Can’t the mistakes be made in simulated environments? 10.20 Elon Musk says simulating the world is not how to develop self-driving cars 10.45 Companies undergoing digital transformations are building ERPs, which are “glorified databases” 11.20 The idea is to develop digital twins, which enable them to ask “what if…” questions 11.30 The coming confluence of three digital twins: workflow, workforce, and administrative processes 12.18 Why don’t supermarkets offer digital twins to their customers? They’re coming 14.55 People often think that creating a data lake and adding a system like Tableau on top is deploying AI 15.15 Even if you give humans better insights they often don’t make better decisions 15.20 Data scientists are not equipped to address opportunities in all 6 of the categories listed earlier 15.40 Companies should start by identifying and then prioritising the frictions in their organisations 16.10 Some companies are taking on “tech debt” which they will have to unwind in five years 16.25 Why aren’t large process industry companies boasting about massive revenue improvements or cost savings? 17.00 To make those decisions you need the right data, and top optimisation skills. That’s unusual 17.55 Companies ask for “quick wins” but that is an oxymoron 18.10 We do see project ROIs of 200%, but most projects fail due to under-investment, or mis-understandings 19.00 Don’t start by just collecting data. The example of a low-cost airline which collected data about everything except rivals’ pricing 20.15 Humans usually do know where the signals are 22.25 Some of Daniel’s favourite AI projects 23.00 Tesco’s last-mile delivery system, which saves 20m delivery miles a year 24.00 Solving PwC’s consultant allocation problem radically improved many lives 25.10 In the next decade there will be a move away from pure ML towards ML+ optimisation 26.35 How these systems have been applied to Satalia 28.10 Daniel has thought a lot about how AI can enable companies to be very adaptable, and allocate decisions well 29.00 Satalia staff used to make recommendations for their own salaries, and their colleagues would make AI-weighted votes 29.30 The goal is to sc
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Calum and David reflect on their involvement in two recent conferences, one in Riyadh, and one in Dublin. Each conference highlighted a potential disruption in a major industry: a country with large ambitions in the AI space, and a new foundation in the longevity space.
00.00 A tale of two cities, two conferences, two industries 00.44 First, the 2nd Saudi Global AI Conference 01.03 Vision 2030 01.11 Saudi has always been a coalition between the fundamentalist Wahhabis and the Royal Family 01.38 The King chooses reform in the wake of 9/11 02.07 Mohamed bin Salman appointed Crown Prince, who embarks on reform 02.28 The partial liberation of women, and the fundamentalists side-lined 03.10 The “Sheikhdown” in 2017 03.49 The Khashoggi affair and the Yemen war lead to Saudi being shunned 04.26 The West is missing what’s going on in Saudi 05.00 Lifting the Saudi economy’s reliance on petrochemicals 05.27 AI is central to Vision 2030 06.00 Can Saudi become one of the world’s top 10 or 15 AI countries? 06.20 The AI duopoly between the US and China is so strong, this isn’t as hard as you might think 06.55 Saudi’s advantages 07.22 Saudi’s disadvantages 07.54 The goal is not implausible 08.10 The short-term goals of the conference. A forum for discussions, deals, and trying to open the world’s eyes 09.45 Saudi is arguably on the way to becoming another Dubai. Continuation and success are not inevitable, but it is encouraging 11.00 Fastest-growth country in the G20, with an oil bonanza 11.25 The proposed brand-new city of Neom with The Line, a futuristic environment 13.07 The second conference: the Longevity Summit in Dublin 13.48 A new foundation announced 14.05 Reports updating on progress in longevity research around the world 14.20 A dozen were new and surprising. Four examples… 14.50 1. Bats. A speaker from Dublin discussed why they live so long – 40 years – and what we can learn from that 15.55 2. Parabiosis on steroids. Linking the blood flow of two animals suggests there are aging elements in our blood which can be removed 17.50 3. Using AI to develop drugs. Companies like Exscientia and Insilico. Cortex Discovery is a smaller, perhaps more nimble player 19.40 4. Hevolution, a new longevity fund backed with up to $1bn of Saudi money per year for 20 years 22.05 As Aubrey de Grey has long said, we need engineering as much as research 22.40 Aubrey thinks aging should be tackled by undoing cell damage rather than changing the human metabolism 24.00 Three phases of his career. Methuselah. SENS. New foundation 25.00 Let’s avoid cancer, heart disease and dementias by continually reversing aging damage 26.00 He is always itchy to explore new areas. This led to a power struggle within SENS, which he lost 27.00 What should previous SENS donors do now? 27.15 The rich crypto investors who have provided large amounts to SENS are backing the new foundation 28.30 One of the new foundation’s investment areas will be parabiosis 28.55 Cryonics will be another investment area 29.15 Lobbying legislators will be another 29.50 Robust Mouse Rejuvenation will be the initial priority 30.50 Pets may be the animal models whose rejuvenation breaks humanity’s “trance of death” 31.05 David has been appointed a director the new foundation 31.50 The other directors 33.05 An exciting future
Audio engineering by Alexander Chace.
Music: Spike Protein, b
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This episode continues our discussion with AI researcher Aleksa Gordić from DeepMind on understanding today’s most advanced AI systems.
00.07 This episode builds on Episode 5 01.05 We start with GANs – Generative Adversarial Networks 01.33 Solving the problem of stability, with higher resolution 03.24 GANs are notoriously hard to train. They suffer from mode collapse 03.45 Worse, the model might not learn anything, and the result is pure noise 03.55 DC GANs introduced convolutional layers to stabilise them and enable higher resolution 04.37 The technique of outpainting 05.55 Generating text as well as images, and producing stories 06.14 AI Dungeon 06.28 From GANs to Diffusion models 06.48 DDPM (De-noising diffusion probabilistic models) does for diffusion models what DC GANs did for GANs 07.20 They are more stable, and don’t suffer from mode collapse 07.30 They do have downsides. They are much more computation intensive 08.24 What does the word diffusion mean in this context? 08.40 It’s adopted from physics. It peels noise away from the image 09.17 Isn’t that rewinding entropy? 09.45 One application is making a photo taken in 1830 look like one taken yesterday 09.58 Semantic Segmentation Masks convert bands of flat colour into realistic images of sky, earth, sea, etc 10.35 Bounding boxes generate objects of a specified class from tiny inputs 11.00 The images are not taken from previously seen images on the internet, but invented from scratch 11.40 The model saw a lot of images during training, but during the creation process it does not refer back to them 12.40 Failures are eliminated by amendments, as always with models like this 12.55 Scott Alexander blogged about models producing images with wrong relationships, and how this was fixed within 3 months 13.30 The failure modes get harder to find as the obvious ones are eliminated 13.45 Even with 175 billion parameters, GPT-3 struggled to handle three digits in computation 15.18 Are you often surprised by what the models do next? 15.50 The research community is like a hive mind, and you never know where the next idea will come from 16.40 Often the next thing comes from a couple of students at a university 16.58 How Ian Goodfellow created the first GAN 17.35 Are the older tribes described by Pedro Domingos (analogisers, evolutionists, Bayesians…) now obsolete? 18.15 We should cultivate different approaches because you never know where they might lead 19.15 Symbolic AI (aka Good Old Fashioned AI, or GOFAI) is still alive and kicking 19.40 AlphaGo combined deep learning and GOFAI 21.00 Doug Lennart is still persevering with Cyc, a purely GOFAI approach 21.30 GOFAI models had no learning element. They can’t go beyond the humans whose expertise they encapsulate 22.25 The now-famous move 37 in AlphaGo’s game two against Lee Sedol in 2016 23.40 Moravec’s paradox. Easy things are hard, and hard things are easy 24.20 The combination of deep learning and symbolic AI has been long urged, and in fact is already happening 24.40 Will models always demand more and more compute? 25.10 The human brain has far more compute power than even our biggest systems today 25.45 Sparse, or MoE (Mixture of Experts) systems are quite efficient 26.00 We need more compute, better algorithms, and more efficiency 26.55 Dedicated AI chips will help a lot with efficiency 26.25 Cerebros claims that GPT-3 could be trai
C-Suite Perspectives Elevate how you lead with insight from today’s most influential executives.
Welcome to episode 5 of the London Futurist podcast, with your co-hosts David Wood and Calum Chace.
We’re attempting something rather ambitious in episodes 5 and 6. We try to explain how today’s cutting edge artificial intelligence systems work, using language familiar to lay people, rather than people with maths or computer science degrees.
Understanding how Transformers and Generative Adversarial Networks (GANs) work means getting to grips with concepts like matrix transformations, vectors, and landscapes with 500 dimensions.
This is challenging stuff, but do persevere. These AI systems are already having a profound impact, and that impact will only grow. Even at the level of pure self-interest, it is often said that in the short term, AIs won’t take all the jobs, but people who understand AI will take the best jobs.
We are extremely fortunate to have as our guide for these episodes a brilliant AI researcher at DeepMind, Aleksa Gordić.
Note that Aleksa is speaking in personal capacity and is not representing DeepMind.
Aleksa's YouTube channel is https://www.youtube.com/c/TheAIEpiphany
00.03 An ambitious couple of episodes 01.22 Introducing Aleksa, a double rising star 02.15 Keeping it simple 02.50 Aleksa's current research, and previous work on Microsoft's HoloLens 03.40 Self-taught in AI. Not representing DeepMind 04.20 The narrative of the Big Bang in 2012, when Machine Learning started to work in AI. 05.15 What machine learning is 05.45 AlexNet. Bigger data sets and more powerful computers 06.40 Deep learning a subset of machine learning, and a re-branding of artificial neural networks 07.27 2017 and the arrival of Transformers 07.40 Attention is All You Need 08.16 Before this there were LSTMs, Long Short-Term Memories 08.40 Why Transformers beat LSTMs 09.58 Tokenisation. Splitting text into smaller units and mapping them onto higher dimension networks 10.30 3D space is defined by three numbers 10.55 Humans cannot envisage multi-dimensional spaces with hundreds of dimensions, but it's OK to imagine them as 3D spaces 11.55 Some dimensions of the word "princess" 12.30 Black boxes 13.05 People are trying to understand how machines handle the dimensions 13.50 "Man is to king as woman is to queen." Using mathematical operators on this kind of relationship 14.35 Not everything is explainable 14.45 Machines discover the relationships themselves 15.15 Supervised and self-supervised learning. Rewarding or penalising the machine for predicting labels 16.25 Vectors are best viewed as arrows in 3D space, although that is over-simplifying 17.20 For instance the relationship between "queen" and "woman" is a vector 17.50 Self-supervised systems do their own labelling 18.30 The labels and relationships have probability distributions 19.20 For instance, a princess is far more likely to wear a slipper than a dog 19.35 Large numbers of parameters 19.40 BERT, the original Transformer, had a hundred million or so parameters 20.04 Now it's in the hundreds of billions, or even trillions 20.24 A parameter is analogous to a synapse in the human brain 21.19 Synapses can have different weights 22.10 The more parameters, the lower the loss 22.35 Not just text, but images too, because images can also be represented as tokens 23.00 In late 2020 Google released the first vision Transformer 23.29 Dall-E and Midjourney are diffusion models, whic
C-Suite Perspectives Elevate how you lead with insight from today’s most influential executives.
In this episode, co-hosts Calum Chace and David Wood explore a number of recent developments in AI - developments that are rapidly changing what counts as "state of the art" in AI.
00.05: Short recap of previous episodes 00.20: A couple of Geoff Hinton stories 02.27: Today's subject: the state of AI today 02.53: Search 03.35: Games 03.58: Translation 04.33: Maps 05.33: Making the world understandable. Increasingly 07.00: Transformers. Attention is all you need 08.00: Masked language models 08.18: GPT-2 and GPT-3 08.54: Parameters and synapses 10.15: Foundation models produce much of the content on the internet 10.40: Data is even more important than size 11.45: Brittleness and transfer learning 13.15: Do machines understand? 14.05: Human understanding and stochastic parrots 15.27: Chatbots 16.22: Tay embarrasses Microsoft 16.53: Blenderbot 17.19: Far from AGI. LaMDA and Blaise Lemoine 18.26: The value of anthropomorphising 19.53: Automation 20.25: Robotic Process Automation (RPA) 20.55: Drug discovery 21.45: New antibiotics. Discovering Halicin 23.50: AI drug discovery as practiced by Insilico, Exscientia and others 25.33: Eroom's Law 26.34: AlphaFold. How 200m proteins fold 28.30: Towards a complete model of the cell 29.19: Analysis 30.04: Air traffic controllers use only 10% of the data available to them 30.36: Transfer learning can mitigate the escalating demand for compute power 31.18: Next up: the short-term future of AI
Audio engineering by Alexander Chace.
Music: Spike Protein, by Koi Discovery, available under CC0 1.0 Public Domain Declaration
For more about the podcast hosts, see https://calumchace.com/ and https://dw2blog.com/
C-Suite Perspectives Elevate how you lead with insight from today’s most influential executives.
AI overview: 2. The Big Bang and the years that followed
07 Sep 2022
00:32:28
In this episode, co-hosts Calum Chace and David Wood continue their review of progress in AI, taking up the story at the 2012 "Big Bang".
00.05: Introduction: exponential impact, big bangs, jolts, and jerks 00.45: What enabled the Big Bang 01.25: Moore's Law 02.05: Moore's Law has always evolved since its inception in 1965 03.08: Intel's tick tock becomes tic tac toe 03.49: GPUs - Graphic Processing Units 04.29: TPUs - Tensor Processing Units 04.46: Moore's Law is not dead or dying 05.10: 3D chips 05.32: Memristors 05.54: Neuromorphic chips 06.48: Quantum computing 08.18: The astonishing effect of exponential growth 09.08: We have seen this effect in computing already. The cost of an iPhone in the 1950s. 09.42: Exponential growth can't continue forever, but Moore's Law hasn't reached any theoretical limits 10.33: Reasons why Moore's Law might end: too small, too expensive, not worthwhile 11.20: Counter-arguments 12.01: "Plenty more room at the bottom" 12.56: Software and algorithms can help keep Moore's Law going 14.15: Using AI to improve chip design 14.40: Data is critical 15.00: ImageNet, Fei Fei Lee, Amazon Turk 16.10: AIs labelling data 16.35: The Big Bang 17.00: Jürgen Schmidhuber challenges the narrative 17.41: The Big Bang enabled AI to make money 18.24: 2015 and the Great Robot Freak-Out 18.43: Progress in many domains, especially natural language processing 19.44: Machine Learning and Deep Learning 20.25: Boiling the ocean vs the scientific method's hypothesis-driven approach 21.15: Deep Learning: levels 21.57: How Deep Learning systems recognise faces 22.48: Supervised, Unsupervised, and Reinforcement Learning 24.00: Variants, including Deep Reinforcement Learning and Self-Supervised Learning 24.30: Yann LeCun's camera metaphor for Deep Learning 26.05: Lack of transparency is a concern 27.45: Explainable AI. Is it achievable? 29.00: Other AI problems 29.17: Has another Big Bang taken place? Large Language Models like GPT-3 30.08: Few-shot learning and transfer learning 30.40: Escaping Uncanny Valley 31.50: Gato and partially general AI
Music: Spike Protein, by Koi Discovery, available under CC0 1.0 Public Domain Declaration
For more about the podcast hosts, see https://calumchace.com/ and https://dw2blog.com/
C-Suite Perspectives Elevate how you lead with insight from today’s most influential executives.
AI is a subject that we will all benefit from understanding better. In this episode, co-hosts Calum Chace and David Wood review progress in AI from the Greeks to the 2012 "Big Bang".
00.05: A prediction 01.09: AI is likely to cause two singularities in this pivotal century - a jobless economy, and superintelligence 02.22: Counterpoint: it may require AGI to displace most people from the workforce. So only one singularity? 03.27: Jobs are nowhere near all that matters in humans 04.11: Are the "Three Cs jobs" safe? Those involving Creativity, Compassion, and Commonsense? Probably not. 05.15: 2012, the Big Bang in AI 05.48: AI now makes money. Google and Facebook ate Rupert Murdoch's lunch 06.30: AI might make the difference between military success and military failure. So there's a geopolitical race as well as a commercial race 07.18: Defining AI. 09.03: Intelligence vs Consciousness 10.15: Does the Turing Test test for Intelligence or Consciousness? 12.30: Can customer service agents pass the Turing Test? 13.07: Attributing consciousness by brain architecture or by behaviour 15.13: Creativity. Move 37 in game two of AlphaGo vs Lee Sedol, and Hassabis' three buckets of creativity 17.13: Music and art produced by AI as examples 19.05: History: Start with the Greeks, Hephaestus (Vulcan to the Romans) built automata, and Aristotle speculated about technological unemployment 19.58: AI has featured in science fiction from the beginning, eg Mary Shelley's Frankenstein, Samuel Butler's Erewhon, E.M. Forster's "The Machine Stops" 20.55: Post-WW2 developments. Conference in Paris in 1951 on "Computing machines and human thought". Norbert Weiner and cybernetics 22.48: The Dartmouth Conference 23.55: Perceptrons - very simple models of the human brain 25.13: Perceptrons debunked by Minsky and Papert, so Symbolic AI takes over 25.49: This debunking was a mistake. More data and better hardware overcomes the hurdles 27.20: Two AI winters, when research funding dries up 28.07: David was taught maths at Cambridge by James Lighthill, author of the report which helped cause the first AI winter 28.58: The Japanese 5th generation computing project under-delivered in the 1980s. But it prompted an AI revival, and its ambitions have been realised by more recent advances 30.45: No more AI winters?
Music: Spike Protein, by Koi Discovery, available under CC0 1.0 Public Domain Declaration
For more about the podcast hosts, see https://calumchace.com/ and https://dw2blog.com/
C-Suite Perspectives Elevate how you lead with insight from today’s most influential executives.
Co-hosts David Wood and Calum Chace share their vision and plans for the London Futurists podcast.
00.20: Why we are launching this podcast. Anticipating and managing exponential impact 02.45: It’s not the Fourth Industrial Revolution – it’s the Information Revolution 04.58: AI’s impact. Smartphones as an example of technology’s power 09.04: The obviousness of change in hindsight. Why technology implementation is often slow 11.30: Technology implementation is often delayed by poor planning 15:20: We were promised jetpacks. Instead, we got omniscience 17.14: Technological development is not deterministic, and it contains dangers 19.08: Technologies are always double-edged swords. They might be somewhat deterministic 22.03: Better hindsight enables better foresight 23.06: Introducing ourselves 23.13: David bio 24.53: Calum bio 26.44: Fiction and non-fiction. We need more positive stories 27.37: Topics for future episodes 28.03: There are connections between all these topics 28.42: Excited by technology, but realistic 29.24: Securing a great future
Music: Spike Protein, by Koi Discovery, available under CC0 1.0 Public Domain Declaration
For more about the podcast hosts, see https://calumchace.com/ and https://dw2blog.com/
C-Suite Perspectives Elevate how you lead with insight from today’s most influential executives.
Can we use AI to improve how we handle conflict? Or even to end the worst conflicts that are happening all around us? That’s the subject of the new book of our guest in this episode, Simon Horton. The book has the bold title “The End of Conflict: How AI will end war and help us get on better”.
Simon has a rich background, including being a stand-up comedian and a trapeze artist – which are, perhaps, two useful skills for dealing with acute conflict. He has taught negotiation and conflict resolution for 20 years, across 25 different countries, where his clients have included the British Army, the Saudi Space Agency, and Goldman Sachs. His previous books include “Change their minds” and “The leader’s guide to negotiation”.
The AI disconnect: understanding vs motivation, with Nate Soares
11 Jun 2025
00:49:31
Our guest in this episode is Nate Soares, President of the Machine Intelligence Research Institute, or MIRI.
MIRI was founded in 2000 as the Singularity Institute for Artificial Intelligence by Eliezer Yudkowsky, with support from a couple of internet entrepreneurs. Among other things, it ran a series of conferences called the Singularity Summit. In 2012, Peter Diamandis and Ray Kurzweil, acquired the Singularity Summit, including the Singularity brand, and the Institute was renamed as MIRI.
Nate joined MIRI in 2014 after working as a software engineer at Google, and since then he’s been a key figure in the AI safety community. In a blogpost at the time he joined MIRI he observed “I turn my skills towards saving the universe, because apparently nobody ever got around to teaching me modesty.”
MIRI has long had a fairly pessimistic stance on whether AI alignment is possible. In this episode, we’ll explore what drives that view—and whether there is any room for hope.
Anticipating an Einstein moment in the understanding of consciousness, with Henry Shevlin
28 May 2025
00:41:40
Our guest in this episode is Henry Shevlin. Henry is the Associate Director of the Leverhulme Centre for the Future of Intelligence at the University of Cambridge, where he also co-directs the Kinds of Intelligence program and oversees educational initiatives.
He researches the potential for machines to possess consciousness, the ethical ramifications of such developments, and the broader implications for our understanding of intelligence.
In his 2024 paper, “Consciousness, Machines, and Moral Status,” Henry examines the recent rapid advancements in machine learning and the questions they raise about machine consciousness and moral status. He suggests that public attitudes towards artificial consciousness may change swiftly, as human-AI interactions become increasingly complex and intimate. He also warns that our tendency to anthropomorphise may lead to misplaced trust in and emotional attachment to AIs.
Note: this episode is co-hosted by David and Will Millership, the CEO of a non-profit called Prism (Partnership for Research Into Sentient Machines). Prism is seeded by Conscium, a startup where both Calum and David are involved, and which, among other things, is researching the possibility and implications of machine consciousness. Will and Calum will be releasing a new Prism podcast focusing entirely on Conscious AI, and the first few episodes will be in collaboration with the London Futurists Podcast.
The case for a conditional AI safety treaty, with Otto Barten
09 May 2025
00:37:41
How can a binding international treaty be agreed and put into practice, when many parties are strongly tempted to break the rules of the agreement, for commercial or military advantage, and when cheating may be hard to detect? That’s the dilemma we’ll examine in this episode, concerning possible treaties to govern the development and deployment of advanced AI.
Our guest is Otto Barten, Director of the Existential Risk Observatory, which is based in the Netherlands but operates internationally. In November last year, Time magazine published an article by Otto, advocating what his organisation calls a Conditional AI Safety Treaty. In March this year, these ideas were expanded into a 34-page preprint which we’ll be discussing today, “International Agreements on AI Safety: Review and Recommendations for a Conditional AI Safety Treaty”.
Before co-founding the Existential Risk Observatory in 2021, Otto had roles as a sustainable energy engineer, data scientist, and entrepreneur. He has a BSc in Theoretical Physics from the University of Groningen and an MSc in Sustainable Energy Technology from Delft University of Technology.
In this episode, we return to the subject of existential risks, but with a focus on what actions can be taken to eliminate or reduce these risks.
Our guest is James Norris, who describes himself on his website as an existential safety advocate. The website lists four primary organizations which he leads: the International AI Governance Alliance, Upgradable, the Center for Existential Safety, and Survival Sanctuaries.
Previously, one of James' many successful initiatives was Effective Altruism Global, the international conference series for effective altruists. He also spent some time as the organizer of a kind of sibling organization to London Futurists, namely Bay Area Futurists. He graduated from the University of Texas at Austin with a triple major in psychology, sociology, and philosophy, as well as with minors in too many subjects to mention.
Human extinction: thinking the unthinkable, with Sean ÓhÉigeartaigh
23 Apr 2025
00:42:34
Our subject in this episode may seem grim – it’s the potential extinction of the human species, either from a natural disaster, like a supervolcano or an asteroid, or from our own human activities, such as nuclear weapons, greenhouse gas emissions, engineered biopathogens, misaligned artificial intelligence, or high energy physics experiments causing a cataclysmic rupture in space and time.
These scenarios aren’t pleasant to contemplate, but there’s a school of thought that urges us to take them seriously – to think about the unthinkable, in the phrase coined in 1962 by pioneering futurist Herman Kahn. Over the last couple of decades, few people have been thinking about the unthinkable more carefully and systematically than our guest today, Sean ÓhÉigeartaigh. Sean is the author of a recent summary article from Cambridge University Press that we’ll be discussing, “Extinction of the human species: What could cause it and how likely is it to occur?”
Sean is presently based in Cambridge where he is a Programme Director at the Leverhulme Centre for the Future of Intelligence. Previously he was founding Executive Director of the Centre for the Study of Existential Risk, and before that, he managed research activities at the Future of Humanity Institute in Oxford.
The best of times and the worst of times, updated, with Ramez Naam
26 Mar 2025
00:45:14
Our guest in this episode, Ramez Naam, is described on his website as “climate tech investor, clean energy advocate, and award-winning author”. But that hardly starts to convey the range of deep knowledge that Ramez brings to a wide variety of fields. It was his 2013 book, “The Infinite Resource: The Power of Ideas on a Finite Planet”, that first alerted David to the breadth of scope of his insight about future possibilities – both good possibilities and bad possibilities. He still vividly remembers its opening words, quoting Charles Dickens from “The Tale of Two Cities”:
Quote: “‘It was the best of times; it was the worst of times’ – the opening line of Charles Dickens’s 1859 masterpiece applies equally well to our present era. We live in unprecedented wealth and comfort, with capabilities undreamt of in previous ages. We live in a world facing unprecedented global risks—risks to our continued prosperity, to our survival, and to the health of our planet itself. We might think of our current situation as ‘A Tale of Two Earths’.” End quote.
12 years after the publication of “The Infinite Resource”, it seems that the Earth has become even better, but also even worse. Where does this leave the power of ideas? Or do we need more than ideas, as ominous storm clouds continue to gather on the horizon?
When we started this Podcast back in August 2022, we, Calum and David, announced the theme to be “Anticipating and managing exponential impact”. We talked about three sub-themes: Developing the skills of exponential foresight; Distinguishing between scenarios, whether they were plausible or implausible, and whether they were desirable or undesirable; and thirdly, Supporting the community of collaborative exponential foresight. 126 episodes later, as we reach the transition between 2025 and 2026, it’s a good time for the two of us to take stock.
Accordingly, in this episode, we each pick out a number of events from the last 12 months which we see as potential signals of larger exponential impact ahead.
PAI at Paris: the global AI ecosystem evolves, with Rebecca Finlay
27 Feb 2025
00:38:58
In this episode, our guest is Rebecca Finlay, the CEO at Partnership on AI (PAI). Rebecca previously joined us in Episode 62, back in October 2023, in what was the run-up to the Global AI Safety Summit in Bletchley Park in the UK. Times have moved on, and earlier this month, Rebecca and the Partnership on AI participated in the latest global summit in that same series, held this time in Paris. This summit, breaking with the previous naming, was called the Global AI Action Summit. We’ll be hearing from Rebecca how things have evolved since we last spoke – and what the future may hold.
Prior to joining Partnership on AI, Rebecca founded the AI & Society program at global research organization CIFAR, one of the first international, multistakeholder initiatives on the impact of AI in society. Rebecca’s insights have been featured in books and media including The Financial Times, The Guardian, Politico, and Nature Machine Intelligence. She is a Fellow of the American Association for the Advancement of Sciences and sits on advisory bodies in Canada, France, and the U.S.
AI agents: challenges ahead of mainstream adoption, with Tom Davenport
03 Feb 2025
00:34:07
The most highly anticipated development in AI this year is probably the expected arrival of AI agents, also referred to as “agentic AI”. We are told that AI agents have the potential to reshape how individuals and organizations interact with technology.
Our guest to help us explore this is Tom Davenport, Distinguished Professor in Information Technology and Management at Babson College, and a globally recognized thought leader in the areas of analytics, data science, and artificial intelligence. Tom has written, co-authored, or edited about twenty books, including "Competing on Analytics" and "The AI Advantage." He has worked extensively with leading organizations and has a unique perspective on the transformative impact of AI across industries. He has recently co-authored an article in the MIT Sloan Management Review, “Five Trends in AI and Data Science for 2025”, which included a section on AI agents – which is why we invited him to talk about the subject.
In this episode, we return to a theme which is likely to become increasingly central to public discussion in the months and years ahead. To use a term coined by this podcast’s cohost Calum Chace, this theme is the Economic Singularity, namely the potential all-round displacement of humans from the workforce by ever more capable automation. That leads to the question: what are our options for managing the transition of society to increasing technological unemployment and technological underemployment.
Our guest, who will be sharing his thinking on these questions, is the prolific writer and YouTuber David Shapiro. As well as keeping on top of fast-changing news about innovations in AI, David has been developing a set of ideas he calls post-labour economics – how an economy might continue to function even if humans can no longer gain financial rewards in direct return for their labour.
Longevity activism at 82, 86, and beyond, with Kenneth Scott and Helga Sands
10 Jan 2025
00:45:48
Our guests in this episode have been described as the world’s two oldest scientifically astute longevity activists. They are Kenneth Scott, aged 82, who is based in Florida, and Helga Sands, aged 86, who lives in London.
David has met both of them several times at a number of longevity events, and they always impress him, not only with their vitality and good health, but also with the level of knowledge and intelligence they apply to the question of which treatments are the best, for them personally and for others, to help keep people young and vibrant.
Models for society when humans have zero economic value, with Jeff LaPorte
02 Jan 2025
00:42:18
Our guest in this episode is Jeff LaPorte, a software engineer, entrepreneur and investor based in Vancouver, who writes Road to Artificia, a newsletter about discovering the principles of post‑AI societies.
Calum recently came across Jeff's article “Valuing Humans in the Age of Superintelligence: HumaneRank” and thought it had some good, original ideas, so we wanted to invite Jeff onto the podcast and explore them.
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From ineffective altruism to effective altruism? with Stefan Schubert
26 Dec 2024
00:34:50
Our subject in this episode is altruism – our human desire and instinct to assist each other, making some personal sacrifices along the way. More precisely, our subject is the possible future of altruism – a future in which our philanthropic activities – our charitable donations, and how we spend our discretionary time – could have a considerably greater impact than at present. The issue is that many of our present activities, which are intended to help others, aren’t particularly effective.
That’s the judgement reached by our guest today, Stefan Schubert. Stefan is a researcher in philosophy and psychology, currently based in Stockholm, Sweden, and has previously held roles at the LSE and the University of Oxford. Stefan is the co-author of the recently published book “Effective Altruism and the Human Mind”.
The global energy transition: an optimistic assessment, with Amory Lovins
16 Dec 2024
00:35:12
Our guest in this episode is Amory Lovins, a distinguished environmental scientist, and co-founder of RMI, which he co-founded in 1982 as Rocky Mountain Institute. It’s what he calls a think do and scale tank, with 700 people in 62 countries, and a budget of well over $100m a year.
For over five decades, Amory has championed innovative approaches to energy systems, advocating for a world where energy services are delivered with least cost and least impact. He has advised all manner of governments, companies, and NGOs, and published 31 books and over 900 papers. It’s an over-used word, but in this case it is justified: Amory is a true thought leader in the global energy transition.
Some people say that all that’s necessary to improve the capabilities of AI is to scale up existing systems. That is, to use more training data, to have larger models with more parameters in them, and more computer chips to crunch through the training data. However, in this episode, we’ll be hearing from a computer scientist who thinks there are many other options for improving AI. He is Alexander Ororbia, a professor at the Rochester Institute of Technology in New York State, where he directs the Neural Adaptive Computing Laboratory.
David had the pleasure of watching Alex give a talk at the AGI 2024 conference in Seattle earlier this year, and found it fascinating. After you hear this episode, we hope you reach a similar conclusion.