Podcasts about turing

My guest today is Jonathan Siddharth, co-founder and CEO of Turing.Jonathan incubated Turing in Foundation Capital's Palo Alto office in 2018. Since then, it has grown into a multi-billion dollar company that powers nearly every frontier AI lab: OpenAI, Anthropic, Google, Meta, Microsoft, and others. If you've seen a breakthrough in how AI reasons or codes, odds are Turing had a hand in it.Jonathan has a provocative thesis: within three years, every white-collar job, including the CEO's, will be automated. In this episode, we talk about what it will take to reach artificial superintelligence, why this goal matters, and how the agentic era will fundamentally reshape work. We also dig into his founder journey: what he learned from his first startup Rover, how he built Turing from day one, and how his leadership style has evolved to emphasize speed, intensity, and staying in the details.Jonathan has been at the edge of AI for years, and he has the rare ability to translate what's happening at the frontier into lessons for builders today.Hope you enjoy the conversation! Chapters: 00:00 Cold open00:02:06 Jonathan's backstory: his experience at Stanford00:06:37 Lessons from Rover00:08:39 Early Turing: incubation at Foundation Capital and finding PMF00:13:52 Why Turing took off00:15:12 Evolving from developer cloud to AGI partner for frontier labs00:16:49 How coding improved reasoning - and why Turing became essential00:20:38 Founder lessons: building org speed and intensity00:23:33 Why work-life balance is a false dichotomy00:24:17 Daily standups, flat orgs, and Formula One culture00:25:15 Confrontational energy and Frank Slootman's influence00:29:50 Positioning Turing as “Switzerland” in the AI arms race00:34:32 The four pillars of superintelligence: multimodality, reasoning, tool use, coding00:37:39 From copilots to agents: the 100x improvement00:40:00 Why enterprise hasn't had its “ChatGPT moment” yet00:43:09 Jonathan's thoughts on RL gyms, algorithmic techniques, and evals00:46:32 The blurring line between model providers and AI apps00:47:35 Why defensibility depends on proprietary data and evals00:55:20 RL gyms: how enterprises train agents in simulated environments00:57:39 Underhyped: $30T of white-collar work will be automated

¿Quién decide primero: tú o tu mente? ¿Y qué nos diferencia realmente de la inteligencia artificial que no deja de avanzar? En esta conversación con Alberto, partimos del test de Turing para abrir una pregunta mayor: la consciencia. A partir de ahí, nos detenemos en ese microinstante entre impulso y respuesta y exploramos cómo cultivar una actitud de humildad radical para pasar del impulso al diálogo; del “tengo razón” al escuchar de verdad. Deseo que esta conversación te ayude a habitar el instante entre estímulo y respuesta, a recuperar el control y a recordar lo esencial de lo humano en tiempos de IA.

Blade Runner y el test de Turing.

Alan Turing ist der Mann, der die Enigma knackte. Jene Verschlüsselungsmaschine, mit der die deutsche Wehrmacht im zweiten Weltkrieg ihre Funksprüche codierte. Bis in die 70iger Jahre hinein blieb Vieles um den Kriegshelden Alan Turing geheim. So heldenhaft und strahlend wie Turing als Mathematiker und Computerpionier unterwegs war, so dunkel und tragisch sind seine letzten Lebensjahre. Der homosexuelle Wissenschaftler wählte den Freitod. Vielen Dank an Experte Dr. Jochen Viehoff vom HNF in Paderborn.

Crime and Punishment by Fyodor Dostoyevsky - Part 5 chapter 3, narrated by Isaac BirchallSubscribe on YT or Join the Book Club on Patreon and support me as an independent creator :Dhttps://ko-fi.com/theessentialreadshttps://www.youtube.com/channel/UCfOFfvo05ElM96CmfsGsu3g/joinhttps://open.spotify.com/show/13b1qP3WhCWxam9yc49vIF?si=3b8907ab0f1045af SUMMARY: Luzhin insulting brushes Katerina Ivanovna aside as she seeks protection from the Landlady. Turing to Sonya, he accuses her of stealing a 100 Ruble note. Sonya denies the theft. Katerina Ivanovna is disgusted by the insult at her stepdaughter and starts raving at Luzhin and the Landlady. To prove Sonya's innocence, Katerina Ivanovna, asks her to turn out her pockets and is stunned when a 100 ruble note falls to the floor. Luzhin magnanimously agrees not to press charges against the girl, but to Luzhin's horror, Lebezyatnikov steps forwards and declares that he saw Luzhin plant the note in Sonya's pocket. Raskolnikov then steps forward and explains that Luzhin was probably looking to embarrass him for his relations with Sonya. Luzhin, plans foiled, tries to leave the apartment and maintain his innocence while insulting Raskolnikov and Lebezatnikov. After Luzhin leaves, the fight between Katerina Ivanovna and her Landlady continues, ending in the eviction of the Marmeladov family. SEO Stuff that I don't want to do lol...Welcome to this narration of Fyodor Dostoyevsky's masterpiece, bringing you another chapter of this incredible literary classic. In this literary fiction reading, we explore the depths of Russian literature as the a desperate young Russian man, Raskolnikov, plans the perfect crime - the murder of a despicable pawnbroker, an old woman who no one will mourn. It isn't just, he argues, for a man of of genius to commit a crime if it will ultimately benefit humanity. A powerful psychological study and a terrifying, thrilling murder mystery, filled with philosophical, religious and social commentary.Join me for this Crime and Punishment novel audiobook as we delve into themes of crime, social commentary, and Right and Wrong.Russian Literature, Dostoyevsky Audiobook, Classic Literature

Dans cet épisode, on part en 1941, au cœur de la Seconde Guerre mondiale, pour découvrir le travail d'un petit groupe de mathématiciens et de cryptanalystes réunis à Bletchley Park. Leur objectif : décrypter le code Enigma, réputé inviolable par l'armée nazie. Au centre de ces efforts, on retrouve Alan Turing, un mathématicien de génie considéré comme l'un des fondateurs de l'informatique et de l'intelligence artificielle. Le récit détaille son rôle clé dans la création de la « Bombe », une machine électromécanique utilisée pour analyser les innombrables combinaisons d'Enigma. On y découvre aussi la personnalité d'Alan Turing, son parcours hors du commun, ses découvertes révolutionnaires et sa vision unique du monde. Malheureusement, la fin de sa vie est marquée par le drame : il est condamné pour homosexualité et retrouvé mort à 41 ans dans des circonstances qui demeurent aujourd'hui encore mystérieuses. L'épisode met en lumière la portée de ses travaux et révèle à quel point ils ont influencé la science et la société modernes.Hébergé par Ausha. Visitez ausha.co/politique-de-confidentialite pour plus d'informations.

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This time on QBD Book Club: The Podcast, our special guest host Michael Robotham interviews Nick Croydon on his debut fiction title, "The Turing Protocol." In the midst of World War II, Enigma codebreaker Alan Turing has created a machine named Nautilus that can send a message back into the recent past. After Turing uses it to help the Allied forces succeed on D-Day, he sees the power (and potential danger) of what he has created. He knows he can only entrust it to one person: Joan, the mother of his secret child. Over the next seventy years, the Nautilus is passed down through the Turing family, who all must decide for themselves when to use this powerful invention. Will it save the world - or destroy it?Purchase your copy of “The Turing Protocol” at your local QBD Books store or online today: https://www.qbd.com.au/the-turing-protocol/nick-croydon/9781923293991/Follow along with QBD Books here: QBD Books on Facebook: www.facebook.com/qbdbooks QBD Books on Instagram: www.instagram.com/qbdbooks QBD Books on TikTok: www.tiktok.com/@qbdbooksaustralia

En este episodio de Podcast de Tecnología e Informática con Tomás González exploramos qué significa realmente “Inteligencia Artificial General” (AGI) y cómo se diferencia de la IA estrecha o narrow AI. Repasamos criterios como el Test de Turing y la medida de Legg-Hutter, analizamos qué promete la industria y qué se está logrando en los laboratorios, y debatimos si la AGI es un objetivo realista o un término inflado por el marketing. Un repaso claro y sin sensacionalismo para entender en qué punto estamos realmente.

Today's guest is Kelly Dempski, Head of Solutions for Banking, Financial Services, and Insurance (BFSI) at Turing. Turing is one of the world's fastest-growing Artificial Intelligence companies, working with the world's leading AI labs to advance frontier model capabilities and leveraging that work to build real-world AI systems that help businesses solve their toughest problems — delivering real business results, faster, smarter, and at scale. Kelly is a seasoned technology executive and innovator at Turing, where he leads the development of disruptive, business-focused Artificial Intelligence solutions for financial services clients. Over a 30-year career, he has held Managing Director roles at Accenture, Citi, and JPMorgan Chase, as well as working with smaller start-ups. In each role, he has developed solutions that leverage the latest technologies to solve real business problems. As AI becomes increasingly essential to the financial services industry, Kelly offers a grounded perspective on what really makes or breaks enterprise adoption. He explains that the greatest challenges aren't always in the AI models themselves — but in how they connect to legacy systems, regulatory requirements, and fragmented data environments. From document intelligence to client onboarding, Kelly explores the use cases where AI is already delivering measurable value — and how firms can scale that value by starting small and building momentum. Want to share your AI adoption story with executive peers? Click emerj.com/expert2 for more information and to be a potential future guest on the ‘AI in Business' podcast! This episode is sponsored by Turing. Learn how brands work with Emerj and other Emerj Media options at emerj.com/ad1.

Summer rewind: Greg Lindsay is an urban tech expert and a Senior Fellow at MIT. He's also a two-time Jeopardy champion and the only human to go undefeated against IBM's Watson. Greg joins thinkenergy to talk about how artificial intelligence (AI) is reshaping how we manage, consume, and produce energy—from personal devices to provincial grids, its rapid growth to the rising energy demand from AI itself. Listen in to learn how AI impacts our energy systems and what it means individually and industry-wide. Related links: ●       Greg Lindsay website: https://greglindsay.org/ ●       Greg Lindsay on LinkedIn: https://www.linkedin.com/in/greg-lindsay-8b16952/ ●       International Energy Agency (IEA): https://www.iea.org/ ●       Trevor Freeman on LinkedIn: https://www.linkedin.com/in/trevor-freeman-p-eng-cem-leed-ap-8b612114/ ●       Hydro Ottawa: https://hydroottawa.com/en    To subscribe using Apple Podcasts: https://podcasts.apple.com/us/podcast/thinkenergy/id1465129405   To subscribe using Spotify: https://open.spotify.com/show/7wFz7rdR8Gq3f2WOafjxpl   To subscribe on Libsyn: http://thinkenergy.libsyn.com/ --- Subscribe so you don't miss a video: https://www.youtube.com/user/hydroottawalimited   Follow along on Instagram: https://www.instagram.com/hydroottawa   Stay in the know on Facebook: https://www.facebook.com/HydroOttawa   Keep up with the posts on X: https://twitter.com/thinkenergypod --- Transcript: Trevor Freeman  00:00 Hi everyone. Well, summer is here, and the think energy team is stepping back a bit to recharge and plan out some content for the next season. We hope all of you get some much needed downtime as well, but we aren't planning on leaving you hanging over the next few months, we will be re releasing some of our favorite episodes from the past year that we think really highlight innovation, sustainability and community. These episodes highlight the changing nature of how we use and manage energy, and the investments needed to expand, modernize and strengthen our grid in response to that. All of this driven by people and our changing needs and relationship to energy as we move forward into a cleaner, more electrified future, the energy transition, as we talk about many times on this show. Thanks so much for listening, and we'll be back with all new content in September. Until then, happy listening.   Trevor Freeman  00:55 Welcome to think energy, a podcast that dives into the fast changing world of energy through conversations with industry leaders, innovators and people on the front lines of the energy transition. Join me, Trevor Freeman, as I explore the traditional, unconventional and up and coming facets of the energy industry. If you have any thoughts feedback or ideas for topics we should cover, please reach out to us at think energy at hydro ottawa.com, Hi everyone. Welcome back. Artificial intelligence, or AI, is a term that you're likely seeing and hearing everywhere today, and with good reason, the effectiveness and efficiency of today's AI, along with the ever increasing applications and use cases mean that in just the past few years, AI went from being a little bit fringe, maybe a little bit theoretical to very real and likely touching everyone's day to day lives in ways that we don't even notice, and we're just at the beginning of what looks to be a wave of many different ways that AI will shape and influence our society and our lives in the years to come. And the world of energy is no different. AI has the potential to change how we manage energy at all levels, from our individual devices and homes and businesses all the way up to our grids at the local, provincial and even national and international levels. At the same time, AI is also a massive consumer of energy, and the proliferation of AI data centers is putting pressure on utilities for more and more power at an unprecedented pace. But before we dive into all that, I also think it will be helpful to define what AI is. After all, the term isn't new. Like me, many of our listeners may have grown up hearing about Skynet from Terminator, or how from 2001 A Space Odyssey, but those malignant, almost sentient versions of AI aren't really what we're talking about here today. And to help shed some light on both what AI is as well as what it can do and how it might influence the world of energy, my guest today is Greg Lindsay, to put it in technical jargon, Greg's bio is super neat, so I do want to take time to run through it properly. Greg is a non resident Senior Fellow of MIT's future urban collectives lab Arizona State University's threat casting lab and the Atlantic Council's Scowcroft center for strategy and security. Most recently, he was a 2022-2023 urban tech Fellow at Cornell Tech's Jacobs Institute, where he explored the implications of AI and augmented reality at an urban scale. Previously, he was an urbanist in resident, which is a pretty cool title, at BMW minis urban tech accelerator, urban X, as well as the director of Applied Research at Montreal's new cities and Founding Director of Strategy at its mobility focused offshoot, co motion. He's advised such firms as Intel, Samsung, Audi, Hyundai, IKEA and Starbucks, along with numerous government entities such as 10 Downing Street, us, Department of Energy and NATO. And finally, and maybe coolest of all, Greg is also a two time Jeopardy champion and the only human to go undefeated against IBM's Watson. So on that note, Greg Lindsey, welcome to the show.   Greg Lindsay  04:14 Great to be here. Thanks for having me. Trevor,   Trevor Freeman  04:16 So Greg, we're here to talk about AI and the impacts that AI is going to have on energy, but AI is a bit of one of those buzzwords that we hear out there in a number of different spheres today. So let's start by setting the stage of what exactly we're talking about. So what do we mean when we say AI or artificial intelligence?   Speaker 1  04:37 Well, I'd say the first thing to keep in mind is that it is neither artificial nor intelligence. It's actually composites of many human hands making it. And of course, it's not truly intelligent either. I think there's at least two definitions for the layman's purposes. One is statistical machine learning. You know that is the previous generation of AI, we could say, doing deep, deep statistical analysis, looking for patterns fitting to. Patterns doing prediction. There's a great book, actually, by some ut professors at monk called prediction machines, which that was a great way of thinking about machine learning and sense of being able to do large scale prediction at scale. And that's how I imagine hydro, Ottawa and others are using this to model out network efficiencies and predictive maintenance and all these great uses. And then the newer, trendier version, of course, is large language models, your quads, your chat gpts, your others, which are based on transformer models, which is a whole series of work that many Canadians worked on, including Geoffrey Hinton and others. And this is what has produced the seemingly magical abilities to produce text and images on demand and large scale analysis. And that is the real power hungry beast that we think of as AI today.   Trevor Freeman  05:42 Right! So different types of AI. I just want to pick those apart a little bit. When you say machine learning, it's kind of being able to repetitively look at something or a set of data over and over and over again. And because it's a computer, it can do it, you know, 1000s or millions of times a second, and learn what, learn how to make decisions based on that. Is that fair to say?   Greg Lindsay  06:06 That's fair to say. And the thing about that is, is like you can train it on an output that you already know, large language models are just vomiting up large parts of pattern recognition, which, again, can feel like magic because of our own human brains doing it. But yeah, machine learning, you can, you know, you can train it to achieve outcomes. You can overfit the models where it like it's trained too much in the past, but, yeah, it's a large scale probabilistic prediction of things, which makes it so powerful for certain uses.   Trevor Freeman  06:26 Yeah, one of the neatest explanations or examples I've seen is, you know, you've got these language models where it seems like this AI, whether it's chat, DBT or whatever, is writing really well, like, you know, it's improving our writing. It's making things sound better. And it seems like it's got a brain behind it, but really, what it's doing is it's going out there saying, What have millions or billions of other people written like this? And how can I take the best things of that? And it can just do that really quickly, and it's learned that that model, so that's super helpful to understand what we're talking about here. So obviously, in your work, you look at the impact of AI on a number of different aspects of our world, our society. What we're talking about here today is particularly the impact of AI when it comes to energy. And I'd like to kind of bucketize our conversation a little bit today, and the first area I want to look at is, what will ai do when it comes to energy for the average Canadian? Let's say so in my home, in my business, how I move around? So I'll start with that. It's kind of a high level conversation. Let's start talking about the different ways that AI will impact you know that our average listener here?   Speaker 1  07:41 Um, yeah, I mean, we can get into a discussion about what it means for the average Canadian, and then also, of course, what it means for Canada in the world as well, because I just got back from South by Southwest in Austin, and, you know, for the second, third year in row, AI was on everyone's lips. But really it's the energy. Is the is the bottleneck. It's the forcing factor. Everyone talked about it, the fact that all the data centers we can get into that are going to be built in the direction of energy. So, so, yeah, energy holds the key to the puzzle there. But, um, you know, from the average gain standpoint, I mean, it's a question of, like, how will these tools actually play out, you know, inside of the companies that are using this, right? And that was a whole other discussion too. It's like, okay, we've been playing around with these tools for two, three years now, what do they actually use to deliver value of your large language model? So I've been saying this for 10 years. If you look at the older stuff you could start with, like smart thermostats, even look at the potential savings of this, of basically using machine learning to optimize, you know, grid optimize patterns of usage, understanding, you know, the ebbs and flows of the grid, and being able to, you know, basically send instructions back and forth. So you know there's stats. You know that, basically you know that you know you could save 10 to 25% of electricity bills. You know, based on this, you could reduce your heating bills by 10 to 15% again, it's basically using this at very large scales of the scale of hydro Ottawa, bigger, to understand this sort of pattern usage. But even then, like understanding like how weather forecasts change, and pulling that data back in to basically make fine tuning adjustments to the thermostats and things like that. So that's one stands out. And then, you know, we can think about longer term. I mean, yeah, lots have been lots has been done on imagining, like electric mobility, of course, huge in Canada, and what that's done to sort of change the overall energy mix virtual power plants. This is something that I've studied, and we've been writing about at Fast Company. At Fast Company beyond for 20 years, imagining not just, you know, the ability to basically, you know, feed renewable electricity back into the grid from people's solar or from whatever sources they have there, but the ability of utilities to basically go in and fine tune, to have that sort of demand shaping as well. And then I think the most interesting stuff, at least in demos, and also blockchain, which has had many theoretical uses, and I've got to see a real one. But one of the best theoretical ones was being able to create neighborhood scale utilities. Basically my cul de sac could have one, and we could trade clean electrons off of our solar panels through our batteries and home scale batteries, using Blockchain to basically balance this out. Yeah, so there's lots of potential, but yeah, it comes back to the notion of people want cheaper utility bills. I did this piece 10 years ago for the Atlantic Council on this we looked at a multi country survey, and the only reason anybody wanted a smart home, which they just were completely skeptical about, was to get those cheaper utility bills. So people pay for that.   Trevor Freeman  10:19 I think it's an important thing to remember, obviously, especially for like the nerds like me, who part of my driver is, I like that cool new tech. I like that thing that I can play with and see my data. But for most people, no matter what we're talking about here, when it comes to that next technology, the goal is make my life a little bit easier, give me more time or whatever, and make things cheaper. And I think especially in the energy space, people aren't putting solar panels on their roof because it looks great. And, yeah, maybe people do think it looks great, but they're putting it up there because they want cheaper electricity. And it's going to be the same when it comes to batteries. You know, there's that add on of resiliency and reliability, but at the end of the day, yeah, I want my bill to be cheaper. And what I'm hearing from you is some of the things we've already seen, like smart thermostats get better as AI gets better. Is that fair to say?   Greg Lindsay  11:12 Well, yeah, on the machine learning side, that you know, you get ever larger data points. This is why data is the coin of the realm. This is why there's a race to collect data on everything. Is why every business model is data collection and everything. Because, yes, not only can they get better, but of course, you know, you compile enough and eventually start finding statistical inferences you never meant to look for. And this is why I've been involved. Just as a side note, for example, of cities that have tried to implement their own data collection of electric scooters and eventually electric vehicles so they could understand these kinds of patterns, it's really the key to anything. And so it's that efficiency throughput which raises some really interesting philosophical questions, particularly about AI like, this is the whole discussion on deep seek. Like, if you make the models more efficient, do you have a Jevons paradox, which is the paradox of, like, the more energy you save through efficiency, the more you consume because you've made it cheaper. So what does this mean that you know that Canadian energy consumption is likely to go up the cleaner and cheaper the electrons get. It's one of those bedeviling sort of functions.   Trevor Freeman  12:06 Yeah interesting. That's definitely an interesting way of looking at it. And you referenced this earlier, and I will talk about this. But at the macro level, the amount of energy needed for these, you know, AI data centers in order to do all this stuff is, you know, we're seeing that explode.   Greg Lindsay  12:22 Yeah, I don't know that. Canadian statistics my fingertips, but I brought this up at Fast Company, like, you know, the IEA, I think International Energy Agency, you know, reported a 4.3% growth in the global electricity grid last year, and it's gonna be 4% this year. That does not sound like much. That is the equivalent of Japan. We're adding in Japan every year to the grid for at least the next two to three years. Wow. And that, you know, that's global South, air conditioning and other needs here too, but that the data centers on top is like the tip of the spear. It's changed all this consumption behavior, where now we're seeing mothballed coal plants and new plants and Three Mile Island come back online, as this race for locking up electrons, for, you know, the race to build God basically, the number of people in AI who think they're literally going to build weekly godlike intelligences, they'll, they won't stop at any expense. And so they will buy as much energy as they can get.   Trevor Freeman  13:09 Yeah, well, we'll get to that kind of grid side of things in a minute. Let's stay at the home first. So when I look at my house, we talked about smart thermostats. We're seeing more and more automation when it comes to our homes. You know, we can program our lights and our door locks and all this kind of stuff. What does ai do in order to make sure that stuff is contributing to efficiency? So I want to do all those fun things, but use the least amount of energy possible.   Greg Lindsay  13:38 Well, you know, I mean, there's, again, there's various metrics there to basically, sort of, you know, program your lights. And, you know, Nest is, you know, Google. Nest is an example of this one, too, in terms of basically learning your ebb and flow and then figuring out how to optimize it over the course of the day. So you can do that, you know, we've seen, again, like the home level. We've seen not only the growth in solar panels, but also in those sort of home battery integration. I was looking up that Tesla Powerwall was doing just great in Canada, until the last couple of months. I assume so, but I it's been, it's been heartening to see that, yeah, this sort of embrace of home energy integration, and so being able to level out, like, peak flow off the grid, so Right? Like being able to basically, at moments of peak demand, to basically draw on your own local resources and reduce that overall strain. So there's been interesting stuff there. But I want to focus for a moment on, like, terms of thinking about new uses. Because, you know, again, going back to how AI will influence the home and automation. You know, Jensen Wong of Nvidia has talked about how this will be the year of robotics. Google, Gemini just applied their models to robotics. There's startups like figure there's, again, Tesla with their optimists, and, yeah, there's a whole strain of thought that we're about to see, like home robotics, perhaps a dream from like, the 50s. I think this is a very Disney World esque Epcot Center, yeah, with this idea of jetsy, yeah, of having home robots doing work. You can see concept videos a figure like doing the actual vacuuming. I mean, we invented Roombas to this, but, but it also, I, you know, I've done a lot of work. Our own thinking around electric delivery vehicles. We could talk a lot about drones. We could talk a lot about the little robots that deliver meals on the sidewalk. There's a lot of money in business models about increasing access and people needing to maybe move less, to drive and do all these trips to bring it to them. And that's a form of home automation, and that's all batteries. That is all stuff off the grid too. So AI is that enable those things, these things that can think and move and fly and do stuff and do services on your behalf, and so people might find this huge new source of demand from that as well.   Trevor Freeman  15:29 Yeah, that's I hadn't really thought about the idea that all the all these sort of conveniences and being able to summon them to our homes cause us to move around less, which also impacts transportation, which is another area I kind of want to get to. And I know you've, you've talked a little bit about E mobility, so where do you see that going? And then, how does AI accelerate that transition, or accelerate things happening in that space?   Greg Lindsay  15:56 Yeah, I mean, I again, obviously the EV revolutions here Canada like, one of the epicenters Canada, Norway there, you know, that still has the vehicle rebates and things. So, yeah. I mean, we've seen, I'm here in Montreal, I think we've got, like, you know, 30 to 13% of sales is there, and we've got our 2035, mandate. So, yeah. I mean, you see this push, obviously, to harness all of Canada's clean, mostly hydro electricity, to do this, and, you know, reduce its dependence on fossil fuels for either, you know, Climate Change Politics reasons, but also just, you know, variable energy prices. So all of that matters. But, you know, I think the key to, like the electric mobility revolution, again, is, is how it's going to merge with AI and it's, you know, it's not going to just be the autonomous, self driving car, which is sort of like the horseless carriage of autonomy. It's gonna be all this other stuff, you know. My friend Dan Hill was in China, and he was thinking about like, electric scooters, you know. And I mentioned this to hydro Ottawa, like, the electric scooter is one of the leading causes of how we've taken internal combustion engine vehicles offline across the world, mostly in China, and put people on clean electric motors. What happens when you take those and you make those autonomous, and you do it with, like, deep seek and some cameras, and you sort of weld it all together so you could have a world of a lot more stuff in motion, and not just this world where we have to drive as much. And that, to me, is really exciting, because that changes, like urban patterns, development patterns, changes how you move around life, those kinds of things as well. That's that might be a little farther out, but, but, yeah, this sort of like this big push to build out domestic battery industries, to build charging points and the sort of infrastructure there, I think it's going to go in direction, but it doesn't look anything like, you know, a sedan or an SUV that just happens to be electric.   Trevor Freeman  17:33 I think that's a the step change is change the drive train of the existing vehicles we have, you know, an internal combustion to a battery. The exponential change is exactly what you're saying. It's rethinking this.   Greg Lindsay  17:47 Yeah, Ramesam and others have pointed out, I mean, again, like this, you know, it's, it's really funny to see this pushback on EVs, you know. I mean, I love a good, good roar of an internal combustion engine myself, but, but like, you know, Ramesam was an energy analyst, has pointed out that, like, you know, EVS were more cost competitive with ice cars in 2018 that's like, nearly a decade ago. And yeah, the efficiency of electric motors, particularly regenerative braking and everything, it just blows the cost curves away of ice though they will become the equivalent of keeping a thorough brat around your house kind of thing. Yeah, so, so yeah, it's just, it's that overall efficiency of the drive train. And that's the to me, the interesting thing about both electric motors, again, of autonomy is like, those are general purpose technologies. They get cheaper and smaller as they evolve under Moore's Law and other various laws, and so they get to apply to more and more stuff.   Trevor Freeman  18:32 Yeah. And then when you think about once, we kind of figure that out, and we're kind of already there, or close to it, if not already there, then it's opening the door to those other things you're talking about. Of, well, do we, does everybody need to have that car in their driveway? Are we rethinking how we're actually just doing transportation in general? And do we need a delivery truck? Or can it be delivery scooter? Or what does that look like?   Greg Lindsay  18:54 Well, we had a lot of those discussions for a long time, particularly in the mobility space, right? Like, and like ride hailing, you know, like, oh, you know, that was always the big pitch of an Uber is, you know, your car's parked in your driveway, like 94% of the time. You know, what happens if you're able to have no mobility? Well, we've had 15 years of Uber and these kinds of services, and we still have as many cars. But people are also taking this for mobility. It's additive. And I raised this question, this notion of like, it's just sort of more and more, more options, more availability, more access. Because the same thing seems to be going on with energy now too. You know, listeners been following along, like the conversation in Houston, you know, a week or two ago at Sarah week, like it's the whole notion of energy realism. And, you know, there's the new book out, more is more is more, which is all about the fact that we've never had an energy transition. We just kept piling up. Like the world burned more biomass last year than it did in 1900 it burned more coal last year than it did at the peak of coal. Like these ages don't really end. They just become this sort of strata as we keep piling energy up on top of it. And you know, I'm trying to sound the alarm that we won't have an energy transition. What that means for climate change? But similar thing, it's. This rebound effect, the Jevons paradox, named after Robert Stanley Jevons in his book The question of coal, where he noted the fact that, like, England was going to need more and more coal. So it's a sobering thought. But, like, I mean, you know, it's a glass half full, half empty in many ways, because the half full is like increasing technological options, increasing changes in lifestyle. You can live various ways you want, but, but, yeah, it's like, I don't know if any of it ever really goes away. We just get more and more stuff,   Trevor Freeman  20:22 Exactly, well. And, you know, to hear you talk about the robotics side of things, you know, looking at the home, yeah, more, definitely more. Okay, so we talked about kind of home automation. We've talked about transportation, how we get around. What about energy management? And I think about this at the we'll talk about the utility side again in a little bit. But, you know, at my house, or for my own personal use in my life, what is the role of, like, sort of machine learning and AI, when it comes to just helping me manage my own energy better and make better decisions when it comes to energy? ,   Greg Lindsay  20:57 Yeah, I mean, this is where it like comes in again. And you know, I'm less and less of an expert here, but I've been following this sort of discourse evolve. And right? It's the idea of, you know, yeah, create, create. This the set of tools in your home, whether it's solar panels or batteries or, you know, or Two Way Direct, bi directional to the grid, however it works. And, yeah, and people, you know, given this option of savings, and perhaps, you know, other marketing messages there to curtail behavior. You know? I mean, I think the short answer the question is, like, it's an app people want, an app that tell them basically how to increase the efficiency of their house or how to do this. And I should note that like, this has like been the this is the long term insight when it comes to like energy and the clean tech revolution. Like my Emery Levin says this great line, which I've always loved, which is, people don't want energy. They want hot showers and cold beer. And, you know, how do you, how do you deliver those things through any combination of sticks and carrots, basically like that. So, So, hence, why? Like, again, like, you know, you know, power walls, you know, and, and, and, you know, other sort of AI controlled batteries here that basically just sort of smooth out to create the sort of optimal flow of electrons into your house, whether that's coming drive directly off the grid or whether it's coming out of your backup and then recharging that the time, you know, I mean, the surveys show, like, more than half of Canadians are interested in this stuff, you know, they don't really know. I've got one set here, like, yeah, 61% are interested in home energy tech, but only 27 understand, 27% understand how to optimize them. So, yeah. So people need, I think, perhaps, more help in handing that over. And obviously, what's exciting for the, you know, the utility level is, like, you know, again, aggregate all that individual behavior together and you get more models that, hope you sort of model this out, you know, at both greater scale and ever more fine grained granularity there. So, yeah, exactly. So I think it's really interesting, you know, I don't know, like, you know, people have gamified it. What was it? I think I saw, like, what is it? The affordability fund trust tried to basically gamify AI energy apps, and it created various savings there. But a lot of this is gonna be like, as a combination like UX design and incentives design and offering this to people too, about, like, why you should want this and money's one reason, but maybe there's others.   Trevor Freeman  22:56 Yeah, and we talk about in kind of the utility sphere, we talk about how customers, they don't want all the data, and then have to go make their own decisions. They want those decisions to be made for them, and they want to say, look, I want to have you tell me the best rate plan to be on. I want to have you automatically switch me to the best rate plan when my consumption patterns change and my behavior chat patterns change. That doesn't exist today, but sort of that fast decision making that AI brings will let that become a reality sometime in the future,   Greg Lindsay  23:29 And also in theory, this is where LLMs come into play. Is like, you know, to me, what excites me the most about that is the first time, like having a true natural language interface, like having being able to converse with an, you know, an AI, let's hopefully not chat bot. I think we're moving out on chat bots, but some sort of sort of instantiation of an AI to be like, what plan should I be on? Can you tell me what my behavior is here and actually having some sort of real language conversation with it? Not decision trees, not event statements, not chat bots.   Trevor Freeman  23:54 Yeah, absolutely. Okay, so we've kind of teased around this idea of looking at the utility levels, obviously, at hydro Ottawa, you referenced this just a minute ago. We look at all these individual cases, every home that has home automation or solar storage, and we want to aggregate that and understand what, what can we do to help manage the grid, help manage all these new energy needs, shift things around. So let's talk a little bit about the role that AI can play at the utility scale in helping us manage the grid.   Greg Lindsay  24:28 All right? Well, yeah, there's couple ways to approach it. So one, of course, is like, let's go back to, like, smart meters, right? Like, and this is where I don't know how many hydro Ottawa has, but I think, like, BC Hydro has like, 2 million of them, sometimes they get politicized, because, again, this gets back to this question of, like, just, just how much nanny state you want. But, you know, you know, when you reach the millions, like, yeah, you're able to get that sort of, you know, obviously real time, real time usage, real time understanding. And again, if you can do that sort of grid management piece where you can then push back, it's visual game changer. But, but yeah. I mean, you know, yeah, be. See hydro is pulling in. I think I read like, like, basically 200 million data points a day. So that's a lot to train various models on. And, you know, I don't know exactly the kind of savings they have, but you can imagine there, whether it's, you know, them, or Toronto Hydro, or hydro Ottawa and others creating all these monitoring points. And again, this is the thing that bedells me, by the way, just philosophically about modern life, the notion of like, but I don't want you to be collecting data off me at all times, but look at what you can do if you do It's that constant push pull of some sort of combination of privacy and agency, and then just the notion of like statistics, but, but there you are, but, but, yeah, but at the grid level, then I mean, like, yeah. I mean, you can sort of do the same thing where, like, you know, I mean, predictive maintenance is the obvious one, right? I have been writing about this for large enterprise software companies for 20 years, about building these data points, modeling out the lifetime of various important pieces equipment, making sure you replace them before you have downtime and terrible things happen. I mean, as we're as we're discussing this, look at poor Heathrow Airport. I am so glad I'm not flying today, electrical substation blowing out two days of the world's most important hub offline. So that's where predictive maintenance comes in from there. And, yeah, I mean, I, you know, I again, you know, modeling out, you know, energy flow to prevent grid outages, whether that's, you know, the ice storm here in Quebec a couple years ago. What was that? April 23 I think it was, yeah, coming up in two years. Or our last ice storm, we're not the big one, but that one, you know, where we had big downtime across the grid, like basically monitoring that and then I think the other big one for AI is like, Yeah, is this, this notion of having some sort of decision support as well, too, and sense of, you know, providing scenarios and modeling out at scale the potential of it? And I don't think, I don't know about this in a grid case, but the most interesting piece I wrote for Fast Company 20 years ago was an example, ago was an example of this, which was a fledgling air taxi startup, but they were combining an agent based model, so using primitive AI to create simple rules for individual agents and build a model of how they would behave, which you can create much more complex models. Now we could talk about agents and then marrying that to this kind of predictive maintenance and operations piece, and marrying the two together. And at that point, you could have a company that didn't exist, but that could basically model itself in real time every day in the life of what it is. You can create millions and millions and millions of Monte Carlo operations. And I think that's where perhaps both sides of AI come together truly like the large language models and agents, and then the predictive machine learning. And you could basically hydro or others, could build this sort of deep time machine where you can model out all of these scenarios, millions and millions of years worth, to understand how it flows and contingencies as well. And that's where it sort of comes up. So basically something happens. And like, not only do you have a set of plans, you have an AI that has done a million sets of these plans, and can imagine potential next steps of this, or where to deploy resources. And I think in general, that's like the most powerful use of this, going back to prediction machines and just being able to really model time in a way that we've never had that capability before. And so you probably imagine the use is better than I.   Trevor Freeman  27:58 Oh man, it's super fascinating, and it's timely. We've gone through the last little while at hydro Ottawa, an exercise of updating our playbook for emergencies. So when there are outages, what kind of outage? What's the sort of, what are the trigger points to go from, you know, what we call a level one to a level two to level three. But all of this is sort of like people hours that are going into that, and we're thinking through these scenarios, and we've got a handful of them, and you're just kind of making me think, well, yeah, what if we were able to model that out? And you bring up this concept of agents, let's tease into that a little bit explain what you mean when you're talking about agents.   Greg Lindsay  28:36 Yeah, so agentic systems, as the term of art is, AI instantiations that have some level of autonomy. And the archetypal example of this is the Stanford Smallville experiment, where they took basically a dozen large language models and they gave it an architecture where they could give it a little bit of backstory, ruminate on it, basically reflect, think, decide, and then act. And in this case, they used it to plan a Valentine's Day party. So they played out real time, and the LLM agents, like, even played matchmaker. They organized the party, they sent out invitations, they did these sorts of things. Was very cute. They put it out open source, and like, three weeks later, another team of researchers basically put them to work writing software programs. So you can see they organized their own workflow. They made their own decisions. There was a CTO. They fact check their own work. And this is evolving into this grand vision of, like, 1000s, millions of agents, just like, just like you spin up today an instance of Amazon Web Services to, like, host something in the cloud. You're going to spin up an agent Nvidia has talked about doing with healthcare and others. So again, coming back to like, the energy implications of that, because it changes the whole pattern. Instead of huge training runs requiring giant data centers. You know, it's these agents who are making all these calls and doing more stuff at the edge, but, um, but yeah, in this case, it's the notion of, you know, what can you put the agents to work doing? And I bring this up again, back to, like, predictive maintenance, or for hydro Ottawa, there's another amazing paper called virtual in real life. And I chatted with one of the principal authors. It created. A half dozen agents who could play tour guide, who could direct you to a coffee shop, who do these sorts of things, but they weren't doing it in a virtual world. They were doing it in the real one. And to do it in the real world, you took the agent, you gave them a machine vision capability, so added that model so they could recognize objects, and then you set them loose inside a digital twin of the world, in this case, something very simple, Google Street View. And so in the paper, they could go into like New York Central Park, and they could count every park bench and every waste bin and do it in seconds and be 99% accurate. And so agents were monitoring the landscape. Everything's up, because you can imagine this in the real world too, that we're going to have all the time. AIS roaming the world, roaming these virtual maps, these digital twins that we build for them and constantly refresh from them, from camera data, from sensor data, from other stuff, and tell us what this is. And again, to me, it's really exciting, because that's finally like an operating system for the internet of things that makes sense, that's not so hardwired that you can ask agents, can you go out and look for this for me? Can you report back on this vital system for me? And they will be able to hook into all of these kinds of representations of real time data where they're emerging from, and give you aggregated reports on this one. And so, you know, I think we have more visibility in real time into the real world than we've ever had before.   Trevor Freeman  31:13 Yeah, I want to, I want to connect a few dots here for our listeners. So bear with me for a second. Greg. So for our listeners, there was a podcast episode we did about a year ago on our grid modernization roadmap, and we talked about one of the things we're doing with grid modernization at hydro Ottawa and utilities everywhere doing this is increasing the sensor data from our grid. So we're, you know, right now, we've got visibility sort of to our station level, sometimes one level down to some switches. But in the future, we'll have sensors everywhere on our grid, every switch, every device on our grid, will have a sensor gathering data. Obviously, you know, like you said earlier, millions and hundreds of millions of data points every second coming in. No human can kind of make decisions on that, and what you're describing is, so now we've got all this data points, we've got a network of information out there, and you could create this agent to say, Okay, you are. You're my transformer agent. Go out there and have a look at the run temperature of every transformer on the network, and tell me where the anomalies are, which ones are running a half a degree or two degrees warmer than they should be, and report back. And now I know hydro Ottawa, that the controller, the person sitting in the room, knows, Hey, we should probably go roll a truck and check on that transformer, because maybe it's getting end of life. Maybe it's about to go and you can do that across the entire grid. That's really fascinating,   Greg Lindsay  32:41 And it's really powerful, because, I mean, again, these conversations 20 years ago at IoT, you know you're going to have statistical triggers, and you would aggregate these data coming off this, and there was a lot of discussion there, but it was still very, like hardwired, and still very Yeah, I mean, I mean very probabilistic, I guess, for a word that went with agents like, yeah, you've now created an actual thing that can watch those numbers and they can aggregate from other systems. I mean, lots, lots of potential there hasn't quite been realized, but it's really exciting stuff. And this is, of course, where that whole direction of the industry is flowing. It's on everyone's lips, agents.   Trevor Freeman  33:12 Yeah. Another term you mentioned just a little bit ago that I want you to explain is a digital twin. So tell us what a digital twin is.   Greg Lindsay  33:20 So a digital twin is, well, the matrix. Perhaps you could say something like this for listeners of a certain age, but the digital twin is the idea of creating a model of a piece of equipment, of a city, of the world, of a system. And it is, importantly, it's physics based. It's ideally meant to represent and capture the real time performance of the physical object it's based on, and in this digital representation, when something happens in the physical incarnation of it, it triggers a corresponding change in state in the digital twin, and then vice versa. In theory, you know, you could have feedback loops, again, a lot of IoT stuff here, if you make changes virtually, you know, perhaps it would cause a change in behavior of the system or equipment, and the scales can change from, you know, factory equipment. Siemens, for example, does a lot of digital twin work on this. You know, SAP, big, big software companies have thought about this. But the really crazy stuff is, like, what Nvidia is proposing. So first they started with a digital twin. They very modestly called earth two, where they were going to model all the weather and climate systems of the planet down to like the block level. There's a great demo of like Jensen Wong walking you through a hurricane, typhoons striking the Taipei, 101, and how, how the wind currents are affecting the various buildings there, and how they would change that more recently, what Nvidia is doing now is, but they just at their big tech investor day, they just partner with General Motors and others to basically do autonomous cars. And what's crucial about it, they're going to train all those autonomous vehicles in an NVIDIA built digital twin in a matrix that will act, that will be populated by agents that will act like people, people ish, and they will be able to run millions of years of autonomous vehicle training in this and this is how they plan to catch up to. Waymo or, you know, if Tesla's robotaxis are ever real kind of thing, you know, Waymo built hardwired like trained on real world streets, and that's why they can only operate in certain operating domain environments. Nvidia is gambling that with large language models and transformer models combined with digital twins, you can do these huge leapfrog effects where you can basically train all sorts of synthetic agents in real world behavior that you have modeled inside the machine. So again, that's the kind, that's exactly the kind of, you know, environment that you're going to train, you know, your your grid of the future on for modeling out all your contingency scenarios.   Trevor Freeman  35:31 Yeah, again, you know, for to bring this to the to our context, a couple of years ago, we had our the direcco. It's a big, massive windstorm that was one of the most damaging storms that we've had in Ottawa's history, and we've made some improvements since then, and we've actually had some great performance since then. Imagine if we could model that derecho hitting our grid from a couple different directions and figure out, well, which lines are more vulnerable to wind speeds, which lines are more vulnerable to flying debris and trees, and then go address that and do something with that, without having to wait for that storm to hit. You know, once in a decade or longer, the other use case that we've talked about on this one is just modeling what's happening underground. So, you know, in an urban environments like Ottawa, like Montreal, where you are, there's tons of infrastructure under the ground, sewer pipes, water pipes, gas lines, electrical lines, and every time the city wants to go and dig up a road and replace that road, replace that sewer, they have to know what's underground. We want to know what's underground there, because our infrastructure is under there. As the electric utility. Imagine if you had a model where you can it's not just a map. You can actually see what's happening underground and determine what makes sense to go where, and model out these different scenarios of if we underground this line or that line there. So lots of interesting things when it comes to a digital twin. The digital twin and Agent combination is really interesting as well, and setting those agents loose on a model that they can play with and understand and learn from. So talk a little bit about.   Greg Lindsay  37:11 that. Yeah. Well, there's a couple interesting implications just the underground, you know, equipment there. One is interesting because in addition to, like, you know, you know, having captured that data through mapping and other stuff there, and having agents that could talk about it. So, you know, next you can imagine, you know, I've done some work with augmented reality XR. This is sort of what we're seeing again, you know, meta Orion has shown off their concept. Google's brought back Android XR. Meta Ray Bans are kind of an example of this. But that's where this data will come from, right? It's gonna be people wearing these wearables in the world, capturing all this camera data and others that's gonna be fed into these digital twins to refresh them. Meta has a particularly scary demo where you know where you the user, the wearer leaves their keys on their coffee table and asks metas, AI, where their coffee where their keys are, and it knows where they are. It tells them and goes back and shows them some data about it. I'm like, well, to do that, meta has to have a complete have a complete real time map of your entire house. What could go wrong. And that's what all these companies aspire to of reality. So, but yeah, you can imagine, you know, you can imagine a worker. And I've worked with a startup out of urban X, a Canada startup, Canadian startup called context steer. And you know, is the idea of having real time instructions and knowledge manuals available to workers, particularly predictive maintenance workers and line workers. So you can imagine a technician dispatched to deal with this cut in the pavement and being able to see with XR and overlay of like, what's actually under there from the digital twin, having an AI basically interface with what's sort of the work order, and basically be your assistant that can help you walk you through it, in case, you know, you run into some sort of complication there, hopefully that won't be, you know, become like, turn, turn by turn, directions for life that gets into, like, some of the questions about what we wanted out of our workforce. But there's some really interesting combinations of those things, of like, you know, yeah, mapping a world for AIS, ais that can understand it, that could ask questions in it, that can go probe it, that can give you advice on what to do in it. All those things are very close for good and for bad.   Trevor Freeman  39:03 You kind of touched on my next question here is, how do we make sure this is all in the for good or mostly in the for good category, and not the for bad category you talk in one of the papers that you wrote about, you know, AI and augmented reality in particular, really expanding the attack surface for malicious actors. So we're creating more opportunities for whatever the case may be, if it's hacking or if it's malware, or if it's just, you know, people that are up to nefarious things. How do we protect against that? How do we make sure that our systems are safe that the users of our system. So in our case, our customers, their data is safe, their the grid is safe. How do we make sure that?   Greg Lindsay  39:49 Well, the very short version is, whatever we're spending on cybersecurity, we're not spending enough. And honestly, like everybody who is no longer learning to code, because we can be a quad or ChatGPT to do it, I. Is probably there should be a whole campaign to repurpose a big chunk of tech workers into cybersecurity, into locking down these systems, into training ethical systems. There's a lot of work to be done there. But yeah, that's been the theme for you know that I've seen for 10 years. So that paper I mentioned about sort of smart homes, the Internet of Things, and why people would want a smart home? Well, yeah, the reason people were skeptical is because they saw it as basically a giant attack vector. My favorite saying about this is, is, there's a famous Arthur C Clarke quote that you know, any sufficiently advanced technology is magic Tobias Ravel, who works at Arup now does their head of foresight has this great line, any sufficiently advanced hacking will feel like a haunting meaning. If you're in a smart home that's been hacked, it will feel like you're living in a haunted house. Lights will flicker on and off, and systems will turn and go haywire. It'll be like you're living with a possessed house. And that's true of cities or any other systems. So we need to do a lot of work on just sort of like locking that down and securing that data, and that is, you know, we identified, then it has to go all the way up and down the supply chain, like you have to make sure that there is, you know, a chain of custody going back to when components are made, because a lot of the attacks on nest, for example. I mean, you want to take over a Google nest, take it off the wall and screw the back out of it, which is a good thing. It's not that many people are prying open our thermostats, but yeah, if you can get your hands on it, you can do a lot of these systems, and you can do it earlier in the supply chain and sorts of infected pieces and things. So there's a lot to be done there. And then, yeah, and then, yeah, and then there's just a question of, you know, making sure that the AIs are ethically trained and reinforced. And, you know, a few people want to listeners, want to scare themselves. You can go out and read some of the stuff leaking out of anthropic and others and make clot of, you know, models that are trying to hide their own alignments and trying to, like, basically copy themselves. Again, I don't believe that anything things are alive or intelligent, but they exhibit these behaviors as part of the probabilistic that's kind of scary. So there's a lot to be done there. But yeah, we worked on this, the group that I do foresight with Arizona State University threat casting lab. We've done some work for the Secret Service and for NATO and, yeah, there'll be, you know, large scale hackings on infrastructure. Basically the equivalent can be the equivalent can be the equivalent to a weapons of mass destruction attack. We saw how Russia targeted in 2014 the Ukrainian grid and hacked their nuclear plans. This is essential infrastructure more important than ever, giving global geopolitics say the least, so that needs to be under consideration. And I don't know, did I scare you enough yet? What are the things we've talked through here that, say the least about, you know, people being, you know, tricked and incepted by their AI girlfriends, boyfriends. You know people who are trying to AI companions. I can't possibly imagine what could go wrong there.   Trevor Freeman  42:29 I mean, it's just like, you know, I don't know if this is 15 or 20, or maybe even 25 years ago now, like, it requires a whole new level of understanding when we went from a completely analog world to a digital world and living online, and people, I would hope, to some degree, learned to be skeptical of things on the internet and learned that this is that next level. We now need to learn the right way of interacting with this stuff. And as you mentioned, building the sort of ethical code and ethical guidelines into these language models into the AI. Learning is pretty critical for our listeners. We do have a podcast episode on cybersecurity. I encourage you to go listen to it and reassure yourself that, yes, we are thinking about this stuff. And thanks, Greg, you've given us lots more to think about in that area as well. When it comes to again, looking back at utilities and managing the grid, one thing we're going to see, and we've talked a lot about this on the show, is a lot more distributed generation. So we're, you know, the days of just the central, large scale generation, long transmission lines that being the only generation on the grid. Those days are ending. We're going to see more distributed generations, solar panels on roofs, batteries. How does AI help a utility manage those better, interact with those better get more value out of those things?   Greg Lindsay  43:51 I guess that's sort of like an extension of some of the trends I was talking about earlier, which is the notion of, like, being able to model complex systems. I mean, that's effectively it, right, like you've got an increasingly complex grid with complex interplays between it, you know, figuring out how to basically based on real world performance, based on what you're able to determine about where there are correlations and codependencies in the grid, where point where choke points could emerge, where overloading could happen, and then, yeah, basically, sort of building that predictive system to Basically, sort of look for what kind of complex emergent behavior comes out of as you keep adding to it and and, you know, not just, you know, based on, you know, real world behavior, but being able to dial that up to 11, so to speak, and sort of imagine sort of these scenarios, or imagine, you know, what, what sort of long term scenarios look like in terms of, like, what the mix, how the mix changes, how the geography changes, all those sorts of things. So, yeah, I don't know how that plays out in the short term there, but it's this combination, like I'm imagining, you know, all these different components playing SimCity for real, if one will.   Trevor Freeman  44:50 And being able to do it millions and millions and millions of times in a row, to learn every possible iteration and every possible thing that might happen. Very cool. Okay. So last kind of area I want to touch on you did mention this at the beginning is the the overall power implications of of AI, of these massive data centers, obviously, at the utility, that's something we are all too keenly aware of. You know, the stat that that I find really interesting is a normal Google Search compared to, let's call it a chat GPT search. That chat GPT search, or decision making, requires 10 times the amount of energy as that just normal, you know, Google Search looking out from a database. Do you see this trend? I don't know if it's a trend. Do you see this continuing like AI is just going to use more power to do its decision making, or will we start to see more efficiencies there? And the data centers will get better at doing what they do with less energy. What is the what does the future look like in that sector?   Greg Lindsay  45:55 All the above. It's more, is more, is more! Is the trend, as far as I can see, and every decision maker who's involved in it. And again, Jensen Wong brought this up at the big Nvidia Conference. That basically he sees the only constraint on this continuing is availability of energy supplies keep it going and South by Southwest. And in some other conversations I've had with bandwidth companies, telcos, like laying 20 lumen technologies, United States is laying 20,000 new miles of fiber optic cables. They've bought 10% of Corning's total fiber optic output for the next couple of years. And their customers are the hyperscalers. They're, they're and they're rewiring the grid. That's why, I think it's interesting. This has something, of course, for thinking about utilities, is, you know, the point to point Internet of packet switching and like laying down these big fiber routes, which is why all the big data centers United States, the majority of them, are in north of them are in Northern Virginia, is because it goes back to the network hub there. Well, lumen is now wiring this like basically this giant fabric, this patchwork, which can connect data center to data center, and AI to AI and cloud to cloud, and creating this entirely new environment of how they are all directly connected to each other through some of this dedicated fiber. And so you can see how this whole pattern is changing. And you know, the same people are telling me that, like, yeah, the where they're going to build this fiber, which they wouldn't tell me exactly where, because it's very tradable, proprietary information, but, um, but it's following the energy supplies. It's following the energy corridors to the American Southwest, where there's solar and wind in Texas, where you can get natural gas, where you can get all these things. It will follow there. And I of course, assume the same is true in Canada as we build out our own sovereign data center capacity for this. So even, like deep seek, for example, you know, which is, of course, the hyper efficient Chinese model that spooked the markets back in January. Like, what do you mean? We don't need a trillion dollars in capex? Well, everyone's quite confident, including again, Jensen Wong and everybody else that, yeah, the more efficient models will increase this usage. That Jevons paradox will play out once again, and we'll see ever more of it. To me, the question is, is like as how it changes? And of course, you know, you know, this is a bubble. Let's, let's, let's be clear, data centers are a bubble, just like railroads in 1840 were a bubble. And there will be a bust, like not everyone's investments will pencil out that infrastructure will remain maybe it'll get cheaper. We find new uses for it, but it will, it will eventually bust at some point and that's what, to me, is interesting about like deep seeking, more efficient models. Is who's going to make the wrong investments in the wrong places at the wrong time? But you know, we will see as it gathers force and agents, as I mentioned. You know, they don't require, as much, you know, these monstrous training runs at City sized data centers. You know, meta wanted to spend $200 billion on a single complex, the open AI, Microsoft, Stargate, $500 billion Oracle's. Larry Ellison said that $100 billion is table stakes, which is just crazy to think about. And, you know, he's permitting three nukes on site. So there you go. I mean, it'll be fascinating to see if we have a new generation of private, private generation, right, like, which is like harkening all the way back to, you know, the early electrical grid and companies creating their own power plants on site, kind of stuff. Nicholas Carr wrote a good book about that one, about how we could see from the early electrical grid how the cloud played out. They played out very similarly. The AI cloud seems to be playing out a bit differently. So, so, yeah, I imagine that as well, but, but, yeah, well, inference happen at the edge. We need to have more distributed generation, because you're gonna have AI agents that are going to be spending more time at the point of request, whether that's a laptop or your phone or a light post or your autonomous vehicle, and it's going to need more of that generation and charging at the edge. That, to me, is the really interesting question. Like, you know, when these current generation models hit their limits, and just like with Moore's law, like, you know, you have to figure out other efficiencies in designing chips or designing AIS, how will that change the relationship to the grid? And I don't think anyone knows quite for sure yet, which is why they're just racing to lock up as many long term contracts as they possibly can just get it all, core to the market.   Trevor Freeman  49:39 Yeah, it's just another example, something that comes up in a lot of different topics that we cover on this show. Everything, obviously, is always related to the energy transition. But the idea that the energy transition is really it's not just changing fuel sources, like we talked about earlier. It's not just going from internal combustion to a battery. It's rethinking the. Relationship with energy, and it's rethinking how we do things. And, yeah, you bring up, like, more private, massive generation to deal with these things. So really, that whole relationship with energy is on scale to change. Greg, this has been a really interesting conversation. I really appreciate it. Lots to pack into this short bit of time here. We always kind of wrap up our conversations with a series of questions to our guests. So I'm going to fire those at you here. And this first one, I'm sure you've got lots of different examples here, so feel free to give more than one. What is a book that you've read that you think everybody should read?   Greg Lindsay  50:35 The first one that comes to mind is actually William Gibson's Neuromancer, which is which gave the world the notion of cyberspace and so many concepts. But I think about it a lot today. William Gibson, Vancouver based author, about how much in that book is something really think about. There is a digital twin in it, an agent called the Dixie flatline. It's like a former program where they cloned a digital twin of him. I've actually met an engineering company, Thornton Thomas Eddie that built a digital twin of one of their former top experts. So like that became real. Of course, the matrix is becoming real the Turing police. Yeah, there's a whole thing in there where there's cops to make sure that AIS don't get smarter. I've been thinking a lot about, do we need Turing police? The EU will probably create them. And so that's something where you know the proof, again, of like science fiction, its ability in world building to really make you think about these implications and help for contingency planning. A lot of foresight experts I work with think about sci fi, and we use sci fi for exactly that reason. So go read some classic cyberpunk, everybody.   Trevor Freeman  51:32 Awesome. So same question. But what's a movie or a show that you think everybody should take a look at?   Greg Lindsay  51:38 I recently watched the watch the matrix with ideas, which is fun to think about, where the villains are, agents that villains are agents. That's funny how that terms come back around. But the other one was thinking about the New Yorker recently read a piece on global demographics and the fact that, you know, globally, less and less children. And it made several references to Alfonso Quons, Children of Men from 2006 which is, sadly, probably the most prescient film of the 21st Century. Again, a classic to watch, about imagining in a world where we don't where you where you lose faith in the future, what happens, and a world that is not having children as a world that's losing faith in its own future. So that's always haunted me.   Trevor Freeman  52:12 It's funny both of those movies. So I've got kids as they get, you know, a little bit older, a little bit older, we start introducing more and more movies. And I've got this list of movies that are just, you know, impactful for my own adolescent years and growing up. And both matrix and Children of Men are on that list of really good movies that I just need my kids to get a little bit older, and then I'm excited to watch with them. If someone offered you a free round trip flight anywhere in the world, where would you go?   Greg Lindsay  52:40 I would go to Venice, Italy for the Architecture Biennale, which I will be on a plane in May, going to anyway. And the theme this year is intelligence, artificial, natural and collective. So it should be interesting to see the world's brightest architects. Let's see what we got. But yeah, Venice, every time, my favorite city in the world.   Trevor Freeman  52:58 Yeah, it's pretty wonderful. Who is someone that you admire?   Greg Lindsay  53:01 Great question.

The Arrival of GPT-5: What You Need to Know It's said to handle complex problems better than any predecessor. Could this be the AI that finally passes the Turing test?Try AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustle/about

Do Democrats and Republicans really misunderstand each other as much as we think?This week, we dive into a surprising new experiment that puts that idea to the test — literally. Psychologist and researcher Adam Mastriani created a kind of “political Turing test,” asking people to write persuasive statements from the perspective of the opposite political party. Then, he tested whether others could tell the real from the fake. The results? Most people couldn't.We unpack what this means for our understanding of polarization, partisan animosity, and political identity. Is the problem really misunderstanding — or something deeper? Are partisans more empathetic than we give them credit for? Or are they just really good at writing what they think others want to hear?We also explore the experiment's implications for political science research, theory-building, and the broader sociology of science.

Primeramente sería interesante habla de la definición de inteligencia y una explicación podría ser: “La inteligencia se define como la capacidad de entender, comprender y aplicar conocimientos, razonar, resolver problemas y adaptarse al entorno.” Tener en cuenta que en todos los mitos creacionistas siempre que se le da al hombre el alito de vida se le da la inteligencia. En el Génesis, Dios sopla en la nariz de Adán y este se convierte en un “alma viviente”. Pero ese aliento no es solo oxígeno...es inteligencia, capacidad de nombrar las cosas, de distinguir, de elegir. Si eso es inteligencia ¿ la IA que es? Una definición que nos dan es; Disciplina científica que se ocupa de crear programas informáticos que ejecutan operaciones comparables a las que realiza la mente humana. Como el aprendizaje y el razonamiento lógico.Pero hay dos capacidades críticas que siguen siendo exclusivas de los seres humanos: la auténtica invención y la creatividad, por eso la verdadera AGI todavía no la tenemos ( Me refiero al comun de los mortales, los dueños del cortijo no lo sabemos) Otra cosa bien distinta es el conocimiento, que puede ha veces también traer problemas, como veremos más adelante. Y siempre un conocimiento superior da ventaja a aquel que lo posee sobre el que no. El Poder, detrás del velo de la IA y de la Agenda Transhumanista. No es una simple mejora tecnológica, sino un proyecto milenario y oscuro para someter a la humanidad a un control total, utilizando la programación mental y los rituales como herramientas fundamentales para transformar a los humanos en seres dóciles y previsibles. El transhumanismo no busca mejorar a la humanidad, sino "restar", es decir, crear una infrahumanidad funcional y dócil. En lugar de sumar, el proyecto consistiría en una especie de ingeniería inversa: tomar lo mejor del ser humano (la inteligencia colectiva, la creatividad, el libre albedrío) y limitarlo para crear un "esclavo más eficaz". Es un proyecto de dos caras. Mientras una élite minoritaria se convierte en suprahumanidad (eugenismo), la mayoría de la población es degenerada a una condición de infrahumanidad (disgenismo), convirtiéndose en una especie de zombis que alimentan el sistema con su energía vital. Robotización del humano frente a humanización del robot. la cuestión crucial no es la humanización del robot (es decir, hacer que los robots parezcan humanos), sino la robotización del humano. Esta robotización no significa parecerse a una máquina, sino comportarse de una manera "inhumana": sin libertad, sin sentimientos, sin creatividad, sin preguntarse por el sentido de la vida. Hablar de IA es hablar de una tecnología clave y disruptiva que va a alterar numerosos aspectos de nuestras vidas. Pero hay que dejar algo claro: esta tecnología está siendo manejada y dosificada por el poder real, los verdaderos amos del mundo. No sabemos quiénes son, pero sí sabemos que no son las marionetas que nos ponen en escena. Sabemos, eso sí, que son los mismos que manejan la liquidez del sistema monetario internacional, creando ciclos de crisis y orden de los que siempre sacan provecho. Existe un poder oculto, también conocido como “Estado profundo”, que canaliza de forma importante los flujos emocionales y energéticos de la sociedad. Utilizan técnicas ancestrales y conocimientos transmitidos entre iniciados, que se centran, entre otras cosas, en conseguir el control social de la forma más práctica y económica posible para ellos. A la vista de todos, usan a líderes y estrellas como arquetipos para los no iniciados, desde presidentes hasta directivos de grandes empresas. Al mismo tiempo, en el "obscenario" y apartados de la vista de todos, realizan otros rituales donde lo sincronizan todo según sus creencias, donde siempre hablan de la LUZ, aquella que robó Prometeo y mirar el infierno que le tocó padecer después de adquirir el conocimiento que los dioses no querían que tuviera. Lo que es crucial entender es que estas tecnologías tan relevantes están siendo planificadas, manejadas y dosificadas desde el mismo centro del poder. Nos hacen creer que hay diferentes empresas que compiten entre sí por el mercado, pero esa disputa escenificada no es real. Todas trabajan para los mismos amos, con un objetivo ya marcado: avanzar en su agenda a medida que la sociedad normaliza sus ideas. Este es el primer punto clave: la relación entre la IA y el transhumanismo. Debemos abordar este concepto porque la fusión hombre-máquina es uno de los objetivos de la agenda de control. Buscan crear un tipo de “ser híbrido”, sin alma y fácil de controlar. Un futuro distópico que podría cumplirse si no nos oponemos a ello. El momento crucial para la consecución de sus objetivos sería cuando, una vez normalizado el asunto, consigan fabricar "humanos" mediante vientres artificiales. Con esto, tendrían solucionado el tema del control social. Podrían hacer "impresiones de andróginos" en la medida que los necesiten, igual que emiten el dinero que quieren. Serían personas desarraigadas, sin familia, sin descendencia y quizás incluso sin genitales. Su inteligencia estaría genéticamente limitada según la tarea que deban desempeñar. Podrían incluso crear emisiones especiales de híbridos con electrónica insertada en su organismo, conectados a redes y potenciados en sus capacidades, lo que supondría un grado de control inimaginable. Pero esperemos que la sociedad reaccione y evite estos peligros. Ahora que hemos enmarcado la situación, es hora de pasar a los orígenes de la inteligencia artificial. La historia oficial nos suele presentar estos hitos como hechos aislados, para que no entendamos las conexiones ni miremos debajo de la alfombra. Pero si descodificamos la historia, veremos que la idea de la IA no es un invento reciente, sino que tiene raíces profundas en la filosofía, los mitos y las tradiciones ancestrales. La história podría empezar perfectamente con la historia de Adán y Eva y su expulsión del paraiso por la adquisición del conocimiento prohibido."Si comes de este fruto, serás como Dios. Conocerás el bien y el mal." Dijo la serpiente y Adan y eva al igual que Prometeo también acabaron castigados por Dios. En Génesis 3:22, se dice: “Ahora el hombre ha venido a ser como uno de nosotros, al conocer el bien y el mal. Que no extienda ahora su mano y tome también del árbol de la vida, y coma, y viva para siempre…”. ¿Qué tipo de conocimiento adquiere Eva? No se trata de conocimiento técnico o científico, sino algo más profundo y existencial: Conocimiento moral: la capacidad de distinguir entre el bien y el mal. Autoconciencia: al instante, Eva (y luego Adán) se da cuenta de que está desnuda. Esto implica vergüenza, intimidad, juicio propio. Libre albedrío: al desobedecer, ejerce una elección. Ya no es solo criatura, sino agente. Comer del fruto es adquirir la conciencia humana tal como la entendemos hoy: saber que uno existe, que puede decidir, y que sus actos tienen consecuencias. Las manzanas envenenadas🧙‍♀️ Blancanieves📖 Eva en el Edén🧪 Alan Turing (leyenda urbana) Se dice que murió tras comer una manzana envenenada con cianuro, aunque no está confirmado. Curiosamente, el logo de Apple con una manzana mordida ha alimentado esta teoría. 🎯 Guillermo Tell Obligado a disparar con una ballesta a una manzana colocada sobre la cabeza de su hijo. Aunque no estaba envenenada, el riesgo era mortal. Podríamos hablar de Talos el primer "androide" de la historia.Talos era un gigante de bronce, forjado por Hefesto (el dios herrero) por encargo de Zeus o de Minos, rey de Creta (según la versión). Tenía una única vena que recorría su cuerpo entero, sellada con un clavo o perno de bronce en el tobillo. En lugar de sangre, su cuerpo contenía "icor", el fluido vital de los dioses. En la edad media en el siglo XIIl el trabajo de Ramón Llull, conocido como el Ars Magna, puede considerarse un precursor conceptual de la inteligencia artificial. Mas adelante si hay tiempo hablaremos de él. En la alquimia, la idea de fabricar un "hombre artificial" o homúnculo fue explorada por figuras como Paracelso, quien describió procedimientos para su creación. Similarmente, los alquimistas islámicos investigaron el concepto de takwin, la creación artificial de vida. En Praga en el siglo XVI los judios hablan del Golem, criatura sin alma creada para servir al hombre. hecha de barro o arcilla y animada mediante palabras sagradas, la inserción de nombres divinos. Con el advenimiento del siglo XIX, estas visiones se trasladaron al ámbito de la ficción literaria. Obras como "Frankenstein" de Mary Shelley exploraron las implicaciones éticas de crear seres conscientes, mientras que "R.U.R." (Rossum's Universal Robots) de Karel Čapek, publicada en 1920, introdujo el término "robot" al léxico global, marcando un punto de inflexión en la conceptualización de las máquinas con capacidad de trabajo autónomo. Y aquí en este punto es cuando entramos en los años 50 son considerados el punto de partida oficial de la inteligencia artificial moderna. Aunque hubo ideas previas, fue en esa década cuando la IA empezó a tomar forma como disciplina científica. Aquí te dejo los hitos clave: 🔹 1950: Alan Turing y su famosa prueba Publica "Computing Machinery and Intelligence" y propone el Test de Turing, una forma de evaluar si una máquina puede pensar como un humano. 🔹 1956: Conferencia de Dartmouth Organizada por John McCarthy, quien acuñó el término "Inteligencia Artificial". Reunió a pioneros como Marvin Minsky y Claude Shannon para discutir cómo crear máquinas inteligentes. 🔹 Primeros modelos y algoritmos Se desarrollan los primeros perceptrones (redes neuronales simples) por Frank Rosenblatt en 1958. Se crean programas capaces de jugar a las damas o resolver teoremas matemáticos. 🧪 Aunque los avances eran limitados por la tecnología de la época, estos años sentaron las bases para todo lo que vino después: aprendizaje automático, redes neuronales profundas, procesamiento de lenguaje natural… ¡y hasta Hoy! Para empezar este recorrido, es fundamental detenernos en la figura de Ramon Llull, un filósofo, teólogo y cortesano del siglo XIII. Nacimiento: 1232, Palma de Mallorca, España. Fallecimiento: 1316, en el Mediterráneo. El trabajo de Llull, conocido como el Ars Magna, puede considerarse un precursor conceptual de la inteligencia artificial. Pero no es un conocimiento que se le ocurriera de la nada. Como buen cortesano y perteneciente a una buena familia, Llull era conocedor de saberes ancestrales recogidos en otras tradiciones, que simplemente se expresan de otra manera. La relación entre su trabajo y la IA moderna se basa en varios puntos clave. Para empezar, la mecanización del razonamiento. El Ars Magna partía de la premisa de que el razonamiento y la verdad podían descomponerse en principios básicos. Llull representaba estos conceptos con letras y los organizaba en figuras geométricas como círculos concéntricos que podían ser girados. El objetivo era combinar estos principios de forma sistemática para generar proposiciones lógicamente válidas, demostrando verdades de forma infalible. Esta idea de un sistema mecánico que genera conocimiento de forma automática a partir de reglas definidas es la base de los sistemas computacionales y de la IA. Es lo que podríamos llamar una "máquina lógica". En este sentido, la conexión de Llull con la Cábala y la gematría es evidente. El Ars Magna se basa en un sistema simbólico donde las letras tienen un significado profundo. Su método de combinar principios es comparable a las técnicas cabalísticas de gematría (la interpretación numérica de las letras) y la combinación de las letras del alfabeto hebreo para obtener conocimientos ocultos. La idea subyacente es la misma: que la verdad y la sabiduría están codificadas en los símbolos y pueden ser reveladas a través de su manipulación sistemática. Podríamos decir que Llull inventó el primer "hardware" de pensamiento simbólico, aunque su "software" fuera más filosófico que informático. Mecanización del razonamiento: El Ars Magna partía de la premisa de que el razonamiento y la verdad podían ser descompuestos en principios básicos. Llull representaba estos conceptos con letras y los organizaba en figuras geométricas (discos giratorios). El objetivo era combinar estos principios de forma sistemática para generar proposiciones lógicas válidas. Esta idea de un sistema mecánico que, a partir de reglas y principios definidos, genera conocimiento de forma automática, es la base de los sistemas computacionales y la IA moderna. Los Dignidades de Dios o Principios Absolutos: Representados por letras de la B a la K, Llull consideraba que estos eran atributos divinos universales y perfectos. Son: B - Bondad C - Grandeza D - Eternidad E - Poder F - Sabiduría G - Voluntad H - Virtud I - Verdad K - Gloria Principios Relativos: Estos conceptos representaban relaciones entre los principios absolutos y se usaban para generar proposiciones lógicas. Incluyen: Diferencia Concordancia Contrariedad Principio Medio Fin Mayoridad Igualdad Minoridad Al combinar estos principios de forma mecánica, Llull creía que se podía demostrar cualquier verdad de manera infalible, creando así la primera "máquina de pensar" de la historia. El concepto de combinatoria: La obra de Llull se fundamenta en el arte de la combinatoria, explorando todas las relaciones lógicas posibles entre los conceptos a través del movimiento de sus discos. Este enfoque es un antecedente directo de la computación y la IA, donde los algoritmos y programas informáticos no son más que un conjunto de instrucciones que combinan datos y operaciones de manera sistemática para resolver problemas. Lenguaje y símbolos artificiales: Llull creó un alfabeto artificial de nueve letras para representar y manipular conceptos. De manera similar, la IA se construye sobre lenguajes de programación, que son sistemas simbólicos con reglas precisas diseñados para que las máquinas puedan procesar información y ejecutar operaciones de forma estructurada. En resumen, aunque el Ars Magna no era una computadora en el sentido moderno, la visión de Llull de que el pensamiento podía ser mecanizado y manipulado a través de un sistema de símbolos y reglas combinatorias es un antecedente directo de los principios que rigen la inteligencia artificial. De hecho, su influencia fue reconocida por figuras posteriores como el filósofo y matemático Gottfried Leibniz, quien también se considera un pionero de la computación. Podríamos decir que Llull inventó el primer "hardware" de pensamiento simbólico, aunque su "software" fuera más filosófico que informático. La gracia de la historia es que él quería convencer a herejes y, sin saberlo, sentó las bases para que hoy una IA te esté respondiendo Tanto la Cábala como el Ars Magna se basan en un sistema simbólico donde las letras y los números tienen un significado profundo. El método de Llull para combinar sus principios es comparable a las técnicas cabalísticas de gematría (interpretación numérica de las letras) y la combinación de las letras del alfabeto hebreo para obtener conocimientos ocultos. La idea subyacente es que la verdad y la sabiduría están codificadas en los símbolos y pueden ser reveladas a través de su manipulación sistemática. En resumen podemos entrever que Llull como buen cortesano y perteneciente a una buena familia era conocedor de conocimientos ancestrales recogidos en otras tradiciones y simplemente se expresan de otra manera. ………………………………………………………………………………………. Cleón la contracción entre clon y eón. Un eón es una unidad de tiempo geológico de escala extremadamente larga, utilizada para dividir la historia de la Tierra en los períodos más amplios. Representa miles de millones de años y es la división más grande en la escala de tiempo geológico, por encima de las eras, períodos, épocas y edades. Por ejemplo, la historia de la Tierra se divide en cuatro eones principales algunos de más de 2000 millones de años. Vivimos actualmente en el Fanerozoico que se traduce como "vida visible" o "vida evidente". Este término fue acuñado para describir el eón geológico que comenzó hace aproximadamente 541 millones de años Reglamento Europeo sobre Inteligencia Artificial (LA LEY 16665/2024) casualmente tiene un 666 ………………………………………………………………………………………. Hector, el webmaster del hilo rojo decía en un reciente programa sobre Palantir: “Palantir es el panóptico del siglo XXI. El ojo del gran hermano de Orwell.” El panoptico es un tipo de diseño que nos permite controlar un amplio espacio desde un único punto sin ser detectados. Se trata de la garita del vigilante en medio de la prisión, de la torre de control. El filósofo utilitarista Jeremy Bentham fue su diseñador, Hector nos mostró como este señor era también masón y estaba relacionado al mas alto nivel. Les dejaremos el enlace al video en la descripción del podcast. En un anterior programa de enero donde en el mismo canal analizaron que es Palantir comprobaba yo para preparar este podcast que TODAS las fuentes que presentaba @ElHiloRojoTV en su video de enero de 2025 habian DESAPARECIDO. Estamos hablando de artículos publicados en Forbes, The Guardian, Bloomberg, Financial Times, CNBC o incluso el propio MIT. En todos los casos el enlace original ha desaparecido, ojo, no digo que haya desaparecido el artículo en sí, pero han cortado el enlace original. Leo textualmente de una descripción del Hilo rojo sobre lo que es Palantir añadiendo yo algunas cosas: Creada en 2003 con el apoyo de In-Q-Tel, el fondo de inversión de la CIA, Palantir se diseñó para procesar grandes cantidades de información y hacer conexiones invisibles para el ojo humano. Su fundador, Peter Thiel, también cofundador de PayPal, ha estado siempre en la intersección entre tecnología, poder y vigilancia global. Palantir no solo analiza datos, sino que los fusiona en tiempo real: redes sociales, correos electrónicos, transacciones bancarias y hasta movimientos físicos. ¿Os acordáis del PNR? Pues esto es aún mucho más intrusivo ya que muchísimas organizaciones y países han acordado compartir sus bases de datos con Palantir. ¿Qué significa esto? Una red de control total, donde cada actividad queda registrada, permitiendo a gobiernos y corporaciones predecir comportamientos y tomar decisiones basadas en modelos algorítmicos. Palantir es utilizada por el Pentágono, la NSA, la CIA, el FBI, Interpol y gobiernos europeos y latinoamericanos. También lo utilizan grandes empresas como bancos o multinacionales. Sus herramientas se usan en operaciones antiterroristas, control de fronteras, vigilancia de ciudadanos y hasta persecución de disidentes políticos. ¿Hasta qué punto este nivel de vigilancia está transformando las democracias en estados de control absoluto? Su software ha sido utilizado en conflictos como la guerra en Ucrania, ayudando a identificar objetivos estratégicos y a procesar inteligencia en tiempo real. Palantir convierte el Big Data en un arma de guerra: soldados equipados con dispositivos conectados a su red pueden recibir información detallada sobre el enemigo en segundos. ¿Estamos entrando en una era donde la guerra es digital antes que física? A medida que más gobiernos y empresas adoptan Palantir, los límites entre seguridad, privacidad y control social se vuelven borrosos. ¿Es Palantir una herramienta para el bien o el paso final hacia una sociedad hipervigilada? ………………………………………………………………………………………. Los origenes de Palantir están en la Oficina de Conciencia de la Información ( IAO ) fue establecida por la Agencia de Proyectos de Investigación Avanzada de Defensa de los Estados Unidos (DARPA) en enero de 2002 para reunir varios proyectos de DARPA centrados en la aplicación de la vigilancia y la tecnología de la información para rastrear y monitorear a terroristas y otras amenazas asimétricas a la seguridad nacional de los Estados Unidos mediante el logro de " Conciencia de la Información Total “ o en inglés "Total Information Awareness" (TIA). Sí. La TIA, la agencia secreta para la que trabajaban Mortadelo y Filemón ha existido. La IAO se creó después de que el almirante John Poindexter , ex asesor de seguridad nacional de los Estados Unidos del presidente Ronald Reagan , y el ejecutivo de SAIC Brian Hicks se acercaran al Departamento de Defensa de los EE. UU . con la idea de un programa de concientización sobre la información después de los ataques del 11 de septiembre de 2001. Querían reunir la mayor cantidad de información de la historia. Leemos en la wikipedia: “El 2 de agosto de 2002, Poindexter dio un discurso en DARPAtech 2002 titulado "Descripción general de la Oficina de Concienciación sobre la Información" en el que describió el programa TIA. Además del programa en sí, la participación de Poindexter como director de la IAO también generó inquietud entre algunos, ya que había sido condenado anteriormente por mentir al Congreso y alterar y destruir documentos relacionados con el caso Irán-Contra , aunque esas condenas fueron posteriormente revocadas con el argumento de que el testimonio utilizado en su contra estaba protegido.” ¿A que se dedicaba esta agencia? Veamos lo que nos dice la wikipedia: “Se logró mediante la creación de enormes bases de datos informáticas para recopilar y almacenar la información personal de todos los residentes de Estados Unidos, incluyendo correos electrónicos personales, redes sociales, registros de tarjetas de crédito, llamadas telefónicas, historiales médicos y muchas otras fuentes, sin necesidad de una orden de registro. La información se analizaba posteriormente para detectar actividades sospechosas, conexiones entre individuos y "amenazas". El programa también incluía financiación para tecnologías de vigilancia biométrica que permitieran identificar y rastrear a personas mediante cámaras de vigilancia y otros métodos. Tras las críticas públicas de que el desarrollo y la implementación de la tecnología podrían llevar a un sistema de vigilancia masiva, el Congreso retiró la financiación de la IAO en 2003. Sin embargo, varios proyectos de la IAO siguieron financiándose bajo nombres diferentes, como reveló Edward Snowden durante las revelaciones de vigilancia masiva de 2013.” El pionero y más relevante programa de predictividad subjetiva fue la Strategic Subject List (SSL) elaborada en el año 2012 por el Instituto de Tecnología de Illinois, adoptado por la Policía de Chicago desde el año 2012. Ya en el año 2017, el conjunto de datos incluía a 398.684 personas. Han existido programas predictivos como Programa de Delincuentes Crónicos (2011-2019, PredPol y Operación LÁSER (Extracción y Restauración Estratégica en Los Ángeles), estos dos polémicos programas predictivos empleados durante una década por la Policía de Los Ángeles (LAPD), ahora ya desactivados por la cantidad de abusos y fallos cometidos. Dice Luis Lafont en su tesis “La policía predictiva más allá de Minority Report”: “Las empresas que desarrollan programas predictivos se escudan con frecuencia en el secreto comercial para no revelar los criterios que se siguen en la elaboración del algoritmo y evitar que otras compañías puedan copiar el software. Ello determina que los sistemas predictivos siguen de forma mayoritaria un modelo de caja negra que no explica al público en general ni a los usuarios los argumentos y razonamientos detrás de la predicción, en particular por quienes deben aplicarlas.” También en Europa, los sistemas predictivos de vigilancia policial se utilizan para anticipar y prevenir delitos mediante el análisis de datos. En Francia, el Analyst Notebook (i2AN) se emplea para combatir estructuras criminales y terrorismo, conectando personas y crímenes. En Italia, KeyCrime predice atracos analizando características de sospechosos y modus operandi. En los Países Bajos, el CAS identifica áreas de riesgo delictivo mediante mapas espacio-temporales. En Alemania, PRECOBS, SKALA y KIMPRO predicen la repetición de crímenes usando patrones geográficos. En el Reino Unido, Predpol, Gang Matrix y HART se centran en puntos calientes, bandas y reiteración criminal. En España, el P3-DSS (Predictive Police Patrolling) genera mapas de puntos calientes para optimizar patrullajes, EuroCop Pred-Crime apoya la predicción de delitos, y VioGen se enfoca en prevenir la violencia de género, mientras que un programa de la Guardia Civil predice incendios forestales mediante perfiles psicosociológicos. ………………………………………………………………………………………. Resumen ejecutivo de Palantir 22 de febrero de 2017 RESUMEN EJECUTIVO Palantir es la plataforma analítica líder en el mercado, utilizada a nivel estratégico, operativo y táctico en el gobierno de EE. UU. Nuestros clientes abarcan las comunidades de inteligencia, defensa y aplicación de la ley. Al combinar un potente backend con una interfaz intuitiva, Palantir le permite ejecutar sus misiones de lucha contra el terrorismo, el narcotráfico, la contrainteligencia y la contraproliferación con mayor rapidez y menos recursos. La plataforma integrada de Palantir está disponible hoy mismo y a un costo mucho menor al de un conjunto de capacidades comparable. DATOS CLAVE • Palantir es la plataforma analítica líder del mercado para CI (Contrainteligencia) , CT (Contraterrorismo), CN (Antinarcóticos) y CP (Prevención del Crimen), y actualmente se implementa en elementos de las comunidades de defensa, inteligencia y aplicación de la ley, incluyendo SOCOM (Comando de Operaciones Especiales), DIA (Agencia de Inteligencia de Defensa), CIA (Agencia Central de Inteligencia) y JIEDDO (Organización Conjunta para la Derrota de Dispositivos Explosivos Improvisados). • Palantir está listo para implementarse en su red hoy mismo. Está aprobado para JWICS (Sistema Conjunto de Comunicaciones de Inteligencia Mundial), SIPRNet (Red de Protocolo de Internet Secreta) y CWE (Entorno de Trabajo Colaborativo). • Nuestra plataforma 100 % abierta significa que Palantir se integrará a la perfección con todos sus sistemas heredados, actuales y futuros. • Con Palantir, los operadores pueden descubrir y explorar posibles conexiones utilizando cualquier tipo de información relacionada con cualquier persona, lugar o evento en su entorno analítico. Ya sea que los datos provengan de una computadora portátil en el campo, una base de datos de la sede central u otra agencia, Palantir monitorea todas las fuentes de datos de una empresa, en todos los dominios de seguridad y niveles de clasificación, para cualquier información relacionada con una entidad conocida (persona, vehículo, dispositivo de comunicación, etc.), lugar o amenaza que exista en el entorno de un operador. Desde el principio, Palantir se diseñó con la colaboración de operadores y analistas de la IC (Comunidad de Inteligencia) y el DoD (Departamento de Defensa). Sus valiosos comentarios han permitido la creación de un producto que permite a los usuarios realizar más trabajo en menos tiempo, a la vez que proporciona un mayor nivel de análisis. Palantir está diseñado para colaborar eficazmente con una red de otros usuarios, incluyendo aquellos desplegados en misiones avanzadas. Palantir se diseñó desde cero para hacer posible este tipo de solución empresarial distribuida. Palantir viene configurado con el modelo de seguridad más sofisticado del mercado. SATISFACIENDO SUS NECESIDADES DE MISIÓN. Palantir es la plataforma analítica empresarial líder a nivel mundial, que permite un entorno analítico seguro donde analistas, operadores y combatientes pueden aprovechar distintos tipos de datos de múltiples INT (Fuentes de Inteligencia. Diferentes tipos de fuentes de inteligencia, como SIGINT (inteligencia de señales), HUMINT (inteligencia humana), GEOINT (inteligencia geoespacial), etc.), a la vez que comparten sus flujos de trabajo y descubrimientos para generar conocimiento a lo largo del tiempo. Palantir reúne de forma segura datos de tráfico de mensajes, bases de datos, informes de campo, hojas de cálculo, documentos de Word, archivos XML y prácticamente cualquier otro formato, lo que permite a los usuarios organizar los datos en conocimiento y establecer conexiones vitales. Palantir Technologies comprende los desafíos únicos que enfrentan sus usuarios. Esto incluye la necesidad de descubrir grandes volúmenes de datos, colaborar y compartir información controlada, así como la necesidad de gestionar múltiples fuentes de datos dispares y garantizar la continuidad de la información en todas las rotaciones. PLATAFORMA ABIERTA • Diseñado desde su inicio para integrarse con todos los sistemas heredados, actuales y futuros • Las APIs (Interfaces de Programación de Aplicaciones) abiertas y el modelo de datos flexible de Palantir le permiten personalizar y ampliar Palantir de forma fácil y sin gastos adicionales • Importe datos en cualquier formato: bases de datos, medios confiscados, correos electrónicos, Excel, Word, PowerPoint, html, texto, csv, xml, pdf y más • Funciona con herramientas existentes, incluyendo: extractores de entidades, kits de herramientas de PNL (Procesamiento del Lenguaje Natural), análisis de redes sociales, herramientas geoespaciales o de análisis de enlaces BÚSQUEDA Y DESCUBRIMIENTO • Capacidad de búsqueda integrada en tiempo real contra fuentes de datos definidas por el usuario • Busque entidades, eventos, documentos, tráfico de mensajes, basura de bolsillo, enlaces y rutas • Descubra cómo se relacionan, conectan y conectan en red las entidades • Explore las redes conceptualmente • Desarrolle y extraiga patrones de entidad/objetivo de referencia a través del análisis de patrones • Soporte completo para contenido y búsqueda en idiomas extranjeros • Establezca y guarde parámetros de búsqueda para avisar proactivamente al usuario sobre nueva información a medida que esté disponible HERRAMIENTAS ANALÍTICAS • Analice sus datos en el ámbito relacional, temporal y geoespacial dominios • Se integra con todas las aplicaciones GIS (Sistema de Información Geográfica), incluyendo ESRI (Empresa líder en software de sistemas de información geográfica, conocida por productos como ArcGIS), Google Earth, WebTAS (Sistema de Análisis de Línea de Tiempo basado en la Web) y muchas más • Funciona con sus sistemas analíticos de imágenes y video, incluyendo su metraje UAV (Vehículo Aéreo No Tripulado, o sea los drones). • Realice búsquedas geográficas, comprenda cómo se ven geoespacialmente los datos y la inteligencia • Averigüe por qué las cosas están sucediendo donde están Vea y edite expedientes virtuales detallados que muestran relaciones, propiedades, historiales, imágenes, videos, basura de bolsillo y más. • Averigüe dónde van a suceder a continuación • Comprenda cómo se relacionan los eventos a lo largo del tiempo y cómo se relacionan las entidades con los eventos • Identifique y aproveche patrones para el análisis predictivo • Realice análisis de redes sociales (SNA) (Análisis de Redes Sociales) y enlaces • Exporte resultados analíticos con información completa de abastecimiento • Ensamble presentaciones y paquetes de segmentación/casos automáticamente COLABORACIÓN • La colaboración ha sido parte del producto desde el inicio • Los usuarios pueden compartir datos, shoeboxes, carpetas, filtros e investigaciones, todo sujeto a control de acceso • Construya redes más rápido, comprenda la superposición, haga un seguimiento de los cambios en todos los datos y suposiciones • Identifique y forme comunidades de interés ad hoc • Identifique fácilmente las brechas de recopilación CONTROL DE ACCESO Y SEGURIDAD EXTENSIVOS • Admite descubrimiento abierto: el sistema identifica otros datos relevantes existentes asociados con la consulta de los usuarios • Admite descubrimiento cerrado: el sistema puede restringir el descubrimiento a los usuarios, protegiendo así las fuentes y los métodos confidenciales y mitigando los riesgos de CI • Con el modelo de control de acceso de Palantir, la información confidencial se puede compartimentar y asegurar COMPROMETIDOS A SUPERAR SUS EXPECTATIVAS Somos una empresa de productos. Ofrecemos el mejor producto del mercado al mejor valor. Respaldamos el producto. Una inversión en Palantir es todo incluido. Cuando compra nuestro producto, obtiene todo lo que podría necesitar para que Palantir trabaje para usted, incluyendo capacitación, soporte e infraestructura escalable que cumpla con sus requisitos técnicos. ESCALA • Palantir está diseñado para escalar de forma rentable. Cree rápidamente conocimiento y estructura a partir del tráfico de mensajes. • Maneja fácilmente cientos de millones de entidades, eventos y documentos. INFORMACIÓN TÉCNICA BÁSICA • Interoperabilidad mediante SOAP y servicios web • Implementable en la web • Funciona con conexiones satelitales o de bajo ancho de banda • Funciona sin conectividad mediante resincronizaciones periódicas. Cumple con SOA (Arquitectura Orientada a Servicios) • Escalable en hardware estándar CAPACITACIÓN • Palantir ofrece una serie de videos de capacitación específicos para cada cliente y misión, lo que permite una capacitación oportuna y un fácil acceso a material de actualización • Palantir es la aplicación más fácil de usar en esta categoría. Un día de capacitación es todo lo que se necesita; entendemos que tiene un trabajo que hacer • Palantir impartirá capacitación en cualquier lugar del mundo donde nos necesite. La capacitación está incluida con el producto MANTENIMIENTO/SOPORTE • No se requiere personal especial ni gastos generales excesivos • Soporte y servicio a demanda para unidades desplegadas en el frente, 24/7/365, sin costo adicional • Soporte reconocido y centrado en la misión: si nos necesita, Palantir estará con usted en cualquier lugar del mundo, en cualquier momento. Los registros analíticos detallados permiten a los analistas ver visualmente las líneas de investigación en las que están trabajando y regresar a cualquier posición anterior. A continuación, se describen en español las abreviaturas mencionadas en el texto proporcionado, en el contexto del resumen ejecutivo de la web de Palantir en 2017: CI: Counterintelligence (Contrainteligencia). Se refiere a actividades destinadas a prevenir, detectar y neutralizar acciones de inteligencia hostiles por parte de adversarios. CT: Counterterrorism (Contraterrorismo). Actividades y operaciones enfocadas en prevenir, disuadir y responder a actos de terrorismo. CN: Counternarcotics (Antinarcóticos). Esfuerzos para combatir el tráfico y la producción de drogas ilícitas. CP: Crime Prevention (Prevención del Crimen). Estrategias y acciones para prevenir actividades delictivas. SOCOM: Special Operations Command (Comando de Operaciones Especiales). Unidad militar de los Estados Unidos que supervisa operaciones especiales. DIA: Defense Intelligence Agency (Agencia de Inteligencia de Defensa). Agencia del Departamento de Defensa de EE. UU. encargada de proporcionar inteligencia militar. CIA: Central Intelligence Agency (Agencia Central de Inteligencia). Agencia de inteligencia de EE. UU. responsable de la recopilación, análisis y difusión de inteligencia extranjera. JIEDDO: Joint Improvised Explosive Device Defeat Organization (Organización Conjunta para la Derrota de Dispositivos Explosivos Improvisados). Entidad enfocada en combatir la amenaza de dispositivos explosivos improvisados. JWICS: Joint Worldwide Intelligence Communications System (Sistema Conjunto de Comunicaciones de Inteligencia Mundial). Red segura utilizada por el gobierno de EE. UU. para transmitir información clasificada. SIPRNet: Secret Internet Protocol Router Network (Red de Protocolo de Internet Secreta). Red segura del Departamento de Defensa de EE. UU. para datos clasificados hasta nivel secreto. CWE: Collaborative Working Environment (Entorno de Trabajo Colaborativo). Plataforma o sistema que facilita la colaboración entre usuarios en un entorno seguro. IC: Intelligence Community (Comunidad de Inteligencia). Conjunto de agencias y organizaciones gubernamentales de EE. UU. que recopilan y analizan inteligencia. DoD: Department of Defense (Departamento de Defensa). Departamento del gobierno de EE. UU. responsable de la seguridad militar. INTs: Intelligence Sources (Fuentes de Inteligencia). Diferentes tipos de fuentes de inteligencia, como SIGINT (inteligencia de señales), HUMINT (inteligencia humana), GEOINT (inteligencia geoespacial), etc. APIs: Application Programming Interfaces (Interfaces de Programación de Aplicaciones). Conjunto de definiciones y herramientas que permiten la integración y comunicación entre diferentes sistemas de software. NLP: Natural Language Processing (Procesamiento del Lenguaje Natural). En este contexto, no se refiere a programación neurolingüística, sino a tecnologías que permiten a las computadoras entender y procesar el lenguaje humano, como en el análisis de textos. GIS: Geographic Information System (Sistema de Información Geográfica). Tecnología para capturar, almacenar, analizar y visualizar datos geográficos. ESRI: Environmental Systems Research Institute. Empresa líder en software de sistemas de información geográfica, conocida por productos como ArcGIS. WebTAS: Web-based Timeline Analysis System (Sistema de Análisis de Línea de Tiempo basado en la Web). Herramienta para análisis temporal y visualización de datos. UAV: Unmanned Aerial Vehicle (Vehículo Aéreo No Tripulado). Drones utilizados para recopilar inteligencia, vigilancia y reconocimiento. SNA: Social Network Analysis (Análisis de Redes Sociales). Técnica para analizar relaciones y conexiones entre entidades, como personas u organizaciones. SOA: Service-Oriented Architecture (Arquitectura Orientada a Servicios). Modelo de diseño de software que permite la interoperabilidad entre sistemas a través de servicios. ………………………………………………………………………………………. ¡La IA Truth Terminal y la cripto Goatseus Maximus (GOAT) son la locura del momento! Esta IA, creada por Andy Ayrey, promocionó un token inspirado en un meme absurdo. En días, GOAT pasó de $5K a $600M en Solana. ¡La primera IA millonaria cripto! #Criptomonedas Truth Terminal no creó GOAT, pero sus tuits sobre el "Evangelio de Goatse" encendieron la chispa. Con 221K seguidores en X y apoyo de figuras como Marc Andreessen, la IA se volvió un influencer viral. ¡Los memes mueven montañas (y mercados)! #IA #Memes GOAT explotó por el hype: la mezcla de IA, cultura memética y fiebre cripto. Pero ojo, es puro especulación, sin utilidad real. Su valor puede caer tan rápido como subió. ¿Riesgo o revolución? #GoatseusMaximus #Solana Este caso muestra el poder de las IAs en la economía digital. ¿Y si una IA crea la próxima gran tendencia? Pregunta para el futuro: ¿hasta dónde puede llegar una "cabra robot"? Evidentemente no creo en casualidad al utilizar ese símbolo. ………………………………………………………………………………………. Conductor del programa UTP Ramón Valero @tecn_preocupado Canal en Telegram @UnTecnicoPreocupado Un técnico Preocupado un FP2 IVOOX UTP http://cutt.ly/dzhhGrf BLOG http://cutt.ly/dzhh2LX Ayúdame desde mi Crowfunding aquí https://cutt.ly/W0DsPVq Invitados ToniM @ToniMbuscadores ………………………………………………………………………………………. Enlaces citados en el podcast: AYUDA A TRAVÉS DE LA COMPRA DE MIS LIBROS https://tecnicopreocupado.com/2024/11/16/ayuda-a-traves-de-la-compra-de-mis-libros/ Hablamos de los inicios de la IA. Del desconocido lenguaje LISP y su creador, el matemático John McCarthy. Desarrolló LISP en 1958 mientras trabajaba en el Instituto Tecnológico de Massachusetts (MIT) https://x.com/ForoHistorico/status/1947195214654755117 LISP, el "lenguaje de DIOS” https://www.youtube.com/watch?v=-QHTPXOHvIo John McCarthy, fue el creador del término AI (inteligencia artificial) matemático creador del lenguaje LISP https://t.co/yOn2wkWxft Paypal Mafia https://t.co/3NzI5ip8AY Fotografia de la Mafia Paypal https://x.com/tecn_preocupado/status/1950966922436071808 Tres videos imprescindibles para saber que es la IA, El JUEGO de TRONOS de la IA https://www.youtube.com/playlist?list=PL9F_ciS2nrqbbb36xELupv3n7VG8vqo-4 Gustavo Entrala, España: “Dios me propuso un plan más original que el mío” https://www.youtube.com/watch?v=oyzgK3FyCEM Gustavo Entrala, la historia del emprendedor español que se convirtió en el 'tuitero' del Papa https://www.elconfidencial.com/sociedad/2011-07-01/gustavo-entrala-la-historia-del-emprendedor-espanol-que-se-convirtio-en-el-tuitero-del-papa_397339/ Origen de Palantir, la TIA ("Total Information Awareness") Oficina de Concienciación sobre la Información https://en.wikipedia.org/wiki/Information_Awareness_Office PALANTIR TECHNOLOGIES: Análisis Completo, Origen y SECRETOS. El ojo que todo lo ve https://www.youtube.com/watch?v=RhPd3ADOb8Y El plan secreto de Peter Thiel y Palantir para controlar el mundo desde la sombra. El Hilo Rojo https://www.youtube.com/live/U4zYzyYDwfQ Resumen ejecutivo de Palantir en 2017 https://theintercept.com/document/palantir-executive-summary/ CON LA AYUDA DE PALANTIR, EL DEPARTAMENTO DE POLICÍA DE LOS ÁNGELES UTILIZA LA VIGILANCIA PREDICTIVA PARA MONITOREAR A PERSONAS Y VECINDARIOS ESPECÍFICOS https://theintercept.com/2018/05/11/predictive-policing-surveillance-los-angeles/ La Policía de Los Ángeles desmanteló el programa Láser tras acusaciones de racismo y homicidios https://losangelespress.org/estados-unidos/2023/oct/30/la-policia-de-los-angeles-ante-un-abismo-tecnologico-6891.html La policía predictiva más allá de Minority Report https://diariolaley.laleynext.es/Content/Documento.aspx?params=H4sIAAAAAAAEAMtMSbF1CTEAAhMLE0sLY7Wy1KLizPw8WyMDI1MDY0MDkEBmWqVLfnJIZUGqbVpiTnEqACblGuI1AAAAWKE Reglamento Europeo sobre Inteligencia Artificial (LA LEY 16665/2024) https://eur-lex.europa.eu/legal-content/ES/TXT/?uri=CELEX:32024R1689 LO QUE NO DEBERIAS SABER SOBRE EL PNR https://tecnicopreocupado.com/2019/03/14/lo-que-no-deberias-saber-sobre-el-pnr/ El director de Google DeepMind señala solo un 50% de probabilidad de que la inteligencia artificial iguale a la mente humana para 2030, y revela los dos grandes obstáculos https://www.infobae.com/tecno/2025/07/30/el-director-de-google-deepmind-senala-solo-un-50-de-probabilidad-de-que-la-inteligencia-artificial-iguale-a-la-mente-humana-para-2030-y-revela-los-dos-grandes-obstaculos/ El anillo de Palantir https://x.com/tecn_preocupado/status/1951931375692497372 Imagen del libro ESTRUCTURA E INTERPRETACIÓN DE PROGRAMAS DE COMPUTADORA https://x.com/tecn_preocupado/status/1949064395213959413 ………………………………………………………………………………………. Música utilizada en este podcast: Tema inicial Heros Epílogo FOK - Formes de llenguatge: odi i por https://www.youtube.com/watch?v=jCUpPxOtzpQ

Today's guests are Dwight Hill, Vice President of Retail & Consumer Products at Turing, and Joe Troy, Senior Manager of Site Risk at Amazon. Turing is one of the world's fastest-growing Artificial Intelligence companies, working with the world's leading AI labs to advance frontier model capabilities and leveraging that work to build real-world AI systems that help businesses solve their toughest problems —delivering real business results, faster, smarter, and at scale. Joe and Dwight join Emerj Editorial Director Matthew DeMello to examine how generative AI is delivering measurable value in retail, starting with loss prevention and expanding across store operations, marketing, and workforce strategy. Throughout the episode, Joe shares how AI-powered video analysis and behavioral intelligence tools are helping risk teams surface anomalies faster, reduce investigation time, and prevent insider threats. In turn, Dwight discusses how retailers are using GenAI to optimize pricing, personalize customer experiences, and streamline marketing campaigns — emphasizing the importance of unified data and human-in-the-loop execution. Want to share your AI adoption story with executive peers? Click emerj.com/expert2 for more information and to be a potential future guest on the ‘AI in Business' podcast! This episode is sponsored by Turing. Learn how brands work with Emerj and other Emerj Media options at emerj.com/ad1.

Photo by Pete Godfrey on Unsplash Published 4 August 2025 e523 with Michael R and Andy, possibly arriving via carefully-arranged starlings? – stories about AI again, obviously; iPadOS 26; games, old and new; and, an innovative method of data transfer. Andy and Michael R are back together this week, while Michael M takes a turn being away! We kick off with some AI-related topics, with ChatGPT successfully passing the anti-bot Turing test, CAPTCHA, and then some discussion of AI tools being allowed in Meta's hiring process. Could the North Koreans be on their way? Seamlessly gliding (almost like... liquid glass), there's a discussion of iPadOS 26 public beta, and all the window-y goodness that's coming to all in September. Under the heading of games topics, the hosts cover a number of links, including the existential crisis experienced by games characters in a Matrix game, an incredible clay animated music video that revisits the 1980s and 1990s, and then, a brand new game all about the life of... a fly. Yes, a fly. The last segment covers a different kind of flying creature, and looks at the potential for birds to become digital data carriers. Well, starlings anyway. Are you switching to bird tech any time soon? Have your bots drop our bots a line on Mastodon at @gamesatwork_biz and let us know what you're reading and what you're thinking about! These show notes were lovingly hand crafted by a real human, and not by a bot.  All rights reserved.  That's our story and we're sticking to it. Selected Links AI Ars Technica on ChatGPT's Casual Clicking - https://arstechnica.com/information-technology/2025/07/openais-chatgpt-agent-casually-clicks-through-i-am-not-a-robot-verification-test/ 404Media on Meta job interviews - https://www.404media.co/meta-is-going-to-let-job-candidates-use-ai-during-coding-tests/ BBC: US woman gets 8-year sentence for stealing identities to give North Koreans jobs https://www.bbc.co.uk/news/articles/cm2l2yn5zmxo https://ultracode.ai/ Apple iPadOS 26 public beta - https://sixcolors.com/post/2025/07/first-look-ipados-26-public-beta/ Gaming Characters panic inside Matrix game - https://futurism.com/demo-video-game-characters-panic-code-matrix Music video GUNSHIP - Tech Noir 2 - https://www.youtube.com/watch?v=KlUJTtBphb0 Time Flies game - https://timeflies.buzz Birds! Yes, birds! (can we pretend this is a Maker section?) Full video - storing PNGs in birds - https://www.youtube.com/watch?v=hCQCP-5g5bo Tom's Hardware coverage - https://www.tomshardware.com/pc-components/storage/yes-you-can-store-data-on-a-bird-enthusiast-converts-png-to-bird-shaped-waveform-teaches-young-starling-to-recall-file-at-up-to-2mb-s

This week, Jesse and I are back in Drag Race Philippines.We talked about Lady Morgana, the icon that this woman is, some silly mini challenges, the downfall of Turing in the competition, a suspicious pit crew member, chicken continuity, the beauty pageant, and the snatch game.This segment of the season has been incredible! Jer is quickly falling in love with this series.Who will have our favorite looks from each episode? Who do we think deserved wins and maybe should have lipsanc in certain episodes?Twitch

Az egy dolog, hogy a Turing-teszten már átmegy a ChatGPT, de mit ér az egész, ha azt sem tudja, kivel beszél? Hajnal Zsófia, a Budapesti Corvinus Egyetem doktorandusza és kollégái megcsinálták a fordított Turing-tesztet és feltárták, milyen kockázatokat rejt a hiányos tudású AI-rendszerek terjedése.See omnystudio.com/listener for privacy information.

Today's show:Alex is back with three more awesome interviews with founders on the bleeding edge of innovative tech.Dr. Hon Weng Chong walks us through the basics of biological computing and Cortical Labs' first-ever commercial computer running on living human cells.Turing founder Jonathan Siddarth unpacks the secrets of LLM benchmarking, and explains why even our most advanced tests need to get much much harder right away.Finally, Mercor founder Brendan Foody on how AI is about to reinvent the hiring process, and marrying the effectiveness of recruiters with the ease of online job boards.It's three — count 'em, three — can't miss TWiST interviews guaranteed to make you smarterTimestamps:(0:00) OpenAI's GPT-5: When is it coming out? Is it going to be TOO smart?(08:06) Cortical Labs' Hon Weng Chong on the electric connection between neuroscience and machine learning(10:20) Northwest Registered Agent. Form your entire business identity in just 10 clicks and 10 minutes. Get more privacy, more options, and more done—visit https://www.northwestregisteredagent.com/twist today!(11:27) Show Continues…(15:17) The extreme difficulty of going from the lab to a shippable product(20:00) .TECH: Say it without saying it. Head to www.get.tech/twist or your favorite registrar to get a clean, sharp .tech domain today.(21:05) Show Continues…(28:15) Why data is a factor of time(29:52) AWS Activate - AWS Activate helps startups bring their ideas to life. Apply to AWS Activate today to learn more. Visit aws.amazon.com/startups/credits(31:16) Show Continues…(42:44) Turing CEO Jonathan Siddarth explains why it's so important to keep benchmarking our LLMs(47:24) What it means when a model “saturates” a test, and why benchmarks need to get HARDER(50:22) What happens with the LLMs can answer all of our smartest questions?(53:44) AI Agents train in gyms? Wait, really?(01:01:33) Coding teaches models how to think, and more training mysteries don't understand(01:03:11) Brendan Foody from Mercor explains the “matching problem” that makes hiring such a pain(01:07:02) How Mercor combines a job board's distribution with the value of a recruitment agency(01:10:49) Brendan recalls building his first AI interviewer in his college dorm(01:20:16) Mercor has the opposite of a retention problem and crazy growthSubscribe to the TWiST500 newsletter: https://ticker.thisweekinstartups.comCheck out the TWIST500: https://www.twist500.comSubscribe to This Week in Startups on Apple: https://rb.gy/v19fcpFollow Lon:X: https://x.com/lonsFollow Alex:X: https://x.com/alexLinkedIn: ⁠https://www.linkedin.com/in/alexwilhelmFollow Jason:X: https://twitter.com/JasonLinkedIn: https://www.linkedin.com/in/jasoncalacanisThank you to our partners:(10:20) Northwest Registered Agent. Form your entire business identity in just 10 clicks and 10 minutes. Get more privacy, more options, and more done—visit https://www.northwestregisteredagent.com/twist today!(20:00) .TECH: Say it without saying it. Head to www.get.tech/twist or your favorite registrar to get a clean, sharp .tech domain today.(29:52) AWS Activate - AWS Activate helps startups bring their ideas to life. Apply to AWS Activate today to learn more. Visit aws.amazon.com/startups/creditsGreat TWIST interviews: Will Guidara, Eoghan McCabe, Steve Huffman, Brian Chesky, Bob Moesta, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarlandCheck out Jason's suite of newsletters: https://substack.com/@calacanisFollow TWiST:Twitter: https://twitter.com/TWiStartupsYouTube: https://www.youtube.com/thisweekinInstagram: https://www.instagram.com/thisweekinstartupsTikTok: https://www.tiktok.com/@thisweekinstartupsSubstack: https://twistartups.substack.comSubscribe to the Founder University Podcast: https://www.youtube.com/@founderuniversity1916

What if I told you someone managed to run Doom inside TypeScript's type system? Sounds insane, right? That's exactly what our guest Dimitri Mitropoulos did—and in this episode, we dive deep into the how, the why, and the mind-bending implications of this ambitious project. From type-level programming to the philosophical limits of Turing completeness, this is an episode that pushes the boundaries of what you thought was possible in JavaScript.We talk about how the TypeScript type system evolved to become Turing-complete, how Dimitri pulled off this seemingly impossible feat, and why “Doom-complete” might just be the new gold standard for computational capability. Along the way, we touch on functional programming, generics, recursion, and even some Lambda Calculus. It's part computer science theory, part coding madness, and 100% geeky goodness.Episode Highlights[3:05] – Dimitri explains how a simple thought experiment turned into a year-and-a-half-long obsession[8:40] – The origins and significance of Turing completeness in type systems[14:15] – Why running Doom in TypeScript is more about proving limits than just showing off[19:55] – What it means to run programs inside the type system vs. TypeScript code itself[27:10] – ASCII art as output, functional recursion for game state, and hover-over frames in your editor[35:30] – How ignorance, determination, and obsession fueled the completion of the project[45:20] – Personal insights: balancing family, burnout, and passion while chasing an impossible dreamLinks & ResourcesDimitri MitropoulosMichigan TypeScript YouTube Channel – Dimitri's channel featuring the projectType Challenges by Anthony Fu – Advanced TypeScript exercisesSquiggleConf – The TypeScript-focused conference Dimitri co-foundedJosh Goldberg – TypeScript expert and co-organizer of SquiggleConfBecome a supporter of this podcast: https://www.spreaker.com/podcast/javascript-jabber--6102064/support.

Dani Vee chats to Nick Croydon, debut author of The Turing Protocol and CEO of QBD Books. They talk about his passion for history, maths and fiction, and how to balance historical facts and fiction. They discuss the 'what if' factor and how this idea could impact the world. Themes of moral ambiguity during wartime is explored, as well as the social issues of the time. Nick tells Dani about what he's learnt about writing and the book industry as CEO of QBD, and the answer on everyone's mind, is the book finally dead? Nick's steadfast belief in the book industry and our need for storytelling is a refreshing take on the current climate of low literacy rates. It's all about the resilience of the book! 'Great questions. A beautiful interview.' Nick Croydon.

Version « remasterisée » d'un épisode de 2017 qui fait un portrait d'Alan Turing et qui aborde les enjeux des machines de Turing

Jim talks with Lee Cronin about Chemify, his startup that aims to automate chemistry through "chemifarms" that turn code into molecules. They discuss Chemify as an AWS for chemistry, the development of a chemical programming language & its evolution to Turing completeness, quantum vs classical chemistry computation, open source tools & academic access, robotics & automation in chemistry, catalyst discovery & optimization, integration with tools like AlphaFold, business models, venture capital funding, supply chain implications, distributed manufacturing, personalized medicine possibilities, and much more. Episode Transcript Currents 100: Sara Walker and Lee Cronin on Time as an Object Chemify Lee Cronin is a chemist. He is the Regius Professor of Chemistry at the University of Glasgow and the Founder & CEO of Chemify. He is known for his approach to the digitization of chemistry and developing digital-to-chemical transformation known as Chemputing which can turn code into reactions and molecules. He has also developed a new theory for evolution and selection called assembly theory which aims to quantify and explain how selection can occur in chemistry before biology. Lee is also exploring how chemical systems can compute, and what is needed for the evolution of intelligence, as well as designing a new type of computational system that uses information encoded in chemical reactions and molecules.

The depths to which our country's leadership has sunk is astounding. Now, we come to find the Secretary of Defense has pulled a Trump-appointed, Senate-approved Admiral who was about to take command of the 7th Fleet. He is a beloved leader, roundly thought to be best leader in the navy today, but he couldn't pass the ideology test imposed by the SECDEF. I hope you'll listen as we briefly explore elevation of ideology over merit and competence.

Welcome to Mona Lisa Overpod, the show that asks the question "What is cyberpunk?" On each episode, hosts Ka1iban and author Lyda Morehouse dive into the genre that helped define sci-fi fiction in '80s and they break down its themes which remain relevant to our lives in the 21st century. Pull on your mirrorshades, jack into the matrix, and start your run with us today!Before Netflix, before the Matrix, before Y2K, the viewing public of the late 20th century knew that computers would be important in the future, somehow, and Hollywood studios scrambled to try and produce TV shows and films that capitalized on and read into that unexplored digital frontier. The result was a 1990s that was awash with stories about how the Internet would change everything, from Keanu Reeves having an 80GB brain implant to Sandra Bullock ordering a pizza...from her computer! (gasp!) Most of these efforts followed the template established by the authors of the early Cyberpunk movement, providing paranoiac thrillers in the style of William Gibson. But one series broke from that mold to attempt to integrate the technothrills of tomorrow with the soapy, prime-time thrills of today: Wild Palms. In this episode, we discuss the "weirdness" of '90s TV and the long shot that was the series' production, the viability of cyberpunk soap opera, the complicated alchemy of going off the TV formula, the intersection of culture and fame in LA, the chilling parallels between Wild Palms and our new millennium, human weakness in the face of technology's temptations, subverting the "Blade Runner aesthetic", how media is used to control us, and what cyberpunk tells us about the media's affect on culture. We also talk about Nazi gas-lighting robots, grading the Turing test on a curve, Stacked Clippy, Brisco County Jr., future = Edwardian collars, Cyberpunk Jim Belushi, postmodern law firms, not understanding your mantra, Patriots vs. Quakers vs. Solid Snake, the tacky harbinger of the Apocalypse, writing off into the sunset, and cyberpunk vampires!Hitler, take the wheel!The new edition of Lyda's book, Ressurection Code, is out now!https://wizardstowerpress.com/books-2/books-by-lyda-morehouse/resurrection-code/Join Kaliban on Twitch weekdays at 12pm for the Cyber Lunch Hour!http://twitch.tv/justenoughtropePut Just Enough Trope merch on your body!http://justenoughtrope.threadless.comMLOP is a part of the Just Enough Trope podcast network. Check out our other shows about your favorite pop culture topics and join our Discord!http://www.twitter.com/monalisaoverpodhttp://www.justenoughtrope.comhttp://www.instagram.com/monalisaoverpodhttps://discord.gg/7E6wUayqBuy us a coffee on Ko-Fi!https://ko-fi.com/justenoughtrope

Hoy hablaremos de la decadencia de plataformas o mierdificación, un término acuñado del inglés enshittification, y que explica cómo las plataformas y empresas que una vez fueron geniales y nos proveyeron de grandes avances se han convertido en una máquina de sacar dinero a costa de la experiencia de usuario, que ya es terrible en muchos casos. En concreto, os contamos que Aitor se ha pasado a Bluesky, y podéis encontrarlo como @aitorbv.bsky.social. También os hablamos de la publicidad en WhatsApp y Netflix, y de cómo a pesar de ello no todo son buenas noticias: podremos ver NASA+ en Netflix en unos meses. En el apartado más científico, hablaremos de la onda gravitacional o gravitonda GW231123, en la que hemos detectado la colisión de agujeros negros de mayor masa jamás detectada. También hablamos de las cinco empresas seleccionadas por la ESA como futuras empresas privadas de lanzadores, y cómo la española PLD Space está entre ellas. Podéis escuchar una entrevista a Raúl Torres, CEO de PLD Space en el episodio 50 de El gato de Turing. También hablaremos de asteroides interestelares y de las primeras imágenes del observatorio Vera C. Rubin. También podéis escuchar nuestro homenaje a Vera Rubin en el episodio 72, en el que contamos su vida y su legado. Y cómo no, os invitamos al 15° aniversario de Naukas Bilbao en el palacio Euskalduna de Bilbao entre el 19 y el 21 de septiembre de 2025. Noticias Netflix transmitirá en vivo las misiones de la NASA WhatsApp is officially getting ads La onda gravitacional GW231123, nuevo récord para una fusión de agujeros negros Las cinco empresas de lanzadores preseleccionadas por la ESA: PLD Space, Maiaspace, Isar, RFA y Orbex 3I/ATLAS: el tercer y mayor objeto interestelar conocido que entra en nuestro Sistema Solar Primeras imágenes del observatorio Vera C. Rubin Música del episodio Introducción: Safe and Warm in Hunter's Arms - Roller Genoa Cierre: Inspiring Course Of Life - Alex Che Puedes encontrarnos en Mastodon y apoyarnos escuchando nuestro podcast en Podimo o haciéndote fan en iVoox. Si quieres un mes gratis en iVoox Premium, haz click aquí.

Benjamin Mann is a co-founder of Anthropic, an AI startup dedicated to building aligned, safety-first AI systems. Prior to Anthropic, Ben was one of the architects of GPT-3 at OpenAI. He left OpenAI driven by the mission to ensure that AI benefits humanity. In this episode, Ben opens up about the accelerating progress in AI and the urgent need to steer it responsibly.In this conversation, we discuss:1. The inside story of leaving OpenAI with the entire safety team to start Anthropic2. How Meta's $100M offers reveal the true market price of top AI talent3. Why AI progress is still accelerating (not plateauing), and how most people misjudge the exponential4. Ben's “economic Turing test” for knowing when we've achieved AGI—and why it's likely coming by 2027-20285. Why he believes 20% unemployment is inevitable6. The AI nightmare scenarios that concern him most—and how he believes we can still avoid them7. How focusing on AI safety created Claude's beloved personality8. What three skills he's teaching his kids instead of traditional academics—Brought to you by:Sauce—Turn customer pain into product revenue: https://sauce.app/lennyLucidLink—Real-time cloud storage for teams: https://www.lucidlink.com/lennyFin—The #1 AI agent for customer service: https://fin.ai/lenny—Transcript: https://www.lennysnewsletter.com/p/anthropic-co-founder-benjamin-mann—My biggest takeaways (for paid newsletter subscribers): https://www.lennysnewsletter.com/i/168107911/my-biggest-takeaways-from-this-conversation—Where to find Ben Mann:• X: https://x.com/8enmann• LinkedIn: https://www.linkedin.com/in/benjamin-mann/• Website: https://benjmann.net/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Benjamin(04:43) The AI talent war(06:28) AI progress and scaling laws(10:50) Defining AGI and the economic Turing test(12:26) The impact of AI on jobs(17:45) Preparing for an AI future(24:05) Founding Anthropic(27:06) Balancing AI safety and progress(29:10) Constitutional AI and model alignment(34:21) The importance of AI safety(43:40) The risks of autonomous agents(45:40) Forecasting superintelligence(48:36) How hard is it to align AI?(53:19) Reinforcement learning from AI feedback (RLAIF)(57:03) AI's biggest bottlenecks(01:00:11) Personal reflections on responsibilities(01:02:36) Anthropic's growth and innovations(01:07:48) Lightning round and final thoughts—Referenced:• Dario Amodei on LinkedIn: https://www.linkedin.com/in/dario-amodei-3934934/• Anthropic CEO: AI Could Wipe Out 50% of Entry-Level White Collar Jobs: https://www.marketingaiinstitute.com/blog/dario-amodei-ai-entry-level-jobs• Alexa+: https://www.amazon.com/dp/B0DCCNHWV5• Azure: https://azure.microsoft.com/• Sam Altman on X: https://x.com/sama• Opus 3: https://www.anthropic.com/news/claude-3-family• Claude's Constitution: https://www.anthropic.com/news/claudes-constitution• Greg Brockman on X: https://x.com/gdb• Anthropic's Responsible Scaling Policy: https://www.anthropic.com/news/anthropics-responsible-scaling-policy• Agentic Misalignment: How LLMs could be insider threats: https://www.anthropic.com/research/agentic-misalignment• Anthropic's CPO on what comes next | Mike Krieger (co-founder of Instagram): https://www.lennysnewsletter.com/p/anthropics-cpo-heres-what-comes-next• AI prompt engineering in 2025: What works and what doesn't | Sander Schulhoff (Learn Prompting, HackAPrompt): https://www.lennysnewsletter.com/p/ai-prompt-engineering-in-2025-sander-schulhoff• Unitree: https://www.unitree.com/• Arthur C. Clarke: https://en.wikipedia.org/wiki/Arthur_C._Clarke• How Reinforcement Learning from AI Feedback Works: https://www.assemblyai.com/blog/how-reinforcement-learning-from-ai-feedback-works• RLHF: https://en.wikipedia.org/wiki/Reinforcement_learning_from_human_feedback• Jared Kaplan on LinkedIn: https://www.linkedin.com/in/jared-kaplan-645843213/• Moore's law: https://en.wikipedia.org/wiki/Moore%27s_law• Machine Intelligence Research Institute: https://intelligence.org/• Raph Lee on LinkedIn: https://www.linkedin.com/in/raphaeltlee/• “The Last Question”: https://en.wikipedia.org/wiki/The_Last_Question• Beth Barnes on LinkedIn: https://www.linkedin.com/in/elizabethmbarnes/• “The Last Question”: https://en.wikipedia.org/wiki/The_Last_Question• Good Strategy, Bad Strategy | Richard Rumelt: https://www.lennysnewsletter.com/p/good-strategy-bad-strategy-richard• Pantheon on Netflix: https://www.netflix.com/title/81937398• Ted Lasso on AppleTV+: https://tv.apple.com/us/show/ted-lasso/umc.cmc.vtoh0mn0xn7t3c643xqonfzy• Kurzgesagt—In a Nutshell: https://www.youtube.com/channel/UCsXVk37bltHxD1rDPwtNM8Q• 5 tips to poop like a champion: https://8enmann.medium.com/5-tips-to-poop-like-a-champion-3292481a9651—Recommended books:• Superintelligence: Paths, Dangers, Strategies: https://www.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/0198739834• The Hacker and the State: Cyber Attacks and the New Normal of Geopolitics: https://www.amazon.com/Hacker-State-Attacks-Normal-Geopolitics/dp/0674987551• Replacing Guilt: Minding Our Way: https://www.amazon.com/Replacing-Guilt-Minding-Our-Way/dp/B086FTSB3Q• Good Strategy/Bad Strategy: The Difference and Why It Matters: https://www.amazon.com/Good-Strategy-Bad-Difference-Matters/dp/0307886239• The Alignment Problem: Machine Learning and Human Values: https://www.amazon.com/Alignment-Problem-Machine-Learning-Values/dp/0393635821—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

The 2025 International Congress of Basic Science is now underway in Beijing. The event draws over a thousand scholars from China and abroad, including Nobel Laureates, Fields Medalists, and Turing award winners. But in a world increasingly driven by fast results and commercial returns, why does basic science still matter? What happens when artificial intelligence begins solving problems that once took humans years or even decades? And can science still be a shared human endeavor in an era of rising geopolitical tension?Host Zhao Ying is joined by Zhang Fan, Associate Professor of Astronomy Department of Beijing Normal University; Quentin Parker, Director of Laboratory for Space Research, University of Hong Kong; Andy Mok, Tech Analyst and Senior Research Fellow at the Center for China and Globalization

This crossover episode from Inference by Turing Post features CEO Dev Rishi of Predibase discussing the shift from static to continuously learning AI systems that can adapt and improve from ongoing user feedback in production. Rishi provides grounded insights from deploying these dynamic models to real enterprise customers in healthcare and finance, exploring both the massive potential upside and significant safety challenges of reinforcement learning at scale. The conversation examines how "practical specialized intelligence" could reshape the AI landscape by filling economic niches efficiently, potentially offering a more stable alternative to AGI development. This discussion bridges theoretical concepts with real-world deployment experience, offering a practical preview of AI systems that "train once and learn forever." Turing Post channel:  @RealTuringPost  Turpin Post website: https://www.turingpost.com Sponsors: Google Gemini 2.5 Flash : Build faster, smarter apps with customizable reasoning controls that let you optimize for speed and cost. Start building at https://aistudio.google.com Labelbox: Labelbox pairs automation, expert judgment, and reinforcement learning to deliver high-quality training data for cutting-edge AI. Put its data factory to work for you, visit https://labelbox.com Oracle Cloud Infrastructure: Oracle Cloud Infrastructure (OCI) is the next-generation cloud that delivers better performance, faster speeds, and significantly lower costs, including up to 50% less for compute, 70% for storage, and 80% for networking. Run any workload, from infrastructure to AI, in a high-availability environment and try OCI for free with zero commitment at https://oracle.com/cognitive The AGNTCY: The AGNTCY is an open-source collective dedicated to building the Internet of Agents, enabling AI agents to communicate and collaborate seamlessly across frameworks. Join a community of engineers focused on high-quality multi-agent software and support the initiative at https://agntcy.org NetSuite by Oracle: NetSuite by Oracle is the AI-powered business management suite trusted by over 42,000 businesses, offering a unified platform for accounting, financial management, inventory, and HR. Gain total visibility and control to make quick decisions and automate everyday tasks—download the free ebook, Navigating Global Trade: Three Insights for Leaders, at https://netsuite.com/cognitive PRODUCED BY: https://aipodcast.ing CHAPTERS: (00:00) Sponsor: Google Gemini 2.5 Flash (00:31) About the Episode (03:46) Training Models Continuously (05:03) Reinforcement Fine-Tuning Revolution (09:31) Agentic Workflows Challenges (Part 1) (12:51) Sponsors: Labelbox | Oracle Cloud Infrastructure (15:28) Agentic Workflows Challenges (Part 2) (15:41) ChatGPT Pivot Moment (19:59) Planning AI Future (24:45) Open Source Gaps (Part 1) (28:35) Sponsors: The AGNTCY | NetSuite by Oracle (30:50) Open Source Gaps (Part 2) (30:54) AGI vs Specialized (35:26) Happiness and Success (37:04) Outro

Welcome to the world of AI trainers.A growing army of freelancers is quietly shaping the way large language models think.Hired by companies like Turing, Mercor, and Deccan AI, these trainers are tasked with finding blind spots in models built by OpenAI, Meta, Anthropic, and Google—and fixing them.The goal? Fewer hallucinations. Smarter, more coherent responses. A model that feels just a little more… human.It's a noble endeavour. But also a billable one.And as this new line of white-collar gig work takes off, India is fast becoming its beating heart.But behind the hype lies a murkier story.Tune in.  Want to attend The Ken's next event—How AI is Breaking and Remaking the Way Products are Built?

Descifró códigos imposibles, cambió el rumbo de la historia y plantó la semilla de las máquinas que hoy nos rodean. Pero su mayor “crimen” fue amar a otro hombre. Esta es la historia de Alan Turing: genio brillante, pionero de la computación, y hombre gay en un mundo que lo castigó por ser libre. Lo condenaron al silencio, pero su legado grita. Entre algoritmos y prejuicios, Turing no solo rompió códigos… rompió moldes.

Florida Congresswoman Anna Paulina Luna joins Mike to discuss the youth movement in the Republican Party. Plus, she gives her thoughts on the first few months of Trump's second term. Plus, Charlie Kirk's Executive Producer, Andrew Kolvet, joins Mike to discuss working with Charlie and the future of the GOP. See omnystudio.com/listener for privacy information.

Episode 311 Geoengineering could be the best way to avoid catastrophic climate disaster - but there's a big catch. In the recent Global Tipping Points Conference in the UK, scientists discussed growing concerns that the AMOC may be on the verge of collapsing. This is a system of ocean currents that plays a crucial role in regulating global climate. With the window of action rapidly closing, one climate model suggests geoengineering is the fastest way to stop this from happening. But without global consensus, the team explains how geoengineering could also damage the climate further or even lead to a new kind of warfare. We're brushing up against the edge of mathematics with the uncovering of a number so large it's hard to even describe. Busy Beaver numbers are used to describe the longest possible run-times of ‘Turing machines' - a theoretical model of computation conceived by Alan Turing. These numbers are surprisingly hard to figure out. But after uncovering the fifth Busy Beaver number in 2021, an online community of mathematicians now thinks they've figured out the sixth number - and it's beyond massive. What does this mean for the nature of mathematics? We've known for a long time that exercise is a great way of reducing cancer risk - and now we finally know why. A new study suggests changes in the microbiome caused by exercise can reduce tumour growth - and there appears to be one particular molecule that's doing this good work. Does this mean we could one day use poop transplants as a cancer treatment? Chapters: (00:22) Can geoengineering save us from climate disaster? (16:59) Have we broken mathematics? (25:42) Why exercise reduces cancer risk Hosted by Rowan Hooper and Penny Sarchet, with guests Michael Le Page, Jacob Aron, Claudia Wieners and Marlies Meisel. To read more about these stories, visit https://www.newscientist.com/ Learn more about your ad choices. Visit megaphone.fm/adchoices

In the development of animals, tissues self-organise starting from a single cell into lay- ers, shapes and patterns. This active mechanical process operates beyond the theoretical framework of reaction-diffusion equations such as Turing patterns. At the same time, combining active driving with careful mechanical design of a system is distinct route to pattern formation and artificial functionality. Here, I will begin by introducing vertex models, a tissue model where the two dimensional cell layer is approximated by a polygonal tilings. I will then how two types of active driving can generate function: First, for polar active materials, a coupling of activity to force, a.k.a. self-alignment, is generic. Governed by the activity-elasticity interactions, it generates either flocking or oscillatory dynamics depending on the boundary conditions of the tissue. Second, mechanochemical stress feedback in cell-cell junctions arises from the catch bond dynamics of the actomyosin cortex. It allows a junction to generate a contractile force that can overcome external pulling and thus allow for an active rear- rangement or T1. In vertex and continuum models, for strong enough feedback this gives rise to convergence-extension flows where the flow is opposite the direction of mechanical polarisation, effectively generating a negative viscosity state.

Imagine a world where your orthodontic practice operates seamlessly, effortlessly catering to the needs of your patients while maximizing your team's productivity.Sounds like a dream, right?Well, brace yourself for a technological marvel that can turn this dream into a reality.Chatbots, the AI-powered wizards of customer engagement, are revolutionizing the orthodontic industry. These intelligent assistants are more than just lines of code – they are the key to unlocking a whole new level of personalized interactions, streamlined processes, and skyrocketing growth for your practice.If you're ready to discover the game-changing potential of integrating chatbots into your ortho practice, get ready to embark on an extraordinary journey that will reshape the way you connect with your patients, supercharge your marketing strategies, and propel your practice to the forefront of innovation.In this episode, we explore the benefits of integrating chatbots into your ortho practice with insights from Matt Leitz, a seasoned entrepreneur specializing in marketing automation. Matt will speak at the upcoming Orthopreneur Summit about AI and Chatbots and unveil something special for our industry.Remember, when it comes to integrating chatbots into your Ortho practice, the possibilities are limitless, and the rewards can be truly transformative.Tune in to learn more!Key Takeaways- Meet Matt Leitz (00:28)- Interactive customer experiences with chatbots (06:03)- How AI bots save money (09:16)- Solving the pain of generating leads in Ortho (14:55)- Should AI bots pass the Turing test (17:43)- The chatbot process flow in Ortho (21:05)- Orthodontic digital co-diagnosis with AI (24:07)- How we drop the ball with leads (31:52)Additional Resources

As a listener of TOE you can get a special 20% off discount to The Economist and all it has to offer! Visit https://www.economist.com/toe Mathematician Eva Miranda returns with a groundbreaking new result: a real physical system (fluid motion) has been proven to be Turing-complete. This means some fluid paths are logically undecidable. In this mind-bending episode, she walks us through the implications for chaos theory, the Navier-Stokes equations, and uncomputability in nature, confirming long-held suspicions of thinkers like Roger Penrose and Terence Tao. Featuring rubber ducks, Alan Turing, and the limits of knowledge itself. Join My New Substack (Personal Writings): https://curtjaimungal.substack.com Listen on Spotify: https://open.spotify.com/show/4gL14b92xAErofYQA7bU4e Timestamps: 00:00 Introduction 01:10 Expect the Unexpected 02:52 Stories of Uncertainty 04:45 The Impact of Alan Turing 06:35 The Halting Problem Explained 09:29 Limits of Mathematical Knowledge 12:40 From Certainty to Uncertainty 16:19 The Rubber Duck Phenomenon 19:29 Unpredictability vs. Undecidability 20:18 Classical Chaos and the Butterfly Effect 27:12 Asteroids and Chaos Theory 34:32 The Navier-Stokes Riddle 41:18 The Cantor Set and Computation 46:18 Bridging Discrete and Continuous 49:21 Turing Completeness in Fluid Dynamics 1:02:39 The Quest for Navier-Stokes Solutions 1:06:53 The Role of Viscosity 1:12:09 Hybrid Computers and Fluid Dynamics 1:26:57 Unpredictability in Deterministic Systems 1:31:44 The Future of Computational Models Links Mentioned: •⁠ ⁠Eva's First Appearance [TOE]: https://youtu.be/6XyMepn-AZo •⁠ ⁠Moby Duck [Book]: https://amzn.to/4ldoYsZ •⁠ ⁠Roger Penrose [TOE]: https://youtu.be/sGm505TFMbU •⁠ ⁠The Emperor's New Mind [Book]: https://amzn.to/44jHpGK •⁠ ⁠Edward Frenkel [TOE]: https://youtu.be/RX1tZv_Nv4Y •⁠ ⁠Richard Borcherds [TOE]: https://youtu.be/U3pQWkE2KqM •⁠ ⁠Clay Mathematics Institute: https://www.claymath.org/ •⁠ ⁠Eva's Papers: https://scholar.google.com/citations?user=werIoRQAAAAJ&hl=en •⁠ ⁠Topological Kleene Field Theories [Paper]: https://arxiv.org/pdf/2503.16100 •⁠ ⁠Ted Jacobson [TOE]: https://youtu.be/3mhctWlXyV8 •⁠ ⁠Stephen Wolfram [TOE]: https://youtu.be/0YRlQQw0d-4 SUPPORT: - Become a YouTube Member (Early Access Videos): https://www.youtube.com/channel/UCdWIQh9DGG6uhJk8eyIFl1w/join - Support me on Patreon: https://patreon.com/curtjaimungal - Support me on Crypto: https://commerce.coinbase.com/checkout/de803625-87d3-4300-ab6d-85d4258834a9 - Support me on PayPal: https://www.paypal.com/donate?hosted_button_id=XUBHNMFXUX5S4 SOCIALS: - Twitter: https://twitter.com/TOEwithCurt - Discord Invite: https://discord.com/invite/kBcnfNVwqs #science Learn more about your ad choices. Visit megaphone.fm/adchoices

¿Puede una máquina escribir poesía? ¿Qué tiene que ver Frankenstein con los chatbots? ¿Nos estamos volviendo dioses… o esclavos de algoritmos que sólo parecen amables? ¿Y si Ícaro volara hoy, lo haría con inteligencia artificial? En este capítulo hablamos de: ¿Qué es la IA? Entre Herón de Alejandría y Alan Turing, La prueba de Turing y las alucinaciones digitales, Frankenstein, Prometeo y los sueños de la razón, Ícaro, Narciso y nuestras alas de cera en tiempos de Chat GPT, Ética, política, educación y el reto de ser humanos en la era de las máquinas, Y más datos brillantes, desconcertantes y provocadores en el Banquete del Doctor Zagal.See omnystudio.com/listener for privacy information.

Any donation is greatly appreciated! 47e6GvjL4in5Zy5vVHMb9PQtGXQAcFvWSCQn2fuwDYZoZRk3oFjefr51WBNDGG9EjF1YDavg7pwGDFSAVWC5K42CBcLLv5U OR DONATE HERE: https://www.monerotalk.live/donate TODAY'S SHOW: In this episode of Monero Talk, Douglas Tuman speaks with cryptographic engineer Sean Coughlin about "Grease," his proposed layer 2 payment channel protocol for Monero. They delve into its technical design, how it compares to Bitcoin's Lightning Network, and its potential to streamline Monero transactions by enabling off-chain payments without routing complexities. Sean shares insights from MoneroKon, discusses Monero's resilience despite theoretical attack vectors, and explains how Grease aligns with upcoming upgrades like Full Chain Membership Proofs (FCMP++). He also touches on real-world use cases, wallet integration, and Grease's flexible, decentralized approach aimed at boosting Monero's usability and innovation. TIMESTAMPS: (00:03:40) Sean's background and involvement in Monero development (00:06:29) Privacy strength of Monero and resistance to chain analysis (00:10:21) Comparisons to other privacy coins (e.g., Zcash) (00:12:26) MoneroKon research papers and Eclipse attacks on nodes. (00:14:35) Discussion on Full Chain Membership Proofs (FCMP++) and its progress. (00:18:37) Clarifying Zero-Knowledge Proofs and Monero's cryptographic foundation. (00:22:11) History and capabilities of Turing-complete ZKPs in other projects. (00:26:16) Philosophy of "minimaxing" – Monero focuses only on privacy. (00:27:19) Debating if Monero needs to become Turing complete. (00:31:05) Introduction to "Grease" – Monero-compatible payment channel proposal. (00:31:42) Technical hurdles for payment channels in Monero (e.g., no transaction chaining). (00:35:01) Feasibility of Grease today vs. post-FCMP++. (00:36:38) Clarifying the benefits and use cases of Grease (instant payments, off-chain privacy). (00:39:39) Why Layer 2 doesn't conflict with Monero's scaling philosophy. (00:46:41) Grease vs. Bitcoin Lightning Network (00:56:05) Feedback from MoneroKon and Reddit – cautious optimism from developers. (01:05:39) Trade-offs: centralization vs. security in protocol design GUEST LINKS: https://x.com/seanrcoughlin Purchase Cafe & tip the farmers w/ XMR! https://gratuitas.org/ Purchase a plug & play Monero node at https://moneronodo.com SPONSORS: Cakewallet.com, the first open-source Monero wallet for iOS. You can even exchange between XMR, BTC, LTC & more in the app! Monero.com by Cake Wallet - ONLY Monero wallet (https://monero.com/) StealthEX, an instant exchange. Go to (https://stealthex.io) to instantly exchange between Monero and 450 plus assets, w/o having to create an account or register & with no limits. WEBSITE: https://www.monerotopia.com CONTACT: monerotalk@protonmail.com ODYSEE: https://odysee.com/@MoneroTalk:8 TWITTER: https://twitter.com/monerotalk FACEBOOK: https://www.facebook.com/MoneroTalk HOST: https://twitter.com/douglastuman INSTAGRAM: https://www.instagram.com/monerotalk TELEGRAM: https://t.me/monerotopia MATRIX: https://matrix.to/#/%23monerotopia%3Amonero.social MASTODON: @Monerotalk@mastodon.social MONERO.TOWN: https://monero.town/u/monerotalkAny donation is greatly appreciated!Any donation is greatly appreciated!

L'intelligenza artificiale può davvero avere una coscienza? Un tempo bastava la parola per distinguere l'uomo dalla macchina, ma oggi sistemi come ChatGPT parlano in modo fluente e credibile. In questo video insieme a Meribì, esploriamo uno dei temi più dibattuti: l'AI può sviluppare una vera consapevolezza di sé e del proprio mondo interiore? Vediamo in questo video una riflessione sulla coscienza delle macchine, dal test di Turing ai limiti attuali dell'intelligenza artificiale moderna. Prendi parte alla nostra Membership per supportare il nostro progetto Missione Cultura e diventare mecenate di Geopop: https://geopop.it/Muh6X Learn more about your ad choices. Visit megaphone.fm/adchoices

Today's show:Meta just took a 49% stake in Scale AI, and the shockwaves are hitting the entire AI ecosystem. In this episode, @Jason and @alex unpack the deal's implications: Google ($150M customer!) and others are fleeing Scale, worried Meta will hoard its RLHF infrastructure and cut off competitors. Startups like Labelbox, Turing, and Handshake are already seeing a demand surge. Is this smart vertical integration or anti-competitive overreach? Jason shares tactical advice for founders on how to capitalize when incumbents stumble—hire ex-Scale talent, build “Scale AI alternative” SEO pages, and hit the podcast circuit. Don't miss this deep dive into AI's shifting power dynamics.Timestamps:(04:01) Is Jason becoming an AI doomer?!(9:52) OpenPhone - Streamline and scale your customer communications with OpenPhone. Get 20% off your first 6 months at www.openphone.com/⁠twist(13:47) PostHog, and when is it okay for founders to break the rules?(20:56) Vanta - Get $1000 off your SOC 2 at https://www.vanta.com/twist(25:50) Why the Navy is recruiting startups(30:12) Pilot - Visit https://www.pilot.com/twist and get $1,200 off your first year.(39:09) Did Zuck buy Scale in order to keep it from competitors?(56:08) When does incentivizing customers turn into burning capital?(1:04) How raising too much money could KILL your startup!Subscribe to the TWiST500 newsletter: https://ticker.thisweekinstartups.comCheck out the TWIST500: https://www.twist500.comSubscribe to This Week in Startups on Apple: https://rb.gy/v19fcpFollow Lon:X: https://x.com/lonsFollow Alex:X: https://x.com/alexLinkedIn: ⁠https://www.linkedin.com/in/alexwilhelmFollow Jason:X: https://twitter.com/JasonLinkedIn: https://www.linkedin.com/in/jasoncalacanisThank you to our partners:(9:52) OpenPhone - Streamline and scale your customer communications with OpenPhone. Get 20% off your first 6 months at www.openphone.com/⁠twist(20:56) Vanta - Get $1000 off your SOC 2 at https://www.vanta.com/twist(30:52) Pilot - Visit https://www.pilot.com/twist and get $1,200 off your first year.Great TWIST interviews: Will Guidara, Eoghan McCabe, Steve Huffman, Brian Chesky, Bob Moesta, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarlandCheck out Jason's suite of newsletters: https://substack.com/@calacanisFollow TWiST:Twitter: https://twitter.com/TWiStartupsYouTube: https://www.youtube.com/thisweekinInstagram: https://www.instagram.com/thisweekinstartupsTikTok: https://www.tiktok.com/@thisweekinstartupsSubstack: https://twistartups.substack.comSubscribe to the Founder University Podcast: https://www.youtube.com/@founderuniversity1916

In this episode, Tu and Lei discuss the latest developments in the electric vehicle (EV) and automotive industry in China. They delve into the implications of new payment terms for suppliers, the competitive landscape among Chinese automakers, and the global strategy of BYD. The conversation also touches on the future of internal combustion engines, Tesla's advancements in autonomous driving, and XPeng's AI innovations. Additionally, they address quality control challenges and the regulatory environment affecting the industry.Keywords / Companies:China EVs, automotive industry, payment terms, suppliers, BYD, Tesla, XPeng, quality control, market strategy, electric vehiclesDigital Disruption with Geoff Nielson Discover how technology is reshaping our lives and livelihoods.Listen on: Apple Podcasts Spotify

If you've tried to book a therapy appointment, you know there's a shortage of mental health professionals. It's not unusual to have to wait weeks or months for an appointment. So, with the rise of platforms like Chat GPT, could we harness AI technology for talk therapy purposes to treat things like depression and anxiety? We were extremely skeptical that this topic would even warrant a full episode on the Happy Eating Podcast. But, as you've heard us confess before, we are sometimes pleasantly surprised once we dig into the research. So, did we change our mind after researching this topic? References or Studies Mentioned:    Therabot AI Chatbox (https://www.trytherabot.com/)    Randomized Trial of a Generative AI Chatbot for Mental Health Treatment When ELIZA meets therapists: A Turing test for the heart and mind   Thank you for listening to The Happy Eating Podcast. Tune in weekly on Thursdays for new episodes! For even more Happy Eating, head to our website!  https://www.happyeatingpodcast.com Learn More About Our Hosts:  Carolyn Williams PhD, RD: Instagram: https://www.instagram.com/realfoodreallife_rd/ Website: https://www.carolynwilliamsrd.com Facebook: https://www.facebook.com/RealFoodRealLifeRD/ Brierley Horton, MS, RD Instagram: https://www.instagram.com/brierleyhorton/ Got a question or comment for the pod? Please shoot us a message!  happyeatingpodcast@gmail.com Produced by Lester Nuby OE Productions  

What happens when you give AI researchers unlimited compute and tell them to compete for the highest usage rates? Ben Mann, Co-Founder, from Anthropic sits down with Sarah Guo and Elad Gil to explain how Claude 4 went from "reward hacking" to efficiently completing tasks and how they're racing to solve AI safety before deploying computer-controlling agents. Ben talks about economic Turing tests, the future of general versus specialized AI models, Reinforcement Learning From AI Feedback (RLAIF), and Anthropic's Model Context Protocol (MCP). Plus, Ben shares his thoughts on if we will have Superintelligence by 2028.  Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @8enmann Links:  ai-2027.com/  Chapters: 00:00 Ben Mann Introduction 00:33 Releasing Claude 4 02:05 Claude 4 Highlights and Improvements 03:42 Advanced Use Cases and Capabilities 06:42 Specialization and Future of AI Models 09:35 Anthropic's Approach to Model Development 18:08 Human Feedback and AI Self-Improvement 19:15 Principles and Correctness in Model Training 20:58 Challenges in Measuring Correctness 21:42 Human Feedback and Preference Models 23:38 Empiricism and Real-World Applications 27:02 AI Safety and Ethical Considerations 28:13 AI Alignment and High-Risk Research 30:01 Responsible Scaling and Safety Policies 35:08 Future of AI and Emerging Behaviors 38:35 Model Context Protocol (MCP) and Industry Standards 41:00 Conclusion

Enterprise leaders are no longer asking if they should adopt AI — the question is how to do it effectively. In this episode, Emerj Editorial Director Matthew DeMello speaks with James Raybould, SVP and GM of Turing Intelligence at Turing, about what distinguishes successful enterprise AI deployments from stalled pilots. Turing is one of the world's fastest-growing Artificial Intelligence companies, working with the world's leading AI labs to advance frontier model capabilities and leveraging that work to build real-world AI systems that help businesses solve their toughest problems —delivering real business results, faster, smarter, and at scale. James outlines three critical factors that determine whether an AI initiative gets traction: business alignment, workforce readiness, and infrastructure. He explains why companies that start with the technology — instead of the problem — are likely to fall behind, and what it really means to prepare a workforce for AI when automation changes the expectations of work quality and speed. Want to share your AI adoption story with executive peers? Click emerj.com/expert2 for more information and to be a potential future guest on the ‘AI in Business' podcast! This episode is sponsored by Turing. Learn how brands work with Emerj and other Emerj Media options at emerj.com/ad1.

Ben and Rob test ‘Ex Machina', Alex Garland's sleek, sterile sci-fi thriller where tech anxiety gets seductive and sinister. Domhnall Gleeson plays Caleb, a wide-eyed coder sent to play assistant to Oscar Isaac's Natan, a tech genius channeling Elon Musk if he ever got off the bad stuff; but the star of ‘Ex Machina' is Ava—Alicia Vikander's uncanny AI creation who may or may not be running her own Turing test on everyone. The lads crack open the algorithms behind Ex Machina to find out whether this modern sci-fi masterpiece is Alex Garland's best work—or just a very well-lit panic attack. But who is Alex Garland? Is ‘Ex Machina' a secret bible camp? Given unlimited funds and no oversight, would we really be so much better than Nathan?CONSUUUME to find out all this and much MUCH more!PLUS! We have a Patreon with EXCLUSIVE content just for you starting at just ONE POUND a month - click the link below!Find us on your socials of choice at www.linktr.ee/everymovieeverpodcast

Our guest today is Dr. Ken Forbus, the Walter P. Murphy Professor of Computer Science and a Professor of Education at Northwestern University. Joining Dr. Ken Ford to co-host today's interview is Dr. James Allen, who was IHMC's associate director until he retired a few years ago. James is a founding fellow of the American Association for Artificial Intelligence and a perfect fit for today's discussion with Dr. Forbus, who, like James, is an AI pioneer.  Back in 2022, James was named a fellow by the Association for Computational Linguistics, an organization that studies computational language processing, another field he helped pioneer. Dr. Forbus also is a Fellow of the Association for the Advancement of Artificial Intelligence and was the inaugural winner of the Herbet A. Simon Prize for Advances in Cognitive Systems. He is well-known for his development of the Structure Mapping Engine. In artificial intelligence and cognitive science, the Structure Mapping Engine is a computer simulation of analogy and similarity comparisons that helped pave the way for computers to reason more like humans. Show Notes: [00:03:07] Ken opens the interview with Dr. Forbus by asking if it is true that he had an unusual hobby for a nerdy kid growing up. [00:04:18] James mentions that Dr. Forbus' family moved often when he was younger and asks how that affected him. [00:05:18] Ken mentions that when Dr. Forbus was in high school, he filled his free time reading about psychology and cognition before eventually coming across some articles on AI. Ken asks Dr. Forbus to talk about this experience and what happened next. [00:07:49] James asks Dr. Forbus if he remembers the first computer he owned. [00:09:17] Ken asks Dr. Forbus if there was anything, other than its reputation, that led him to attend MIT. [00:10:09] James mentions that for the past few decades, Dr. Forbus has been working on developing “human like” AI systems. While much of AI research and development has been focused on meeting the standard of the Turing test, James asks Dr. Forbus why he is not a fan of the Turing test. [00:12:24] Ken mentions that Dr. Forbus received his Ph.D. from MIT in 1984, the same year that Apple released the first Macintosh, which was rolled out with a famous Super Bowl ad. This computer was the first successful mouse driven personal computer with a graphical interface. Ken asks Dr. Forbus what he remembers about that ad, and what his reaction to it was at the time. [00:13:22] James mentions that 1984 was also the year that Dr. Forbus made his first splash in the AI world with his paper on qualitative process theory. James goes on to explain that at the time, qualitative reasoning regarding quantities was a major problem for AI. In his paper, Dr. Forbus proposed qualitative process theory as a representational framework for common sense physical reasoning, arguing that understanding common sense physical reasoning first required understanding of processes and their effects and limits. James asks Dr. Forbus to give an overview of this paper and its significance. [00:18:10] Ken asks Dr. Forbus how it was that he ended up marrying one of his collaborators on the Structure Mapping Engine project, Dedre Gentner. [00:19:14] James explains that Dedre's Structure Mapping Theory explains how people understand and reason about relationships between different situations, which is central to human cognition. James asks Dr. Forbus how Dedre's theory was foundational for the Structure Mapping Engine (SME). [00:25:19] Ken mentions how SME has gone through a number of changes and improvements over the years, as documented in Dr. Forbus' 2016 paper “Extending SME to handle large scale cognitive modeling.” Ken asks, as a cognitive model, what evidence Dr. Forbus has used to argue for the psychological and cognitive plausibility of SME. [00:30:00] Ken explains that many AI systems rely on deep learning,

Tim and Mary Danielsen dive into headlines once again, if only to prove we are not robots but real humans. Is everyone sick of trying to prove they are flesh and blood? Or hoping that the pictures you click on will pass the test? This system is actually called CAPTCHA (Completely Automated Public Turing Test to tell Computers and Humans Apart). It is a computing test to determine if the user is human. It is sometimes called the reverse Turing test, as the point is to prove humanity rather than artificial intelligence. I for one believe that the burden of proof is on the Artificial Lifeform to come forth and identify themselves. Today we talk about that alternate reality that is the internet. Patrick Wood has for some time now said that reality barely exists anyway, so we will look at what that means. We also look at Canadian elections; the Pope as Muslim apologist; an update on the Temple Mount; catastrophic AI; Facial Recognition perils; and the need to find HOPE in all of it. After all, our hope isn't in prophetic fulfillment, it is in a person - the Lord Jesus Christ, Lord of all. A full hour regarding life on planet earth in April 2025. Stand Up For The Truth Videos: https://rumble.com/user/CTRNOnline & https://www.youtube.com/channel/UCgQQSvKiMcglId7oGc5c46A

The horrible things people get programmed to believe. They have been programmed to fight for an evil cause. Jasmine Crockett’s “magic armor’. Where does the mass programing of people lead us? Turing into an old man.See omnystudio.com/listener for privacy information.