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    Technology

    London Futurists

    Anticipating and managing exponential impact – hosts David Wood and Calum Chace

    Calum Chace is a sought-after keynote speaker and best-selling writer on artificial intelligence. He focuses on the medium- and long-term impact of AI on all of us, our societies and our economies. He advises companies and governments on AI policy.

    His non-fiction books on AI are Surviving AI, about superintelligence, and The Economic Singularity, about the future of jobs. Both are now in their third editions.

    He also wrote Pandora’s Brain and Pandora’s Oracle, a pair of techno-thrillers about the first superintelligence. He is a regular contributor to magazines, newspapers, and radio.

    In the last decade, Calum has given over 150 talks in 20 countries on six continents. Videos of his talks, and lots of other materials are available at https://calumchace.com/.

    He is co-founder of a think tank focused on the future of jobs, called the Economic Singularity Foundation. The Foundation has published Stories from 2045, a collection of short stories written by its members.

    Before becoming a full-time writer and speaker, Calum had a 30-year career in journalism and in business, as a marketer, a strategy consultant and a CEO. He studied philosophy, politics, and economics at Oxford University, which confirmed his suspicion that science fiction is actually philosophy in fancy dress.

    David Wood is Chair of London Futurists, and is the author or lead editor of twelve books about the future, including The Singularity Principles, Vital Foresight, The Abolition of Aging, Smartphones and Beyond, and Sustainable Superabundance.

    He is also principal of the independent futurist consultancy and publisher Delta Wisdom, executive director of the Longevity Escape Velocity (LEV) Foundation, Foresight Advisor at SingularityNET, and a board director at the IEET (Institute for Ethics and Emerging Technologies). He regularly gives keynote talks around the world on how to prepare for radical disruption. See https://deltawisdom.com/.

    As a pioneer of the mobile computing and smartphone industry, he co-founded Symbian in 1998. By 2012, software written by his teams had been included as the operating system on 500 million smartphones.

    From 2010 to 2013, he was Technology Planning Lead (CTO) of Accenture Mobility, where he also co-led Accenture’s Mobility Health business initiative.

    Has an MA in Mathematics from Cambridge, where he also undertook doctoral research in the Philosophy of Science, and a DSc from the University of Westminster.

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    Copyright: © 2022 London Futurists

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    Latest Episodes:
    Developing responsible AI, with Ray Eitel-Porter Dec 07, 2022
    Show notes

    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


    Anticipating Longevity Escape Velocity, with Aubrey de Grey Nov 30, 2022
    Show notes

    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


    Expanding humanity's moral circle, with Jacy Reese Anthis Nov 23, 2022
    Show notes

    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/


    Hacking the simulation, with Roman Yampolskiy Nov 16, 2022
    Show notes

    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/


    Pioneering AI drug development, with Alex Zhavoronkov Nov 09, 2022
    Show notes

    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 Insilico has all the data it needs. Its bottleneck is talent
    35.00 Another requirement is co-operation from governments and regulators, who often struggle to sort the chaff from the wheat in self-proclaimed AI companies
    37.00 Longevity research is the most philanthropic activity in the world
    37.30 Longevity Medicine Course is available to get clinical practitioners up to speed with the sector


    The Singularity Principles Nov 02, 2022
    Show notes

    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 the same tribe
    27.50 Is AI ethics a helpful term?
    28.05 AI ethics is a growing profession, but if problems are ethical then people who disagree with you are bad, not just wrong
    28.55 AI ethics makes debates about AI harder to resolve, and more angry
    29.15 AI researchers are understandably offended by finger-wagging, self-proclaimed AI ethicists who may not understand what they are talking about


    Collapsing AGI timelines, with Ross Nordby Oct 26, 2022
    Show notes

    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
    Effective Altruism Long-Term Future Fund: https://funds.effectivealtruism.org/funds/far-future
    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 to avoid a backlash against secrecy
    33.55 It would be great if more billionaires opened their wallets, but actually there are funds available for people who want to work on the problem
    34.20 People who can help would not have to take a pay cut to work on AI alignment
    Audio engineering by Alexander Chace
    Music: Spike Protein, by Koi Discovery, available under CC0 1.0 Public Domain Declaration


    The terabrain is near, with Simon Thorpe Oct 19, 2022
    Show notes

    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
    Simon's academic page: https://cerco.cnrs.fr/page-perso-simon-thorpe/
    Simon's personal blog: https://simonthorpesideas.blogspot.com/
    Audio engineering by Alexander Chace.
    Music: Spike Protein, by Koi Discovery, available under CC0 1.0 Public Domain Declaration


    AI for organisations, with Daniel Hulme Oct 12, 2022
    Show notes

    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 scale this approach not just across WPP, but across the planet
    30.35 Heads of HR in WPP operating companies love the idea
    Daniel's entry on Wikipedia: https://en.wikipedia.org/wiki/Daniel_J._Hulme
    Audio engineering by Alexander Chace.
    Music: Spike Protein, by Koi Discovery, available under CC0 1.0 Public Domain Declaration


    A tale of two cities: Riyadh and Dublin Oct 05, 2022
    Show notes

    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, by Koi Discovery, available under CC0 1.0 Public Domain Declaration
    The conference websites: https://globalaisummit.org/ and https://longevitysummitdublin.com/
    For more about the podcast hosts, see https://calumchace.com/ and https://dw2blog.com/


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