Podcasts about master algorithm

This episode is sponsored by Thuma. Thuma is a modern design company that specializes in timeless home essentials that are mindfully made with premium materials and intentional details. To get $100 towards your first bed purchase, go to http://thuma.co/eyeonai   Can AI Ever Reach AGI? Pedro Domingos Explains the Missing Link In this episode of Eye on AI, renowned computer scientist and author of The Master Algorithm, Pedro Domingos, breaks down what's still missing in our race toward Artificial General Intelligence (AGI) — and why the path forward requires a radical unification of AI's five foundational paradigms: Symbolists, Connectionists, Bayesians, Evolutionaries, and Analogizers.   Topics covered: Why deep learning alone won't achieve AGI How reasoning by analogy could unlock true machine creativity The role of evolutionary algorithms in building intelligent systems Why transformers like GPT-4 are impressive—but incomplete The danger of hype from tech leaders vs. the real science behind AGI What the Master Algorithm truly means — and why we haven't found it yet Pedro argues that creativity is easy, reliability is hard, and that reasoning by analogy — not just scaling LLMs — may be the key to Einstein-level breakthroughs in AI.   Whether you're an AI researcher, machine learning engineer, or just curious about the future of artificial intelligence, this is one of the most important conversations on how to actually reach AGI.    

This episode is sponsored by Indeed.  Stop struggling to get your job post seen on other job sites. Indeed's Sponsored Jobs help you stand out and hire fast. With Sponsored Jobs your post jumps to the top of the page for your relevant candidates, so you can reach the people you want faster. Get a $75 Sponsored Job Credit to boost your job's visibility! Claim your offer now: https://www.indeed.com/EYEONAI     In this episode, renowned AI researcher Pedro Domingos, author of The Master Algorithm, takes us deep into the world of Connectionism—the AI tribe behind neural networks and the deep learning revolution.   From the birth of neural networks in the 1940s to the explosive rise of transformers and ChatGPT, Pedro unpacks the history, breakthroughs, and limitations of connectionist AI. Along the way, he explores how supervised learning continues to quietly power today's most impressive AI systems—and why reinforcement learning and unsupervised learning are still lagging behind.   We also dive into: The tribal war between Connectionists and Symbolists The surprising origins of Backpropagation How transformers redefined machine translation Why GANs and generative models exploded (and then faded) The myth of modern reinforcement learning (DeepSeek, RLHF, etc.) The danger of AI research narrowing too soon around one dominant approach Whether you're an AI enthusiast, a machine learning practitioner, or just curious about where intelligence is headed, this episode offers a rare deep dive into the ideological foundations of AI—and what's coming next. Don't forget to subscribe for more episodes on AI, data, and the future of tech.     Stay Updated: Craig Smith on X:https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI     (00:00) What Are Generative Models? (03:02) AI Progress and the Local Optimum Trap (06:30) The Five Tribes of AI and Why They Matter (09:07) The Rise of Connectionism (11:14) Rosenblatt's Perceptron and the First AI Hype Cycle (13:35) Backpropagation: The Algorithm That Changed Everything (19:39) How Backpropagation Actually Works (21:22) AlexNet and the Deep Learning Boom (23:22) Why the Vision Community Resisted Neural Nets (25:39) The Expansion of Deep Learning (28:48) NetTalk and the Baby Steps of Neural Speech (31:24) How Transformers (and Attention) Transformed AI (34:36) Why Attention Solved the Bottleneck in Translation (35:24) The Untold Story of Transformer Invention (38:35) LSTMs vs. Attention: Solving the Vanishing Gradient Problem (42:29) GANs: The Evolutionary Arms Race in AI (48:53) Reinforcement Learning Explained (52:46) Why RL Is Mostly Just Supervised Learning in Disguise (54:35) Where AI Research Should Go Next  

This episode is sponsored by Thuma.   Thuma is a modern design company that specializes in timeless home essentials that are mindfully made with premium materials and intentional details.   To get $100 towards your first bed purchase, go to http://thuma.co/eyeonai In this episode of the Eye on AI podcast, Pedro Domingos—renowned AI researcher and author of The Master Algorithm—joins Craig Smith to break down the Symbolist approach to artificial intelligence, one of the Five Tribes of Machine Learning. Pedro explains how Symbolic AI dominated the field for decades, from the 1950s to the early 2000s, and why it's still playing a crucial role in modern AI. He dives into the Physical Symbol System Hypothesis, the idea that intelligence can emerge purely from symbol manipulation, and how AI pioneers like Marvin Minsky and John McCarthy built the foundation for rule-based AI systems. The conversation unpacks inverse deduction—the Symbolists' "Master Algorithm"—and how it allows AI to infer general rules from specific examples. Pedro also explores how decision trees, random forests, and boosting methods remain some of the most powerful AI techniques today, often outperforming deep learning in real-world applications. We also discuss why expert systems failed, the knowledge acquisition bottleneck, and how machine learning helped solve Symbolic AI's biggest challenges. Pedro shares insights on the heated debate between Symbolists and Connectionists, the ongoing battle between logic-based reasoning and neural networks, and why the future of AI lies in combining these paradigms. From AlphaGo's hybrid approach to modern AI models integrating logic and reasoning, this episode is a deep dive into the past, present, and future of Symbolic AI—and why it might be making a comeback. Don't forget to like, subscribe, and hit the notification bell for more expert discussions on AI, technology, and the future of intelligence!   Stay Updated: Craig Smith Twitter: https://twitter.com/craigss Eye on A.I. Twitter: https://twitter.com/EyeOn_AI   (00:00) Pedro Domingos onThe Five Tribes of Machine Learning   (02:23) What is Symbolic AI?   (04:46) The Physical Symbol System Hypothesis Explained   (07:05) Understanding Symbols in AI   (11:51) What is Inverse Deduction?   (15:10) Symbolic AI in Medical Diagnosis   (17:35) The Knowledge Acquisition Bottleneck   (19:05) Why Symbolic AI Struggled with Uncertainty   (20:40) Machine Learning in Symbolic AI – More Than Just Connectionism   (24:08) Decision Trees & Their Role in Symbolic Learning   (26:55) The Myth of Feature Engineering in Deep Learning   (30:18) How Symbolic AI Invents Its Own Rules   (31:54) The Rise and Fall of Expert Systems – The CYCL Project   (38:53) Symbolic AI vs. Connectionism   (41:53) Is Symbolic AI Still Relevant Today?   (43:29) How AlphaGo Combined Symbolic AI & Neural Networks   (45:07) What Symbolic AI is Best At – System 2 Thinking   (47:18) Is GPT-4o Using Symbolic AI?   

This episode is sponsored by Thuma. Thuma is a modern design company that specializes in timeless home essentials that are mindfully made with premium materials and intentional details. To get $100 towards your first bed purchase, go to http://thuma.co/eyeonai   In this episode of the Eye on AI podcast, Pedro Domingos, renowned AI researcher and author of The Master Algorithm, joins Craig Smith to explore the evolution of machine learning, the resurgence of Bayesian AI, and the future of artificial intelligence. Pedro unpacks the ongoing battle between Bayesian and Frequentist approaches, explaining why probability is one of the most misunderstood concepts in AI. He delves into Bayesian networks, their role in AI decision-making, and how they powered Google's ad system before deep learning. We also discuss how Bayesian learning is still outperforming humans in medical diagnosis, search & rescue, and predictive modeling, despite its computational challenges. The conversation shifts to deep learning's limitations, with Pedro revealing how neural networks might be just a disguised form of nearest-neighbor learning. He challenges conventional wisdom on AGI, AI regulation, and the scalability of deep learning, offering insights into why Bayesian reasoning and analogical learning might be the future of AI. We also dive into analogical learning—a field championed by Douglas Hofstadter—exploring its impact on pattern recognition, case-based reasoning, and support vector machines (SVMs). Pedro highlights how AI has cycled through different paradigms, from symbolic AI in the '80s to SVMs in the 2000s, and why the next big breakthrough may not come from neural networks at all. From theoretical AI debates to real-world applications, this episode offers a deep dive into the science behind AI learning methods, their limitations, and what's next for machine intelligence. Don't forget to like, subscribe, and hit the notification bell for more expert discussions on AI, technology, and the future of innovation!    Stay Updated: Craig Smith Twitter: https://twitter.com/craigss Eye on A.I. Twitter: https://twitter.com/EyeOn_AI (00:00) Introduction (02:55) The Five Tribes of Machine Learning Explained   (06:34) Bayesian vs. Frequentist: The Probability Debate   (08:27) What is Bayes' Theorem & How AI Uses It   (12:46) The Power & Limitations of Bayesian Networks   (16:43) How Bayesian Inference Works in AI   (18:56) The Rise & Fall of Bayesian Machine Learning   (20:31) Bayesian AI in Medical Diagnosis & Search and Rescue   (25:07) How Google Used Bayesian Networks for Ads   (28:56) The Role of Uncertainty in AI Decision-Making   (30:34) Why Bayesian Learning is Computationally Hard   (34:18) Analogical Learning – The Overlooked AI Paradigm   (38:09) Support Vector Machines vs. Neural Networks   (41:29) How SVMs Once Dominated Machine Learning   (45:30) The Future of AI – Bayesian, Neural, or Hybrid?   (50:38) Where AI is Heading Next  

Will we ever have an AI for president? In this episode of the podcast I speak with Pedro Domingos about the impact of AI on society, industry, and politics. Pedro is professor emeritus of computer science and engineering at the University of Washington and co-founded the International Machine Learning Society. He is also the Author of `The Master Algorithm' and `2040: A Silicon Valley Satire'. This episode is not a paid advertisement for Pedro's books, but we use his book `2040' to set the context for the discussion.  We discuss the hype and fear surrounding AI and the future of tech, and Pedro gives an insiders view into the realities of AI development and impact. In his view AI is a human made tool. It isn't going to take over the planet like the terminator, but it will be something that is used by humans to shift the balance of power in society, industry, and politics. A particularly interesting aspect of the discussion surrounds digital twins. Imagine a world in which dating apps are replaced with a digital platform in which your digital twin simulates dates, and even entire lives with the digital twins of potential partners. Users would then go on physical dates with the top performing selection. This same idea could be extended to predicting crime, presidential election outcomes, and more. All of a sudden the simulation hypothesis doesn't seem to crazy, when simulations are used to predict future outcomes in the real world. Another key topic in this conversation is that of `wokism'. Pedro discusses `wokism' as a new iteration of cultural marxism, an ideology that reinterprets the dynamics of class struggle through cultural and identity-based lenses. While the creation of wealth in a society is not inherently a zero-sum game—where one person's gain is necessarily another's loss— the division and redistribution of that wealth that has been created is a zero-sum conflict. Pedro suggests that `wokism' is an attempt to shift power and resources within society, but where the division between the oppressed and the privileged is identity based. ►Watch on YouTube: https://youtu.be/LjBZc-Zh4bc ►Find out more about Pedro's work here: https://en.wikipedia.org/wiki/Pedro_Domingos https://scholar.google.com/citations?user=KOrhfVMAAAAJ&hl=en https://homes.cs.washington.edu/~pedrod/ ►Follow Pedro on Twitter: @pmddomingos These conversations are supported by the Andrea von Braun foundation (http://www.avbstiftung.de/), as an exploration of the rich, exciting, connected, scientifically literate, and (most importantly) sustainable future of humanity. The Andrea von Braun Foundation has provided me with full creative freedom with their support. As such, the views expressed in these episodes are my own and/or those of my guests.  

Pedro Domingos is a renowned AI researcher, tech industry insider, and Professor Emeritus of Computer Science and Engineering at the University of Washington. An expert in deep learning, neural networks, and AI ethics, Pedro's research work encompasses some of the most cutting edge concepts like the black box of deep networks, and Markov Logic Network. Pedro is the author of the best-selling book ‘The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World' (Basic Books, 2015), which has been translated into over twelve languages and sold over 300,000 copies.His latest book, ‘2040: A Silicon Valley Satire', published in August 2024, is a novel that imagines the 2040 presidential election, where the Republican candidate is an AI named PresiBot, and the Democratic candidate is a fake Native American chief seeking to abolish the United States. Pedro is the author/co-author of over 200 technical publications in machine learning, data science, and other areas. He's a member of the editorial board of the Machine Learning journal, co-founder of the International Machine Learning Society, and past associate editor of JAIR.To read more about Pedro Domingos, visit https://businessabc.net/wiki/pedro-do...The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World is a book by Pedro Domingos released in 2015.The book outlines five approaches of machine learning: inductive reasoning, Connectionism, evolutionary computation, Bayes' theorem, analogical modelling. Pedro explains these tribes to the reader by referring to more understandable processes of logic, connections made in the brain, natural selection, probability and similarity judgments. The book suggests that each different tribe has the potential to contribute to a unifying "master algorithm".Towards the end of the book the author pictures a "master algorithm" in the near future, where machine learning algorithms asymptotically grow to a perfect understanding of how the world and people in it work. Although the algorithm doesn't yet exist, he briefly reviews his own invention of the Markov logic network.In 2016 Bill Gates recommended the book, alongside Nick Bostrom's Superintelligence, as one of two books everyone should read to understand AI.2040: A Silicon Valley SatireIn a chaotic 2040 presidential race, an AI candidate named PresiBot is pitted against a Native American chief determined to dismantle the United States. The mastermind behind PresiBot is Ethan, the struggling CEO of KumbAI, a tech startup that created the AI as a last-ditch publicity stunt. However, with PresiBot prone to malfunctioning and spouting hallucinations, the nation's fate hangs by a thread.Set against a backdrop of a dystopian, tech-dominated San Francisco, 2040 is a fast-paced, satirical narrative that skewers the modern American landscape. From the overwhelming influence of Silicon Valley's tech giants to the fear-mongering surrounding AI and the increasingly bizarre political movements, the novel paints a sharp, witty critique of the near future. With razor-sharp dialogue and a plot that mirrors today's concerns, 2040 offers a darkly comedic reflection on where we might be headed.Support the show

For video version watch the episode on: → YouTube link: https://youtu.be/9-J4eV8qvZg Join us as we dive deep into the world of AI with Pedro Domingos, a machine learning pioneer and author of the acclaimed book "The Master Algorithm." Pedro shares his insights on the current AI hype cycle, the real impact of AI on society, and how he sees the future of this transformative technology. Guest on the episode: Pedro Domingos (Author and Machine Learning Professor) → Website: pedrodomingos.org → Linkedin: https://www.linkedin.com/in/pedro-domingos-77b183/ → Twitter: https://x.com/pmddomingos → Read Pedro's new book 2040: https://2040novel.com/ Host: Nataraj (Investor at Incisive VC, angel investor, and Senior Product Manager) → Linkedin: https://www.linkedin.com/in/natarajsindam/ → Twitter: https://x.com/natarajsindam → Email updates: https://startupproject.beehiiv.com/ → Website: ⁠⁠https://thestartupproject.io⁠⁠ In this episode, we discuss: 00:00 - Introduction:** Meet Pedro Domingos, a legend in the field of machine learning. 03:28 - Early Career:** Learn how Pedro got into machine learning in the 80s, a time when it wasn't considered "cool". 08:17 - AI vs. Machine Learning:** Pedro clarifies the difference between these two terms and explains why machine learning is the driving force behind all other AI applications. 13:26 - The AI Hype Cycle:** Pedro discusses the rapid progress of AI, but also warns of the potential for a stock market bubble if the hype exceeds the reality. 21:45 - LLMs and the Future of AI:** Pedro analyzes whether the current hype around LLMs will lead to AGI and speculates on what breakthroughs might be needed to get there. 31:44 - The Master Algorithm:** Pedro talks about the purpose and scope of his book, "The Master Algorithm," and how it offers a broader understanding of AI beyond just LLMs. 42:29 - Open AI and the Comedy of Errors:** Pedro dives into the hype surrounding Open AI and its perceived dominance in the AI space, sharing his perspective on their overestimation. 54:25 - AI Safety and the Call for a Moratorium:** Pedro dismisses the fear-mongering around AI's supposed threat to humanity and provides a rational perspective on the current AI safety concerns. 1:03:19 - AI in 2040:** Pedro discusses his satirical book, "2040," which paints a humorous, yet thought-provoking, picture of how AI might impact society. 1:14:18 - Recommendation Systems and Human Psychology:** Pedro examines the limitations of current recommendation systems and argues for a shift towards systems that give users more control over their information consumption. 1:24:38 - The Importance of Critical Information Consumption:** Pedro shares his strategies for staying informed in the age of information overload, emphasizing the need for a balanced and diverse intake of information. 1:38:59 - The Importance of Choosing the Right Problem:** Pedro stresses the importance of finding research problems that align with your passion and potential impact, rather than chasing the latest trends. Don't forget to like and subscribe for more insightful conversations about the world of AI! → YouTube: https://youtu.be/9-J4eV8qvZg→ Spotify: https://open.spotify.com/episode/3Og8mbra1cokQ5cRJdjZn1?si=iqEOqKLLSqSbk8ehkniFqg→ Apple podcasts: https://podcasts.apple.com/us/podcast/85-ai-should-not-be-regulated-author-ml-researcher/id1551300319?i=1000673806783→ Email updates: https://startupproject.beehiiv.com/ → Others: https://spotifyanchor-web.app.link/e/qYaG6vhTRNb #AI #machinelearning #themasteralgorithm #llm #agi #futureofai #tech #society #2040 #openai #google #facebook #recommendation #information #research #podcasts #startups #sharp ratio

Todd Ault interviews Pedro Domingos, a world-renowned AI researcher, tech-industry insider, and the author of the best-selling book The Master Algorithm. This groundbreaking scientific book, released in 2017, introduced machine learning to a wide audience and was recommended by Bill Gates as "required reading for everyone to understand AI". Pedro's latest book is titled 2040: A Silicon Valley Satire,  which explores the concept of an AI robot running for President. Tune in to hear Pedro provide insight into the origins of algorithms, their ethics, challenging limitations, and why backpropagation is driving the AI revolution.    "The goal of the book [2040: A Silicon Valley Satire] is for it to be a cautionary tale. In a sense, it's a caricature of where we are now. But it's also saying to people 'Look if we don't do anything - this is where we're headed' and it's not a good place."Pedro Domingos

This episode is sponsored by Bloomreach. Bloomreach is a cloud-based e-commerce experience platform and B2B service specializing in marketing automation, product discovery, and content management systems.   Check out Bloomreach: https://www.bloomreach.com Explore Loomi AI: https://www.bloomreach.com/en/products/loomi Other Bloomreach products: https://www.bloomreach.com/en/products     In this episode of the Eye on AI podcast, we sit down with Pedro Domingos, professor of computer science and author of The Master Algorithm and 2040, to dive deep into the future of artificial intelligence, machine learning, and AI governance.   Pedro shares his expertise in AI, offering a unique perspective on the real dangers and potential of AI, far from the apocalyptic fears of superintelligence taking over. We explore his satirical novel, 2040, where an AI candidate for president—Prezibot—raises questions about control, democracy, and the flaws in both AI systems and human decision-makers.   Throughout the episode, Pedro sheds light on Silicon Valley's utopian dreams clashing with its dystopian realities, highlighting the contrast between tech innovation and societal challenges like homelessness. He discusses how AI has already integrated into our daily lives, from recommendation systems to decision-making tools, and what this means for the future.   We also unpack the ongoing debate around AI safety, the limits of current AI models like ChatGPT, and why he believes AI is more of a tool to amplify human intelligence rather than an existential threat. Pedro offers his insights into the future of AI development, focusing on how symbolic AI and neural networks could pave the way for more reliable and intelligent systems.   Don't forget to like, subscribe, and hit the notification bell to stay updated on the latest insights into AI, machine learning, and tech culture.   Stay Updated: Craig Smith Twitter: https://twitter.com/craigss Eye on A.I. Twitter: https://twitter.com/EyeOn_AI     (00:00) Preview and Introduction (01:06) Pedro's Background and Contributions to AI (03:36) The Satirical Take on AI in '2040' (05:42) AI Safety Debate: Geoffrey Hinton vs. Yann LeCun (08:06) Debunking AI's Real Risks (12:45) Satirical Elements in '2040': HappyNet and Prezibot (17:57) AI as a Decision-Making Tool: Potential and Risks (22:55) The Limits of AI as an Arbiter of Truth (27:35) Crowdsourced AI: PreziBot 2.0 and Real-Time Decision Making (29:54) AI Governance and the Kill Switch Debate (37:42) Integrating AI into Society: Challenges and Optimism (47:11) Pedro's Current Research and Future of AI (55:17) Scaling AI and the Future of Reinforcement Learning  

Our guest today is Pedro Domingos, who is joining an elite group of repeat guests – he joined us before in episode 34 in April 2023.Pedro is Professor Emeritus Of Computer Science and Engineering at the University of Washington. He has done pioneering work in machine learning, like the development of Markov logic networks, which combine probabilistic reasoning with first-order logic. He is probably best known for his book "The Master Algorithm" which describes five different "tribes" of AI researchers, and argues that progress towards human-level general intelligence requires a unification of their approaches.More recently, Pedro has become a trenchant critic of what he sees as exaggerated claims about the power and potential of today's AI, and of calls to impose constraints on it.He has just published “2040: A Silicon Valley Satire”, a novel which ridicules Big Tech and also American politics.Selected follow-ups:Pedro Domingos - University of WashingtonPrevious London Futurists Podcast episode featuring Pedro Domingos2040: A Silicon Valley SatireThe Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our WorldThe Bonfire of the VanitiesRon HowardMike JudgeMartin ScorsesePandora's BrainTranscendenceFuture of Life Institute moratorium open letterOpenAI working on new reasoning technology under code name ‘Strawberry'Artificial Intelligence: A Modern Approach - by Stuart Russell and Peter NorvigGoogle's AI reasons its way around the London Underground - NatureConsciumIs LaMDA Sentient? — an Interview - by Blake LemoineCould a Large Language Model be Conscious? - Talk by David Chalmers at NeurIPS 2022Jeremy BenthamThe Extended Phenotype - 1982 book by Richard DawkinsClarion West: Workshops for people who are serious about writingMusic: Spike Protein, by Koi Discovery, available under CC0 1.0 Public Domain Declaration

As the author of the bestselling Master Algorithm, University of Washington professor Pedro Domingos is one of the world's most respected AI experts. So I was a little surprised that his new book, 2040, is a science-fictional satire of American politics & Silicon Valley. In 2040, Domingos is, however, also using fiction to write a critique of the current Silicon Valley mania for AI. The book is both a warning about the technological limits of AI as well as an investigation of the way that it could truly democratize American politics. And so, by 2040, Domingos promised me, we really might be close to the reality of what he calls “an agora in a Presibot”. Pedro Domingos is a leading AI researcher and the author of the worldwide bestseller "The Master Algorithm", an introduction to machine learning for a general audience. He is a professor of computer science at the University of Washington in Seattle. He won the SIGKDD Innovation Award and the IJCAI John McCarthy Award, two of the highest honors in data science and AI, and is a Fellow of AAAS and AAAI. He has written for the Wall Street Journal, Scientific American, Wired, and others, and is a highly sought-after speaker.Named as one of the "100 most connected men" by GQ magazine, Andrew Keen is amongst the world's best known broadcasters and commentators. In addition to presenting KEEN ON, he is the host of the long-running How To Fix Democracy show. He is also the author of four prescient books about digital technology: CULT OF THE AMATEUR, DIGITAL VERTIGO, THE INTERNET IS NOT THE ANSWER and HOW TO FIX THE FUTURE. Andrew lives in San Francisco, is married to Cassandra Knight, Google's VP of Litigation & Discovery, and has two grown children.Keen On is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit keenon.substack.com/subscribe

This and all episodes at: https://aiandyou.net/ . Mounir Shita, CEO of Kimera Systems, is author of the upcoming book The Science of Intelligence, which contains some interesting and thought-provoking explorations of intelligence that had me thinking about Pedro Domingos' book The Master Algorithm. We talk about theories of AGI, free will, egg smashing, and Mounir's prototype smartphone app that learned how to silence itself in a movie theater! All this plus our usual look at today's AI headlines. Transcript and URLs referenced at HumanCusp Blog.          

"How The Quest For The Ultimate Learning Machine Will Remake Our World"

The AI Asia Pacific Institute (AIAPI) is hosting a series of conversations with leading artificial intelligence (AI) experts to study ChatGPT and its risks, looking to arrive at tangible recommendations for regulators and policymakers. These experts include Dr. Toby Walsh, Dr. Stuart Russell, Dr. Pedro Domingos, and Dr. Luciano Floridi, as well as our internal advisory board and research affiliates. We have published a briefing note outlining some of the critical risks of generative AI and highlighting potential concerns. The following is a conversation with Dr. Pedro Domingos.  Dr. Pedro Domingos is a professor emeritus of computer science and engineering at the University of Washington and the author of The Master Algorithm. He is a winner of the SIGKDD Innovation Award and the IJCAI John McCarthy Award, two of the highest honors in data science and AI. He is a Fellow of the AAAS and AAAI, and has received an NSF CAREER Award, a Sloan Fellowship, a Fulbright Scholarship, an IBM Faculty Award, several best paper awards, and other distinctions. Dr. Domingos received an undergraduate degree (1988) and M.S. in Electrical Engineering and Computer Science (1992) from IST, in Lisbon, and an M.S. (1994) and Ph.D. (1997) in Information and Computer Science from the University of California at Irvine. He is the author or co-author of over 200 technical publications in machine learning, data mining, and other areas. He is a member of the editorial board of the Machine Learning journal, co-founder of the International Machine Learning Society, and past associate editor of JAIR. Dr. Domingos was program co-chair of KDD-2003 and SRL-2009, and served on the program committees of AAAI, ICML, IJCAI, KDD, NIPS, SIGMOD, UAI, WWW, and others. He has written for the Wall Street Journal, Spectator, Scientific American, Wired, and others. He helped start the fields of statistical relational AI, data stream mining, adversarial learning, machine learning for information integration, and influence maximization in social networks. *** For show notes and past guests, please visit https://aiasiapacific.org/podcast/ For questions, please contact us at contact@aiasiapacific.org or follow us on Twitter or Instagram to stay in touch.

"Learn to use AI. That's, that's my message. You wanna learn to use AI as a professional and as a citizen in your personal life. The more you know how to use it, the better you'll make of it, the better your life will be. AI gives power; like any technology, it gives power to those who understand it and use it" - Pedro Domingos Recent developments in AI, specifically consumer-facing generative AIs, are helping people create a lot of cool content while also generating a ton of concern. A big bucket of that concern is AI alignment - what are the possible unintended consequences to humans? The internet transformed our relationship to information, but it took a few years; now, AI is doing it in real time.  My guest on this episode is Professor Pedro Domingos.  Pedro is a leading AI researcher and the author of the worldwide bestseller "The Master Algorithm." He is a professor of computer science at the University of Washington in Seattle. He won the Special Interest Group on Knowledge Discovery and Data Mining Innovation Award and the international joint Conference on AI John McCarthy Award, two of the highest honors in data science and AI. Pedro helped start the fields of statistical relational AI, data stream mining, adversarial learning, machine learning for information integration, and influence maximization in social networks.  On this episode, we run the gamut to include... Where are we with generative AIs Pedro demystifies LLMs (Large Language Models) Progress and problems with generative AIs Hallucination in AI - and Illusion in humans The homunculus fallacy Risks, regulations, known-unknowns Comments on existential threats The S curve in emerging technologies like AI AI's possible impact on employment and the economy Artificial General Intellience or AGI Goals and end games, is AGI the goal? Does he think LLMs AI's like ChatGPT are conscious? No matter your technical level, you'll enjoy this discussion with Pedro. He is passionate about the subject matter, no surprise - much of what he's predicted has come to pass in the field, And if you feel a tinge of AI anxiety, consider this a bit of exposure therapy.  Listen and learn more about how these systems work and how they might impact your life.  For show notes and more, visit larryweeks.com  

Pedro Domingos, Professor Emeritus of Computer Science and Engineering at the University of Washington, is renowned for his research in machine learning, particularly for his work on Markov logic networks that allow for uncertain inference. He is also the author of the acclaimed book "The Master Algorithm". Panel: Dr. Tim Scarfe TOC: [00:00:00] Introduction [00:01:34] Galaxtica / misinformation / gatekeeping [00:12:31] Is there a master algorithm? [00:16:29] Limits of our understanding [00:21:57] Intentionality, Agency, Creativity [00:27:56] Compositionality [00:29:30] Digital Physics / It from bit / Wolfram [00:35:17] Alignment / Utility functions [00:43:36] Meritocracy [00:45:53] Game theory [01:00:00] EA/consequentialism/Utility [01:11:09] Emergence / relationalism [01:19:26] Markov logic [01:25:38] Moving away from anthropocentrism [01:28:57] Neurosymbolic / infinity / tensor algerbra [01:53:45] Abstraction [01:57:26] Symmetries / Geometric DL [02:02:46] Bias variance trade off [02:05:49] What seen at neurips [02:12:58] Chalmers talk on LLMs [02:28:32] Definition of intelligence [02:32:40] LLMs [02:35:14] On experts in different fields [02:40:15] Back to intelligence [02:41:37] Spline theory / extrapolation YT version: https://www.youtube.com/watch?v=C9BH3F2c0vQ References; The Master Algorithm [Domingos] https://www.amazon.co.uk/s?k=master+algorithm&i=stripbooks&crid=3CJ67DCY96DE8&sprefix=master+algorith%2Cstripbooks%2C82&ref=nb_sb_noss_2 INFORMATION, PHYSICS, QUANTUM: THE SEARCH FOR LINKS [John Wheeler/It from Bit] https://philpapers.org/archive/WHEIPQ.pdf A New Kind Of Science [Wolfram] https://www.amazon.co.uk/New-Kind-Science-Stephen-Wolfram/dp/1579550088 The Rationalist's Guide to the Galaxy: Superintelligent AI and the Geeks Who Are Trying to Save Humanity's Future [Tom Chivers] https://www.amazon.co.uk/Does-Not-Hate-You-Superintelligence/dp/1474608795 The Status Game: On Social Position and How We Use It [Will Storr] https://www.goodreads.com/book/show/60598238-the-status-game Newcomb's paradox https://en.wikipedia.org/wiki/Newcomb%27s_paradox The Case for Strong Emergence [Sabine Hossenfelder] https://philpapers.org/rec/HOSTCF-3 Markov Logic: An Interface Layer for Artificial Intelligence [Domingos] https://www.morganclaypool.com/doi/abs/10.2200/S00206ED1V01Y200907AIM007 Note; Pedro discussed “Tensor Logic” - I was not able to find a reference Neural Networks and the Chomsky Hierarchy [Grégoire Delétang/DeepMind] https://arxiv.org/abs/2207.02098 Connectionism and Cognitive Architecture: A Critical Analysis [Jerry A. Fodor and Zenon W. Pylyshyn] https://ruccs.rutgers.edu/images/personal-zenon-pylyshyn/proseminars/Proseminar13/ConnectionistArchitecture.pdf Every Model Learned by Gradient Descent Is Approximately a Kernel Machine [Pedro Domingos] https://arxiv.org/abs/2012.00152 A Path Towards Autonomous Machine Intelligence Version 0.9.2, 2022-06-27 [LeCun] https://openreview.net/pdf?id=BZ5a1r-kVsf Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges [Michael M. Bronstein, Joan Bruna, Taco Cohen, Petar Veličković] https://arxiv.org/abs/2104.13478 The Algebraic Mind: Integrating Connectionism and Cognitive Science [Gary Marcus] https://www.amazon.co.uk/Algebraic-Mind-Integrating-Connectionism-D

ChatGPT is both fascinating and weird.Check The Lead-Lag Report on your favorite social networks.Twitter: https://twitter.com/leadlagreportYouTube: https://www.youtube.com/c/theleadlagreportFacebook: https://www.facebook.com/leadlagreportInstagram: https://instagram.com/leadlagreport                             Sign up for The Lead-Lag Report at www.leadlagreport.com and use promo code PODCAST30 for 2 weeks free and 30% off.                              Nothing on this channel should be considered as personalized financial advice or a solicitation to buy or sell any securities.                              The content in this program is for informational purposes only. You should not construe any information or other material as investment, financial, tax, or other advice. The views expressed by the participants are solely their own. A participant may have taken or recommended any investment position discussed, but may close such position or alter its recommendation at any time without notice. Nothing contained in this program constitutes a solicitation, recommendation, endorsement, or offer to buy or sell any securities or other financial instruments in any jurisdiction. Please consult your own investment or financial advisor for advice related to all investment decisions.See disclosures for The Lead-Lag Report here: The Lead-Lag Report (leadlagreport.com)Foodies unite…with HowUdish!It's social media with a secret sauce: FOOD! The world's first network for food enthusiasts. HowUdish connects foodies across the world!Share kitchen tips and recipe hacks. Discover hidden gem food joints and street food. Find foodies like you, connect, chat and organize meet-ups!HowUdish makes it simple to connect through food anywhere in the world.So, how do YOU dish? Download HowUdish on the Apple App Store today:

Leonard S. Johnson is the Founder and CEO of AIEDC, a 5G Cloud Mobile App Maker and Service Provider with Machine Learning to help small and midsize businesses create their own iOS and Android mobile apps with no-code or low-code so they can engage and service their customer base, as well as provide front and back office digitization services for small businesses. Victoria talks to Leonard about using artificial intelligence for good, bringing the power of AI to local economics, and truly democratizing AI. The Artificial Intelligence Economic Development Corporation (AIEDC) (https://netcapital.com/companies/aiedc) Follow AIEDC on Twitter (https://twitter.com/netcapital), Instagram (https://www.instagram.com/netcapital/), Facebook (https://www.facebook.com/Netcapital/), or LinkedIn (https://www.linkedin.com/company/aiedc/). Follow Leonard on Twitter (https://twitter.com/LeonardSJ) and LinkedIn (https://www.linkedin.com/in/leonardsjohnson84047/). Follow thoughtbot on Twitter (https://twitter.com/thoughtbot) or LinkedIn (https://www.linkedin.com/company/150727/). Become a Sponsor (https://thoughtbot.com/sponsorship) of Giant Robots! Transcript: VICTORIA: This is The Giant Robots Smashing Into Other Giant Robots Podcast, where we explore the design, development, and business of great products. I'm your host, Victoria Guido. And with us today is Leonard S. Johnson or LS, Founder and CEO AIEDC, a 5G Cloud Mobile App Maker and Service Provider with Machine Learning to help small and midsize businesses create their own iOS and Android mobile apps with no-code or low-code so they can engage and service their customer base, as well as provide front and back office digitization services for small businesses. Leonard, thanks for being with us today. LEONARD: Thank you for having me, Victoria. VICTORIA: I should say LS, thank you for being with us today. LEONARD: It's okay. It's fine. VICTORIA: Great. So tell us a little more about AIEDC. LEONARD: Well, AIEDC stands for Artificial Intelligence Economic Development Corporation. And the original premise that I founded it for...I founded it after completing my postgraduate work at Stanford, and that was 2016. And it was to use AI for economic development, and therefore use AI for good versus just hearing about artificial intelligence and some of the different movies that either take over the world, and Skynet, and watch data privacy, and these other things which are true, and it's very evident, they exist, and they're out there. But at the end of the day, I've always looked at life as a growth strategy and the improvement of what we could do and focusing on what we could do practically. You do it tactically, then you do it strategically over time, and you're able to implement things. That's why I think we keep building collectively as humanity, no matter what part of the world you're in. VICTORIA: Right. So you went to Stanford, and you're from South Central LA. And what about that background led you to pursue AI for good in particular? LEONARD: So growing up in the inner city of Los Angeles, you know, that South Central area, Compton area, it taught me a lot. And then after that, after I completed high school...and not in South Central because I moved around a lot. I grew up with a single mother, never knew my real father, and then my home life with my single mother wasn't good because of just circumstances all the time. And so I just started understanding that even as a young kid, you put your brain...you utilize something because you had two choices. It's very simple or binary, you know, A or B. A, you do something with yourself, or B, you go out and be social in a certain neighborhood. And I'm African American, so high probability that you'll end up dead, or in a gang, or in crime because that's what it was at that time. It's just that's just a situation. Or you're able to challenge those energies and put them toward a use that's productive and positive for yourself, and that's what I did, which is utilizing a way to learn. I could always pick up things when I was very young. And a lot of teachers, my younger teachers, were like, "You're very, very bright," or "You're very smart." And there weren't many programs because I'm older than 42. So there weren't as many programs as there are today. So I really like all of the programs. So I want to clarify the context. Today there's a lot more engagement and identification of kids that might be sharper, smarter, whatever their personal issues are, good or bad. And it's a way to sort of separate them. So you're not just teaching the whole group as a whole and putting them all in one basket, but back then, there was not. And so I just used to go home a lot, do a lot of reading, do a lot of studying, and just knick-knack with things in tech. And then I just started understanding that even as a young kid in the inner city, you see economics very early, but they don't understand that's really what they're studying. They see economics. They can see inflation because making two ends meet is very difficult. They may see gang violence and drugs or whatever it might end up being. And a lot of that, in my opinion, is always an underlining economic foundation. And so people would say, "Oh, why is this industry like this?" And so forth. "Why does this keep happening?" It's because they can't function. And sometimes, it's just them and their family, but they can't function because it's an economic system. So I started focusing on that and then went into the Marine Corps. And then, after the Marine Corps, I went to Europe. I lived in Europe for a while to do my undergrad studies in the Netherlands in Holland. VICTORIA: So having that experience of taking a challenge or taking these forces around you and turning into a force for good, that's led you to bring the power of AI to local economics. And is that the direction that you went eventually? LEONARD: So economics was always something that I understood and had a fascination prior to even starting my company. I started in 2017. And we're crowdfunding now, and I can get into that later. But I self-funded it since 2017 to...I think I only started crowdfunding when COVID hit, which was 2020, and just to get awareness and people out there because I couldn't go to a lot of events. So I'm like, okay, how can I get exposure? But yeah, it was a matter of looking at it from that standpoint of economics always factored into me, even when I was in the military when I was in the Marine Corps. I would see that...we would go to different countries, and you could just see the difference of how they lived and survived. And another side note, my son's mother is from Ethiopia, Africa. And I have a good relationship with my son and his mother, even though we've been apart for over 15 years, divorced for over 15 years or so or longer. But trying to keep that, you can just see this dichotomy. You go out to these different countries, and even in the military, it's just so extreme from the U.S. and any part of the U.S, but that then always focused on economics. And then technology, I just always kept up with, like, back in the '80s when the mobile brick phone came out, I had to figure out how to get one. [laughs] And then I took it apart and then put it back together just to see how it works, so yeah. But it was a huge one, by the way. I mean, it was like someone got another and broke it, and they thought it was broken. And they're like, "This doesn't work. You could take this piece of junk." I'm like, "Okay." [laughs] VICTORIA: Like, oh, great. I sure will, yeah. Now, I love technology. And I think a lot of people perceive artificial intelligence as being this super futuristic, potentially harmful, maybe economic negative impact. So what, from your perspective, can AI do for local economics or for people who may not have access to that advanced technology? LEONARD: Well, that's the key, and that's what we're looking to do with AIEDC. When you look at the small and midsize businesses, it's not what people think, or their perception is. A lot of those in the U.S. it's the backbone of the United States, our economy, literally. And in other parts of the world, it's the same where it could be a one or two mom-and-pop shops. That's where that name comes from; it's literally two people. And they're trying to start something to build their own life over time because they're using their labor to maybe build wealth or somehow a little bit not. And when I mean wealth, it's always relative. It's enough to sustain themselves or just put food on the table and be able to control their own destiny to the best of their ability. And so what we're looking to do is make a mobile app maker that's 5G that lives in the cloud, that's 5G compliant, that will allow small and midsize businesses to create their own iOS or Android mobile app with no-code or low-code, basically like creating an email. That's how simple we want it to be. When you create your own email, whether you use Microsoft, Google, or whatever you do, and you make it that simple. And there's a simple version, and there could be complexity added to it if they want. That would be the back office digitization or customization, but that then gets them on board with digitization. It's intriguing that McKinsey just came out with a report stating that in 2023, in order to be economically viable, and this was very recent, that all companies would need to have a digitization strategy. And so when you look at small businesses, and you look at things like COVID-19, or the COVID current ongoing issue and that disruption, this is global. And you look at even the Ukrainian War or the Russian-Ukrainian War, however you term it, invasion, war, special operation, these are disruptions. And then, on top of that, we look at climate change which has been accelerating in the last two years more so than it was prior to this that we've experienced. So this is something that everyone can see is self-evident. I'm not even focused on the cause of the problem. My brain and the way I think, and my team, we like to focus on solutions. My chairman is a former program director of NASA who managed 1,200 engineers that built the International Space Station; what was it? 20-30 years ago, however, that is. And he helped lead and build that from Johnson Center. And so you're focused on solutions because if you're building the International Space Station, you can only focus on solutions and anticipate the problems but not dwell on them. And so that kind of mindset is what I am, and it's looking to help small businesses do that to get them on board with digitization and then in customization. And then beyond that, use our system, which is called M.I.N.D. So we own these...we own patents, three patents, trademarks, and service marks related to artificial intelligence that are in the field of economics. And we will utilize DEVS...we plan to do that which is a suite of system specifications to predict regional economic issues like the weather in a proactive way, not reactive. A lot of economic situations are reactive. It's reactive to the Federal Reserve raising interest rates or lowering rates, Wall Street, you know, moving money or not moving money. It is what it is. I mean, I don't judge it. I think it's like financial engineering, and that's fine. It's profitability. But then, at the end of the day, if you're building something, it's like when we're going to go to space. When rockets launch, they have to do what they're intended to do. Like, I know that Blue Origin just blew up recently. Or if they don't, they have a default, and at least I heard that the Blue Origin satellite, if it were carrying passengers, the passengers would have been safe because it disembarked when it detected its own problem. So when you anticipate these kinds of problems and you apply them to the local small business person, you can help them forecast and predict better like what weather prediction has done. And we're always improving that collectively for weather prediction, especially with climate change, so that it can get to near real-time as soon as possible or close a window versus two weeks out versus two days out as an example. VICTORIA: Right. Those examples of what you call a narrow economic prediction. LEONARD: Correct. It is intriguing when you say narrow economic because it wouldn't be narrow AI. But it would actually get into AGI if you added more variables, which we would. The more variables you added in tenancies...so if you're looking at events, the system events discretion so discrete event system specification you would specify what they really, really need to do to have those variables. But at some point, you're working on a system, what I would call AGI. But AGI, in my mind, the circles I run in at least or at least most of the scientists I talk to it's not artificial superintelligence. And so the general public thinks AGI...and I've said this to Stephen Ibaraki, who's the founder of AI for Good at Global Summit at the United Nations, and one of his interviews as well. It's just Artificial General Intelligence, I think, has been put out a lot by Hollywood and entertainment and so forth, and some scientists say certain things. We won't be at artificial superintelligence. We might get to Artificial General Intelligence by 2030 easily, in my opinion. But that will be narrow AI, but it will cover what we look at it in the field as cross-domain, teaching a system to look at different variables because right now, it's really narrow. Like natural language processing, it's just going to look at language and infer from there, and then you've got backward propagation that's credit assignment and fraud and detection. Those are narrow data points. But when you start looking at something cross-domain...who am I thinking of? Pedro Domingos who wrote the Master Algorithm, which actually, Xi Jinping has a copy of, the President of China, on his bookshelf in his office because they've talked about that, and these great minds because Stephen Ibaraki has interviewed these...and the founder of Google Brain and all of these guys. And so there's always this debate in the scientific community of what is narrow AI what it's not. But at the end of the day, I just like Pedro's definition of it because he says the master algorithm will be combining all five, so you're really crossing domains, which AI hasn't done that. And to me, that will be AGI, but that's not artificial superintelligence. And artificial superintelligence is when it becomes very, you know, like some of the movies could say, if we as humanity just let it run wild, it could be crazy. VICTORIA: One of my questions is the future of AI more like iRobot or Bicentennial Man? LEONARD: Well, you know, interesting. That's a great question, Victoria. I see most of AI literally as iRobot, as a tool more than anything, except at the end when it implied...so it kind of did two things in that movie, but a wonderful movie to bring up. And I like Will Smith perfectly. Well, I liked him a lot more before -- VICTORIA: I think iRobot is really the better movie. LEONARD: Yeah, so if people haven't seen iRobot, I liked Will Smith, the actor. But iRobot showed you two things, and it showed you, one, it showed hope. Literally, the robot...because a lot of people put AI and robots. And AI by itself is the brain or the mind; I should say hardware are the robots or the brain. Software...AI in and of itself is software. It's the mind itself. That's why we have M.I.N.D Machine Intelligence NeuralNetwork Database. We literally have that. That's our acronym and our slogan and everything. And it's part of our patents. But its machine intelligence is M.I.N.D, and we own that, you know; the company owns it. And so M.I.N.D...we always say AI powered by M.I.N.D. We're talking about that software side of, like, what your mind does; it iterates and thinks, the ability to think itself. Now it's enclosed within a structure called, you know, for the human, it's called a brain, the physical part of it, and that brain is enclosed within the body. So when you look at robots...and my chairman was the key person for robotics for the International Space Station. So when you look at robotics, you are putting that software into hardware, just like your cell phone. You have the physical, and then you have the actual iOS, which is the operating system. So when you think about that, yeah, iRobot was good because it showed how these can be tools, and they were very, in the beginning of the movie, very helpful, very beneficial to humanity. But then it went to a darker side and showed where V.I.K.I, which was an acronym as well, I think was Virtual Interactive Kinetic technology of something. Yeah, I believe it was Virtual Interactive Kinetic inference or technology or something like that, V.I.K.I; I forgot the last I. But that's what it stood for. It was an acronym to say...and then V.I.K.I just became all aware and started killing everyone with robots and just wanted to say, you know, this is futile. But then, at the very, very end, V.I.K.I learned from itself and says, "Okay, I guess this isn't right." Or the other robot who could think differently argued with V.I.K.I, and they destroyed her. And it made V.I.K.I a woman in the movie, and then the robot was the guy. But that shows that it can get out of hand. But it was intriguing to me that they had her contained within one building. This wouldn't be artificial superintelligence. And I think sometimes Hollywood says, "Just take over everything from one building," no. It wouldn't be on earth if it could. But that is something we always have to think about. We have to think about the worst-case scenarios. I think every prudent scientist or business person or anyone should do that, even investors, I mean, if you're investing something for the future. But you also don't focus on it. You don't think about the best-case scenario, either. But there's a lot of dwelling on the worst-case scenario versus the good that we can do given we're looking at where humanity is today. I mean, we're in 2022, and we're still fighting wars that we fought in 1914. VICTORIA: Right. Which brings me to my next question, which is both, what are the most exciting opportunities to innovate in the AI space currently? And conversely, what are the biggest challenges that are facing innovation in that field? LEONARD: Ooh, that's a good question. I think, in my opinion, it's almost the same answer; one is...but I'm in a special field. And I'm surprised there's not a lot of competition for our company. I mean, it's very good for me and the company's sense. It's like when Mark Zuckerberg did Facebook, there was Friendster, and there was Myspace, but they were different. They were different verticals. And I think Mark figured out how to do it horizontally, good or bad. I'm talking about the beginning of when he started Facebook, now called Meta. But I'm saying utilizing AI in economics because a lot of times AI is used in FinTech and consumerism, but not economic growth where we're really talking about growing something organically, or it's called endogenous growth. Because I studied Paul Romer's work, who won the Nobel Prize in 2018 for economic science. And he talked about the nature of ideas. And we were working on something like that in Stanford. And I put out a book in 2017 of January talking about cryptocurrencies, artificial intelligence but about the utilization of it, but not the speculation. I never talked about speculation. I don't own any crypto; I would not. It's only once it's utilized in its PureTech form will it create something that it was envisioned to do by the protocol that Satoshi Nakamoto sort of created. And it still fascinates me that people follow Bitcoin protocol, even for the tech and the non-tech, but they don't know who Satoshi is. But yeah, it's a white paper. You're just following a white paper because I think logically, the world is going towards that iteration of evolution. And that's how AI could be utilized for good in an area to focus on it with economics and solving current problems. And then going forward to build a new economy where it's not debt-based driven or consumer purchase only because that leaves a natural imbalance in the current world structure. The western countries are great. We do okay, and we go up and down. But the emerging and developing countries just get stuck, and they seem to go into a circular loop. And then there are wars as a result of these things and territory fights and so forth. So that's an area I think where it could be more advanced is AI in the economic realm, not so much the consumer FinTech room, which is fine. But consumer FinTech, in my mind, is you're using AI to process PayPal. That's where I think Elon just iterated later because PayPal is using it for finance. You're just moving things back and forth, and you're just authenticating everything. But then he starts going on to SpaceX next because he's like, well, let me use technology in a different way. And I do think he's using AI on all of his projects now. VICTORIA: Right. So how can that tech solve real problems today? Do you see anything even particular about Southern California, where we're both at right now, where you think AI could help predict some outcomes for small businesses or that community? LEONARD: I'm looking to do it regionally then globally. So I'm part of this Southern Cal Innovation Hub, which is just AI. It's an artificial intelligence coordination between literally San Diego County, Orange County, and Los Angeles County. And so there's a SoCal Innovation Hub that's kind of bringing it together. But there are all three groups, like; I think the CEO in Orange County is the CEO of Leadership Alliance. And then in San Diego, there's another group I can't remember their name off the top of my head, and I'm talking about the county itself. So each one's representing a county because, you know. And then there's one in Northern California that I'm also associated with where if you look at California as its own economy in the U.S., it's still pretty significant as an economic cycle in the United States, period. That's why so many politicians like California because they can sway the votes. So yeah, we're looking to do that once, you know, we are raising capital. We're crowdfunding currently. Our total raise is about 6 million. And so we're talking to venture capitalists, private, high net worth investors as well. Our federal funding is smaller. It's just like several hundred thousand because most people can only invest a few thousand. But I always like to try to give back. If you tell people...if you're Steve Jobs, like, okay, I've got this Apple company. In several years, you'll see the potential. And people are like, ah, whatever, but then they kick themselves 15 years later. [laughs] Like, oh, I wish I thought about that Apple stock for $15 when I could. But you give people a chance, and you get the word out, and you see what happens. Once you build a system, you share it. There are some open-source projects. But I think the open source, like OpenAI, as an example, Elon Musk funds that as well as Microsoft. They both put a billion dollars into it. It is an open-source project. OpenAI claims...but some of the research does go back to Microsoft to be able to see it. And DeepMind is another research for AI, but they're owned by Google. And so, I'm also very focused on democratizing artificial intelligence for the benefit of everyone. I really believe that needs to be democratized in a sense of tying it to economics and making it utilized for everyone that may need it for the benefit of humanity where it's profitable and makes money, but it's not just usurping. MID-ROLL AD: As life moves online, brick-and-mortar businesses are having to adapt to survive. With over 18 years of experience building reliable web products and services, thoughtbot is the technology partner you can trust. We provide the technical expertise to enable your business to adapt and thrive in a changing environment. We start by understanding what's important to your customers to help you transition to intuitive digital services your customers will trust. We take the time to understand what makes your business great and work fast yet thoroughly to build, test, and validate ideas, helping you discover new customers. Take your business online with design‑driven digital acceleration. Find out more at tbot.io/acceleration or click the link in the show notes for this episode. VICTORIA: With that democratizing it, is there also a need to increase the understanding of the ethics around it and when there are certain known use cases for AI where it actually is discriminatory and plays to systemic problems in our society? Are you familiar with that as well? LEONARD: Yes, absolutely. Well, that's my whole point. And, Victoria, you just hit the nail on the head. Truly democratizing AI in my mind and in my brain the way it works is it has opened up for everyone. Because if you really roll it back, okay, companies now we're learning...we used to call it several years ago UGC, User Generated Content. And now a lot of people are like, okay, if you're on Facebook, you're the product, right? Or if you're on Instagram, you're the product. And they're using you, and you're using your data to sell, et cetera, et cetera. But user-generated content it's always been that. It's just a matter of the sharing of the economic. That's why I keep going back to economics. So if people were, you know, you wouldn't have to necessarily do advertising if you had stakeholders with advertising, the users and the company, as an example. If it's a social media company, just throwing it out there, so let's say you have a social media...and this has been talked about, but I'm not the first to introduce this. This has been talked about for over ten years, at least over 15 years. And it's you share as a triangle in three ways. So you have the user and everything else. So take your current social media, and I won't pick on Facebook, but I'll just use them, Facebook, Instagram, or Twitter. Twitter's having issues recently because Elon is trying to buy them or get out of buying them. But you just looked at that data, and then you share with the user base. What's the revenue model? And there needs to be one; let me be very clear. There has to be incentive, and there has to be profitability for people that joined you earlier, you know, joined the corporation, or become shareholders, or investors, or become users, or become customers. They have to be able to have some benefit, not extreme greater than everyone else but a great benefit from coming in earlier by what they contributed at the time. And that is what makes this system holistic in my opinion, like Reddit or any of these bloggers. But you make it where they use their time and the users, and you share it with the company and then the data and so forth, and whatever revenue economic model you have, and it's a sort of a three-way split. It's just not always equal. And that's something that I think in economics, we're still on a zero-sum game, I win, you lose sort of economic model globally. That's why there's a winner of a war and a loser of a war. But in reality, as you know, Victoria, there are no winners of any war. So it's funny, [laughs] I was just saying, well, you know, because of the economic mode, but Von Neumann, who talked about that, also talked about something called a non-zero-sum game when he talked about it in mathematics that you can win, and I can win; we just don't win equally because they never will match that. So if I win, I may win 60; you win 40. Or you may win 60, I win 40, and we agree to settle on that. It's an agreement versus I'm just going to be 99, and you'll be 1%, or I'm just going to be 100, and you're at 0. And I think that our economic model tends to be a lot of that, like, when you push forth and there needs to be more of that. When you talk about the core of economics...and I go way back, you know, prior to the Federal Reserve even being started. I just look at the world, and it's always sort of been this land territorial issue of what goods are under the country. But we've got technology where we can mitigate a lot of things and do the collective of help the earth, and then let's go off to space, all of space. That's where my brain is focused on. VICTORIA: Hmm. Oh yeah, that makes sense to me. I think that we're all going to have to evolve our economic models here in the future. I wonder, too, as you're building your startup and you're building your company, what are some of the technology trade-offs you're having to make in the stack of the AI software that you're building? LEONARD: Hmm. Good question. But clarify, this may be a lot deeper dive because that's a general question. And I don't want to...yeah, go ahead. VICTORIA: Because when you're building AI, and you're going to be processing a lot of data, I know many data scientists that are familiar with tools like Jupyter Notebooks, and R, and Python. And one issue that I'm aware of is keeping the environments the same, so everything that goes into building your app and having those infrastructure as code for your data science applications, being able to afford to process all that data. [laughs] And there are just so many factors that go into building an AI app versus building something that's more easy, like a web-based user form. So just curious if you've encountered those types of trade-offs or questions about, okay, how are we going to actually build an app that we can put out on everybody's phone and that works responsibly? LEONARD: Oh, okay. So let me be very clear, but I won't give too much of the secret sauce away. But I can define this technically because this is a technical audience. This is not...so what you're really talking about is two things, and I'm clear about this, though. So the app maker won't really read and write a lot of data. It'll just be the app where people could just get on board digitalization simple, you know, process payments, maybe connect with someone like American Express square, MasterCard, whatever. And so that's just letting them function. That's sort of small FinTech in my mind, you know, just transaction A to B, B to A, et cetera. And it doesn't need to be peer-to-peer and all of the crypto. It doesn't even need to go that level yet. That's just level one. Then they will sign up for a service, which is because we're really focused on artificial intelligence as a service. And that, to me, is the next iteration for AI. I've been talking about this for about three or four years now, literally, in different conferences and so forth for people who haven't hit it. But that we will get to that point where AI will become AI as a service, just like SaaS is. We're still at the, you know, most of the world on the legacy systems are still software as a service. We're about to hit AI as a service because the world is evolving. And this is true; they did shut it down. But you did have okay, so there are two case points which I can bring up. So JP Morgan did create something called a Coin, and it was using AI. And it was a coin like crypto, coin like a token, but they called it a coin. But it could process, I think, something like...I may be off on this, so to the sticklers that will be listening, please, I'm telling you I may be off on the exact quote, but I think it was about...it was something crazy to me, like 200,000 of legal hours and seconds that it could process because it was basically taking the corporate legal structure of JP Morgan, one of the biggest banks. I think they are the biggest bank in the U.S. JPMorgan Chase. And they were explaining in 2017 how we created this, and it's going to alleviate this many hours of legal work for the bank. And I think politically; something happened because they just pulled away. I still have the original press release when they put it out, and it was in the media. And then it went away. I mean, no implementation [laughs] because I think there was going to be a big loss of jobs for it. And they basically would have been white-collar legal jobs, most specifically lawyers literally that were working for the bank. And when they were talking towards investment, it was a committee. I was at a conference. And I was like, I was fascinated by that. And they were basically using Bitcoin protocol as the tokenization protocol, but they were using AI to process it. And it was basically looking at...because legal contracts are basically...you can teach it with natural language processing and be able to encode and almost output it itself and then be able to speak with each other. Another case point was Facebook. They had...what was it? Two AI systems. They began to create their own language. I don't know if you remember that story or heard about it, and Facebook shut it down. And this was more like two years ago, I think, when they were saying Facebook was talking, you know, when they were Facebook, not Meta, so maybe it was three years ago. And they were talking, and they were like, "Oh, Facebook has a language. It's talking to each other." And it created its own little site language because it was two AI bots going back and forth. And then the engineers at Facebook said, "We got to shut this down because this is kind of getting out of the box." So when you talk about AI as a service, yes, the good and the bad, and what you take away is AWS, Oracle, Google Cloud they do have services where it doesn't need to cost you as much anymore as it used to in the beginning if you know what you're doing ahead of time. And you're not just running iterations or data processing because you're doing guesswork versus, in my opinion, versus actually knowing exactly specifically what you're looking for and the data set you're looking to get out of it. And then you're talking about just basically putting in containers and clustering it because it gets different operations. And so what you're really looking at is something called an N-scale graph data that can process data in maybe sub seconds at that level, excuse me. And one of my advisors is the head of that anyway at AGI laboratory. So he's got an N graph database that can process...when we implement it, we'll be able to process data at the petabyte level at sub-seconds, and it can run on platforms like Azure or AWS, and so forth. VICTORIA: Oh, that's interesting. So it sounds like cloud providers are making compute services more affordable. You've got data, the N-scale graph data, that can run more transactions more quickly. And I'm curious if you see any future trends since I know you're a futurist around quantum computing and how that could affect capacity for -- LEONARD: Oh [laughs] We haven't even gotten there yet. Yes. Well, if you look at N-scale, if you know what you're doing and you know what to look for, then the quantum just starts going across different domains as well but at a higher hit rate. So there's been some quantum computers online. There's been several...well, Google has their quantum computer coming online, and they've been working on it, and Google has enough data, of course, to process. So yeah, they've got that data, lots of data. And quantum needs, you know, if it's going to do something, it needs lots of data. But then the inference will still be, I think, quantum is very good at processing large, large, large amounts of data. We can just keep going if you really have a good quantum computer. But it's really narrow. You have to tell it exactly what it wants, and it will do it in what we call...which is great like in P or NP square or P over NP which is you want to do it in polynomial time, not non-polynomial, polynomial time which is...now speaking too fast. Okay, my brain is going faster than my lips. Let me slow it down. So when you start thinking about processing, if we as humans, let's say if I was going to process A to Z, and I'm like, okay, here is this equation, if I tell you it takes 1000 years, it's of no use to us, to me and you Victoria because we're living now. Now, the earth may benefit in 1000 years, but it's still of no use. But if I could take this large amount of data and have it process within minutes, you know, worst case hours...but then I'll even go down to seconds or sub-seconds, then that's really a benefit to humanity now, today in present term. And so, as a futurist, yes, as the world, we will continue to add data. We're doing it every day, and we already knew this was coming ten years ago, 15 years ago, 20 years ago, even actually in the '50s when we were in the AI winter. We're now in AI summer. In my words, I call it the AI summer. So as you're doing this, that data is going to continue to increase, and quantum will be needed for that. But then the specific need...quantum is very good at looking at a specific issue, specifically for that very narrow. Like if you were going to do the trajectory to Jupiter or if we wanted to send a probe to Jupiter or something, I think we're sending something out there now from NASA, and so forth, then you need to process all the variables, but it's got one trajectory. It's going one place only. VICTORIA: Gotcha. Well, that's so interesting. I'm glad I asked you that question. And speaking of rockets going off to space, have you ever seen a SpaceX launch from LA? LEONARD: Actually, I saw one land but not a launch. I need to go over there. It's not too far from me. But you got to give credit where credit's due and Elon has a reusable rocket. See, that's where technology is solving real-world problems. Because NASA and I have, you know, my chairman, his name is Alexander Nawrocki, you know, he's Ph.D., but I call him Rocki. He goes by Rocki like I go by LS. But it's just we talk about this like NASA's budget. [laughs] How can you reduce this? And Elon says they will come up with a reusable rocket that won't cost this much and be able to...and that's the key. That was the kind of Holy Grail where you can reuse the same rocket itself and then add some little variables on top of it. But the core, you wouldn't constantly be paying for it. And so I think where the world is going...and let me be clear, Elon pushes a lot out there. He's just very good at it. But I'm also that kind of guy that I know that Tesla itself was started by two Stanford engineers. Elon came on later, like six months, and then he invested, and he became CEO, which was a great investment for Elon Musk. And then CEO I just think it just fit his personality because it was something he loved. But I also have studied for years Nikola Tesla, and I understand what his contributions created where we are today with all the patents that he had. And so he's basically the father of WiFi and why we're able to communicate in a lot of this. We've perfected it or improved it, but it was created by him in the 1800s. VICTORIA: Right. And I don't think he came from as fortunate a background as Elon Musk, either. Sometimes I wonder what I could have done born in similar circumstances. [laughter] And you certainly have made quite a name for yourself. LEONARD: Well, I'm just saying, yeah, he came from very...he did come from a poor area of Russia which is called the Russian territory, to be very honest, Eastern Europe, definitely Eastern Europe. But yeah, I don't know once you start thinking about that [laughs]. You're making me laugh, Victoria. You're making me laugh. VICTORIA: No, I actually went camping, a backpacking trip to the Catalina Island, and there happened to be a SpaceX launch that night, and we thought it was aliens because it looked wild. I didn't realize what it was. But then we figured it was a launch, so it was really great. I love being here and being close to some of this technology and the advancements that are going on. I'm curious if you have some thoughts about...I hear a lot about or you used to hear about Silicon Valley Tech like very Northern California, San Francisco focus. But what is the difference in SoCal? What do you find in those two communities that makes SoCal special? [laughs] LEONARD: Well, I think it's actually...so democratizing AI. I've been in a moment like that because, in 2015, I was in Dubai, and they were talking about creating silicon oasis. And so there's always been this model of, you know, because they were always, you know, the whole Palo Alto thing is people would say it and it is true. I mean, I experienced it. Because I was in a two-year program, post-graduate program executive, but we would go up there...I wasn't living up there. I had to go there maybe once every month for like three weeks, every other month or something. But when you're up there, it is the air in the water. It's just like, people just breathe certain things. Because around the world, and I would travel to Japan, and China, and other different parts of Asia, Vietnam, et cetera and in Africa of course, and let's say you see this and people are like, so what is it about Silicon Valley? And of course, the show, there is the Hollywood show about it, which is pretty a lot accurate, which is interesting, the HBO show. But you would see that, and you would think, how are they able to just replicate this? And a lot of it is a convergence. By default, they hear about these companies' access because the key is access, and that's what we're...like this podcast. I love the concept around it because giving awareness, knowledge, and access allows other people to spread it and democratize it. So it's just not one physical location, or you have to be in that particular area only to benefit. I mean, you could benefit in that area, or you could benefit from any part of the world. But since they started, people would go there; engineers would go there. They built company PCs, et cetera. Now that's starting to spread in other areas like Southern Cal are creating their own innovation hubs to be able to bring all three together. And those three are the engineers and founders, and idea makers and startups. And you then need the expertise. I'm older than 42; I'm not 22. [laughs] So I'm just keeping it 100, keeping it real. So I'm not coming out at 19. I mean, my son's 18. And I'm not coming out, okay, this my new startup, bam, give me a billion dollars, I'm good. And let me just write off the next half. But when you look at that, there's that experience because even if you look at Mark Zuckerberg, I always tell people that give credit where credit is due. He brought a senior team with him when he was younger, and he didn't have the experience. And his only job has been Facebook out of college. He's had no other job. And now he's been CEO of a multi-billion dollar corporation; that's a fact. Sometimes it hurts people's feelings. Like, you know what? He's had no other job. Now that can be good and bad, [laughs] but he's had no other jobs. And so that's just a credit, like, if you can surround yourself with the right people and be focused on something, it can work to the good or the bad for your own personal success but then having that open architecture. And I think he's been trying to learn and others versus like an Elon Musk, who embraces everything. He's just very open in that sense. But then you have to come from these different backgrounds. But let's say Elon Musk, Mark Zuckerberg, let's take a guy like myself or whatever who didn't grow up with all of that who had to make these two ends meet, figure out how to do the next day, not just get to the next year, but get to the next day, get to the next week, get to the next month, then get to the next year. It just gives a different perspective as well. Humanity's always dealing with that. Because we had a lot of great engineers back in the early 1900s. They're good or bad, you know, you did have Nikola Tesla. You had Edison. I'm talking about circa around 1907 or 1909, prior to World War I. America had a lot of industries. They were the innovators then, even though there were innovations happening in Europe, and Africa, and China, as well and Asia. But the innovation hub kind of created as the America, quote, unquote, "industrial revolution." And I think we're about to begin a new revolution sort of tech and an industrial revolution that's going to take us to maybe from 20...we're 2022 now, but I'll say it takes us from 2020 to 2040 in my head. VICTORIA: So now that communities can really communicate across time zones and locations, maybe the hubs are more about solving specific problems. There are regional issues. That makes a lot more sense. LEONARD: Yes. And collaborating together, working together, because scientists, you know, COVID taught us that. People thought you had to be in a certain place, but then a lot of collaboration came out of COVID; even though it was bad globally, even though we're still bad, if people were at home, they start collaborating, and scientists will talk to scientists, you know, businesses, entrepreneurs, and so forth. But if Orange County is bringing together the mentors, the venture capital, or at least Southern California innovation and any other place, I want to say that's not just Silicon Valley because Silicon Valley already has it; we know that. And that's that region. It's San Jose all the way up to...I forgot how far north it's past San Francisco, actually. But it's that region of area where they encompass the real valley of Silicon Valley if you're really there. And you talk about these regions. Yes, I think we're going to get to a more regional growth area, and then it'll go more micro to actually cities later in the future. But regional growth, I think it's going to be extremely important globally in the very near term. I'm literally saying from tomorrow to the next, maybe ten years, regional will really matter. And then whatever you have can scale globally anyway, like this podcast we're doing. This can be distributed to anyone in the world, and they can listen at ease when they have time. VICTORIA: Yeah, I love it. It's both exciting and also intimidating. [laughs] And you mentioned your son a little bit earlier. And I'm curious, as a founder and someone who spent a good amount of time in graduate and Ph.D. programs, if you feel like it's easy to connect with your son and maintain that balance and focusing on your family while you're building a company and investing in yourself very heavily. LEONARD: Well, I'm older, [laughs] so it's okay. I mean, I've mentored him, you know. And me and his mom have a relationship that works. I would say we have a better relationship now than when we were together. It is what it is. But we have a communication level. And I think she was just a great person because I never knew my real father, ever. I supposedly met him when I was two or one; I don't know. But I have no memories, no photos, nothing. And that was just the environment I grew up in. But with my son, he knows the truth of everything about that. He's actually in college. I don't like to name the school because it's on the East Coast, and it's some Ivy League school; that's what I will say. And he didn't want to stay on the West Coast because I'm in Orange County and his mom's in Orange County. He's like, "I want to get away from both of you people." [laughter] And that's a joke, but he's very independent. He's doing well. When he graduated high school, he graduated with 4.8 honors. He made the valedictorian. He was at a STEM school. VICTORIA: Wow. LEONARD: And he has a high GPA. He's studying computer science and economics as well at an Ivy League, and he's already made two or three apps at college. And I said, "You're not Mark, so calm down." [laughter] But anyway, that was a recent conversation. I won't go there. But then some people say, "LS, you should be so happy." What is it? The apple doesn't fall far from the tree. But this was something he chose around 10 or 11. I'm like, whatever you want to do, you do; I'll support you no matter what. And his mom says, "Oh no, I think you programmed him to be like you." [laughs] I'm like, no, I can't do that. I just told him the truth about life. And he's pretty tall. VICTORIA: You must have -- LEONARD: He played basketball in high school a lot. I'm sorry? VICTORIA: I was going to say you must have inspired him. LEONARD: Yeah. Well, he's tall. He did emulate me in a lot of ways. I don't know why. I told him just be yourself. But yes, he does tell me I'm an inspiration to that; I think because of all the struggles I've gone through when I was younger. And you're always going through struggles. I mean, it's just who you are. I tell people, you know, you're building a company. You have success. You can see the future, but sometimes people can't see it, [laughs] which I shouldn't really say, but I'm saying anyway because I do that. I said this the other night to some friends. I said, "Oh, Jeff Bezo's rocket blew up," going, you know, Blue Origin rocket or something. And then I said Elon will tell Jeff, "Well, you only have one rocket blow up. I had three, [laughter] SpaceX had three." So these are billionaires talking to billionaires about, you know, most people don't even care. You're worth X hundred billion dollars. I mean, they're worth 100 billion-plus, right? VICTORIA: Right. LEONARD: I think Elon is around 260 billion, and Jeff is 160 or something. Who cares about your rocket blowing up? But it's funny because the issues are still always going to be there. I've learned that. I'm still learning. It doesn't matter how much wealth you have. You just want to create wealth for other people and better their lives. The more you search on bettering lives, you're just going to have to wake up every day, be humble with it, and treat it as a new day and go forward and solve the next crisis or problem because there will be one. There is not where there are no problems, is what I'm trying to say, this panacea or a utopia where you personally, like, oh yeah, I have all this wealth and health, and I'm just great. Because Elon has had divorce issues, so did Jeff Bezos. So I told my son a lot about this, like, you never get to this world where it's perfect in your head. You're always going to be doing things. VICTORIA: That sounds like an accurate future prediction if I ever heard one. [laughs] Like, there will be problems. No matter where you end up or what you choose to do, you'll still have problems. They'll just be different. [laughs] LEONARD: Yeah, and then this is for women and men. It means you don't give up. You just keep hope alive, and you keep going. And I believe personally in God, and I'm a scientist who actually does. But I look at it more in a Godly aspect. But yeah, I just think you just keep going, and you keep building because that's what we do as humanity. It's what we've done. It's why we're here. And we're standing on the shoulders of giants, and I just always considered that from physicists and everyone. VICTORIA: Great. And if people are interested in building something with you, you have that opportunity right now to invest via the crowdfunding app, correct? LEONARD: Yes, yes, yes. They can do that because the company is still the same company because eventually, we're going to branch out. My complete vision for AIEDC is using artificial intelligence for economic development, and that will spread horizontally, not just vertically. Vertically right now, just focus on just a mobile app maker digitization and get...because there are so many businesses even globally, and I'm not talking only e-commerce. So when I say small to midsize business, it can be a service business, car insurance, health insurance, anything. It doesn't have to be selling a particular widget or project, you know, product. And I'm not saying there's nothing wrong with that, you know, interest rates and consumerism. But I'm not thinking about Shopify, and that's fine, but I'm talking about small businesses. And there's the back office which is there are a lot of tools for back offices for small businesses. But I'm talking about they create their own mobile app more as a way to communicate with their customers, update them with their customers, and that's key, especially if there are disruptions. So let's say that there have been fires in California. In Mississippi or something, they're out of water. In Texas, last year, they had a winter storm, electricity went out. So all of these things are disruptions. This is just in the U.S., And of course, I won't even talk about Pakistan, what's going on there and the flooding and just all these devastating things, or even in China where there's drought where there are these disruptions, and that's not counting COVID disrupts, the cycle of business. It literally does. And it doesn't bubble up until later when maybe the central banks and governments pay attention to it, just like in Japan when that nuclear, unfortunately, that nuclear meltdown happened because of the earthquake; I think it was 2011. And that affected that economy for five years, which is why the government has lower interest rates, negative interest rates, because they have to try to get it back up. But if there are tools and everyone's using more mobile apps and wearables...and we're going to go to the metaverse and all of that. So the internet of things can help communicate that. So when these types of disruptions happen, the flow of business can continue, at least at a smaller level, for an affordable cost for the business. I'm not talking about absorbing costs because that's meaningless to me. VICTORIA: Yeah, well, that sounds like a really exciting project. And I'm so grateful to have this time to chat with you today. Is there anything else you want to leave for our listeners? LEONARD: If they want to get involved, maybe they can go to our crowdfunding page, or if they've got questions, ask about it and spread the word. Because I think sometimes, you know, they talk about the success of all these companies, but a lot of it starts with the founder...but not a founder. If you're talking about a startup, it starts with the founder. But it also stops with the innovators that are around that founder, male or female, whoever they are. And it also starts with their community, building a collective community together. And that's why Silicon Valley is always looked at around the world as this sort of test case of this is how you create something from nothing and make it worth great value in the future. And I think that's starting to really spread around the world, and more people are opening up to this. It's like the crowdfunding concept. I think it's a great idea, like more podcasts. I think this is a wonderful idea, podcasts in and of themselves, so people can learn from people versus where in the past you would only see an interview on the business news network, or NBC, or Fortune, or something like that, and that's all you would understand. But this is a way where organically things can grow. I think the growth will continue, and I think the future's bright. We just have to know that it takes work to get there. VICTORIA: That's great. Thank you so much for saying that and for sharing your time with us today. I learned a lot myself, and I think our listeners will enjoy it as well. You can subscribe to the show and find notes along with a complete transcript for this episode at giantrobots.fm. If you have questions or comments, email us at hosts@giantrobot.fm. You can find me on Twitter @victori_ousg. This podcast is brought to you by thoughtbot and produced and edited by Mandy Moore. Thanks for listening. See you next time. ANNOUNCER: This podcast was brought to you by thoughtbot. thoughtbot is your expert design and development partner. Let's make your product and team a success. Special Guest: Leonard S. Johnson.

Happy Friday Friends!I'm dropping another good one on you. If the title doesn't speak volumes then this one isn't for you. My next guest is on the way to change the world

Happy Friday Friends!I'm dropping another good one on you. If the title doesn't speak volumes then this one isn't for you. My next guest is on the way to change the world

Today I am joined by Antonio T Smith Jr. Antonio T, Smith, Jr. had an unexpected beginning when, at the age of 6, he became homeless and had to figure out how to take care of himself while living in a trash can.The will to live overrode the trash can. He defeated the trash can in his mind and it kept him alive.It propelled him on a journey of mastering business, sales, marketing leadership, the Law of Attraction and the Law of One.Antonio T. Smith Jr is an American Tech CEO and millionaire, who is on pace to become a billionaire by 2025, with headquarters on four different continents, and is creating 100,000 Millionaires while giving away $1.5 Billion by 2025. Antonio is passionate about artificial intelligence and plans to be the first person to create the Master Algorithm, which he plans to use to create a Resource-based society, that will completely eliminate the need for money.Antonio is building a city to where the people of the light share it and create a harmonious society regardless of race, creed, or religion. Our city will have its own currency, and soon we will become a resource-based economy. We will have democratically elected officials. We will pull our resources and create a society humans deserve with our own police force and our own government. We are the light. We are love. We are you. We are one. Separation is an illusion. We leave you in the light and the love of the one infinite creator. Follow your dreams.We talk about how he came out of homelessness, work he did on his money mindset, his rules of money, and how to sell anything.To connect with Antonio https://www.instagram.com/theatsjr/ See acast.com/privacy for privacy and opt-out information.

Antonio T. Smith, Jr. had an unexpected beginning when, at the age of 6, he became homeless and had to figure out how to take care of himself while living in a trash can. That is the beginning of Antonio's story, but there is so much more. We talked about giving away a billion dollars, the universe, top secret operations, trying to adjust to life after the army, and so much more I can't even begin to describe it all. This is the type of show I love to do. More from Antonio and links: Antonio T. Smith Jr is an American Tech CEO and millionaire, who is on pace to become a billionaire by 2025, with headquarters on four different continents, and is creating 100,000 Millionaires while giving away $1.5 Billion by 2025. Antonio is passionate about artificial intelligence and plans to be the first person to create the Master Algorithm, which he plans to use to create a Resource-based society, that will completely eliminate the need for money. Antonio is building a city to where the people of the light share it and create a harmonious society regardless of race, creed, or religion. Our city will have its own currency, and soon we will become a resource-based economy. We will have democratically elected officials. We will pull our resources and create a society humans deserve with our own police force and our own government. We are the light. We are love. We are you. We are one. Separation is an illusion. We leave you in the light and the love of the one infinite creator. Follow your dreams. Antonio created a podcast with over 850 episodes. This podcast, created and hosted by Antonio T. Smith, Jr. and includes frequent co-host Deaunna M. Smith, is designed to help you develop an excellent attitude, enhance your self-esteem, develop your creative genius, set and achieve goals, harness your mind's power, and explain the elements of personal growth. In addition, this podcast dissects, observes, and gives practical guidance to the critical success factors such as writing skills, public speaking skills, effective communication skills, and fostering excellent relationships. It includes interviews with Millionaires and Billionaires and includes the best of Antonio T. Smith, Jr's teachings. This is the Secret of Success, by Antonio T. Smith, Jr., and it is a daily podcast designed to be your breakthrough. Antonio's Links: Instagram: http://instagram.com/theatsjr  • TikTok: @ats9696 • Facebook: http://facebook.com/theatsjr  • LinkedIn: https://www.linkedin.com/in/antoniots... •► Subscribe to my channel here: http://bit.ly/SubPlzATS • Twitter: http://twitter.com/theatsjr  • Medium: http://medium.com/@theatsjr Music for all episodes by Jon Griffin. My YouTube channel: https://www.youtube.com/channel/UCugOLERePPuD4nwtZO-Zwnw?view_as=subscriber My Instagram: @joelyshmoley and @slideswithjohn FaceBook: https://www.facebook.com/wereyoustilltalking/ #Podcasting #AntonioTSmithJr #Lightbearers #Spiritualality #Allone

Note: there are no politics discussed in this show and please do not interpret this show as any kind of a political statement from us. We have decided not to discuss politics on MLST anymore due to its divisive nature. Patreon: https://www.patreon.com/mlst Discord: https://discord.gg/HNnAwSduud [00:00:00] Intro [00:01:36] What we all need to understand about machine learning [00:06:05] The Master Algorithm Target Audience [00:09:50] Deeply Connected Algorithms seen from Divergent Frames of Reference [00:12:49] There is a Master Algorithm; and it's mine! [00:14:59] The Tribe of Evolution [00:17:17] Biological Inspirations and Predictive Coding [00:22:09] Shoe-Horning Gradient Descent [00:27:12] Sparsity at Training Time vs Prediction Time [00:30:00] World Models and Predictive Coding [00:33:24] The Cartoons of System 1 and System 2 [00:40:37] AlphaGo Searching vs Learning [00:45:56] Discriminative Models evolve into Generative Models [00:50:36] Generative Models, Predictive Coding, GFlowNets [00:55:50] Sympathy for a Thousand Brains [00:59:05] A Spectrum of Tribes [01:04:29] Causal Structure and Modelling [01:09:39] Entropy and The Duality of Past vs Future, Knowledge vs Control [01:16:14] A Discrete Universe? [01:19:49] And yet continuous models work so well [01:23:31] Finding a Discretised Theory of Everything

For a while now, machines have been inseparably tied to our lives. The algorithms on Google, Netflix, Amazon, Xbox, and Tinder have run your life unwittingly. Machines are digesting data that you willingly share with them. Artificial intelligence has also impacted healthcare, from the development of vaccines to the search for a cure for cancer. Machine learning is transforming every aspect of our lives, but what is AI's ultimate foundation?Author and AI expert Pedro Domingos discusses machine learning's five tribes in his book Master Algorithm. During this episode, Pedro shares how the ultimate algorithm can derive knowledge about the past, the present, and the future from data. Listen as he and Greg tackle why such an algorithm should exist and compelling arguments from neuroscience, evolution, physics, statistics, and other branches of computer science.Episode Quotes:Are computer scientists the new age philosophers?I don't think scientists could have supplanted the psychologists and philosophers, and so on. I do think, however, that computer science and machine learning, in particular, changes the way we do everything in a very profound way. If you look at science, more than anything else, its progress is determined by the tools that are available. Galileo was Galileo because he had the telescope. No telescope, no Galileo, and the examples go on. And the thing is that computers are the most extraordinary tool for science, among other things. But for science in particular that we have ever created, they magnify our ability to do things in a way that was —I think — hard to imagine, even 50 years ago.Is machine learning just a bunch of different tools, all trying different approaches to solve the same problems?At the end of the day, the best algorithm is almost never any existing one. What a machine learning algorithm does, it's not magic. It's incorporating knowledge, and knowledge will be different in different domains. There are broad classes of domains where the same knowledge is relevant, and indeed different paradigms tend to do well in different problems. So, deep learning does very well at perceptual problems because, again, you know, these things were inspired by the neurology of the visual system, and et cetera, et cetera.Is the evolutionary model applicable and aligned with what's happening in AI and will there be obstacles in pursuing this line of thinking?There's more to be discovered about how evolution learns. And by the way, there's more to be discovered for the purposes of AI and also for the purposes of understanding evolution. I actually think that if someone really had a supercomputer, that could simulate evolution over a billion years. With the model of evolution that we have today, it would fail. It wouldn't get there. There are some mechanisms that also evolved. But again, this is this interesting series of stages, right? Even within evolution, there are levels of how evolution works. And I think there's a lot of that, that we still don't understand. But we will at some point, and I think that will be beneficial both for biology and for AI.Time Code Guide:00:03:06 How A.I. is revolutionizing the way we think00:04:31 Tycho Brahe stage00:06:44 Is the unified field theory of machine learning the same as the general approach to learning?00:09:11 Computers represent the fourth stage of learning and transmission of knowledge, do you think it's a discontinuity from the first three stages, which all seems to be natural phenomenon?00:10:21 The emergence of AI, life, evolution of the nervous system, and cultures00:12:01 The speed at which computers communicate and facilitate the transfer of Knowledge00:13:10 Possibilities and ways you can play with the computer's processing capacity00:14:29 How did we leap from the AI winter to the AI boom that we have today?00:17:25 Learning machines and self-driving cars00:18:48 AI and Linguistics00:19:33 Do each AI ‘tribe' have a singular view of pursuing a particular approach in AI without acknowledging that it can have limitations later on?00:24:54 One paradigm in AI and Master Algorithm00:27:13 The Rise of the Connectionist00:28:00 What's next for AI?00:33:37 Is it possible to automate the trial and error process and have an algorithm where we learn how to learn?00:37:49 Is the evolutionary model doing anything for AI, and what are the obstacles in this line of thinking?00:41:53 How do we know whether a school of ideas is dead or simply dormant?00:43:01 How do you advance interdisciplinary learning within the different school of thoughts in AI?00:44:24 Thoughts on Geoff Hinton's work and back propagation00:46:22 Is there a guidebook to creating a unified theory?00:48:11 AGI, AI and humans00:51:01 Automating the Scientific Process00:52:26 Thoughts on the Future of AIShow Links:Guest ProfileAcademic ProfileProfile at the International Telecommunications UnionPedro Domingos on TwitterHis WorkPedro Domingos on Google ScholarThe Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our WorldMarkov Logic: An Interface Layer for Artificial Intelligence (Synthesis Lectures on Artificial Intelligence and Machine Learning)

TEMAS: Aubrey de Grey y la ciencia del envejecimiento Estoicismo y la reflexión “Memento Mori” Amor Fati y los obstáculos en el camino Oración de la Serenidad y el Estoicismo La transcendencia del ser Humano Baruch de Spinoza y la Filosofía Humanista La Iluminación (Ilustración) y el Humanismo Dataísmo y las creencias del Siglo XXI Las características de las Revoluciones La narrativa de Bitcoin como moneda del Renacimiento Digital El Pentágono y Simulación de las Guerras Financieras del 2025 Memes y la Teoría del Gen Egoísta (Richard Dawkins) y Ancestor´s Tale Sesión con Mad Cripto (Polkadot), MoonRiver, Remarker NFTs

TEMAS: Introducción al streaming Bitcoin y El Salvador “The Master Algorithm” en la Inteligencia Artificial Iniciativas de los Bancos y Blockchain Coinbase y el sistema financiero Gobierno y el Proof of Violence (PoV) y la prueba de censura Nodos en una red Blockchain y la descentralización Protocolo gossip en las redes Blockchain Internet of Things y Light nodes Sapiens y 21 Lecciones para el Siglo XXI, y Zero to One Fusión en frío (ITER) y los reactores Tokamak La Singularidad de Ray Kurzweil

TEMAS: DotMarketCap y arquitectura Polkadot Noticias en Cripto -Messari, Bankless, The Block, The DeFiant, Cypherpunk Summit Tulum 2022 Microdosing y Cetonas (Ayuno intermitente) Proyecto DoinGud y Alpha leak Ciudades con AI en Shenzhen y la distopía de los pagos digitales Laguna bioluminiscencia en Jamaica (Montego Bay) Halong Bay en Vietnam, la Gran Migración en Africa Skydiving (salto en paracaídas) desde San Marcos TX (Sky Ranch) After Party Techno

ini topik campur aduk (lagi). Semalam saya membedah buku "The Master Algorithm" karangan Pedro Domingos. Kemudian keluh kesah (ramblings) saya terkait dengan situasi saat ini, yang mana orang lebih melihat ke dirinya sendiri.

In this week's episode of Reformed Millennials, Broc and Joel discuss the Buy Now Pay Later craze, especially the Square / Afterpay deal — they revist some surprising advertising spend numbers released from FAANG, talk a bit about a Canadian startup who has interesting take on consumer health tech, and lastly try to get more context on how AMD fits into the narrative of vehicle chip shortages in Canada. Listen on Apple, Spotify, or Google Podcasts.If you aren’t in the Reformed Millennials Facebook Group join us for daily updates, discussions, and deep dives into the investable trends Millennials should be paying attention to.👉 For specific investment questions or advice contact Joel @ Gold Investment Management.📈📊Market Update💵📉Johnson & Johnson trying to break out of a 6-month base.If we're above 172, a trader would want to be long $JNJ with a target up near 210-212.The risk here is very well defined. If we're not breaking out from this base, then we do not want to own it. It's that simple. And we likely don’t want to be too “risk on” across a lot of the aggregate market.When it's going, you'll know. If we're not out of this base, then be patient with it.But the risk vs reward here is very much skewed in favor of the bulls IF we're above 172.The next one Emerging Markets and Regional Banks losing their 2018 highs:This is a big one. If you want one chart to watch as we head into August, this is it.They need to get back above those 2018 highs asap, or else.There's a lot of downsides if they can't recover. Which could also bleed into our North American markets.A kick save - and a beauty, if you will, is what's necessary here for Regional Banks and Emerging Markets.Let's see if we get it.💸Reformed Millennials - Post of The WeekA must-read from Matthew Ball on the MetaverseFrom the article:How Should We Think About the Metaverse and When Will It Emerge?With the above in mind, let’s turn to the Metaverse. The Metaverse is often mis-described as virtual reality. This is like saying the mobile internet is the iPhone. The iPhone isn’t the mobile internet; it’s the consumer hardware and app platform most frequently used to access the mobile internet.Sometimes the Metaverse is described as a virtual user-generated content (UGC) platform. This is like saying the internet is Yahoo!, Facebook, or World of Warcraft. Yahoo! is an internet portal/index, Facebook is a UGC-focused social network, World of Warcraft is an MMO. Other times we receive a more sophisticated explanation, such as ‘the Metaverse is a persistent virtual space enabling continuity of identity and assets’. This is much closer to the truth, but it too is insufficient. It’s a bit like saying the internet is Verizon, or Safari, or HTML. Those are a broadband provider that connects you to the entire web, a web browser that can access/render all of the internet’s webpages from a single screen and IP identifier, and a markup language that enables the creation and display of the web. And certainly, the Metaverse doesn’t mean a game or virtual space where you can hang out (similarly, the Metaverse isn’t now ‘here’ just because more of us now are hanging out virtually and/or more often).Instead, we need to think of the Metaverse as a sort of successor state to the mobile internet. And while consumers will have core devices and platforms through which they interact with the Metaverse, the Metaverse depends on so much more. There’s a reason we don’t say Facebook or Google is an internet. They are destinations and ecosystems on or in the internet, each accessible via a browser or smartphone that can also access the vast rest of the internet. Similarly, Fortnite and Roblox feel like the Metaverse because they embody so many technologies and trends into a single experience that, like the iPhone, is tangible and feels different from everything that came before. But they do not constitute the Metaverse.The China Tech Crackdown - MUST READ Tencent's WeChat has suspended new signups. The $100bn tutoring industry has been told to go non-profit. Didi might delist. Chinese tech indices are down 15% in the last two days. Remember when this was all about Jack Ma? Remember when WeChat was going to go global?I'm not a China analyst, but there's a lot going on, from the CCP asserting its authority to overdue intervention into some under-regulated spaces, with a dose of turf wars as well (what do the financial regulators think of the cyberspace agency deciding who can list overseas?). This also overlaps with China's intensified push for tech sovereignty, with some people suggesting it wants to rebalance from consumer internet to semiconductors and the other primary tech it depends on foreign companies for today (your iPhone is assembled in China, but all the high-value parts are made elsewhere).But if it's not clear what's going on inside China, it's even less clear what this means for the rest of us. Will Chinese Internet giants be forced to make serious efforts to expand internationally? Does the creative torrent of Chinese consumer tech innovation slow down? Could it affect consumer electronics supply chains?If you want to get a feel for whats happening - check out Chinese characteristicsYouTube and Brand AdvertisingA big part of YouTube’s growth in recent years has been the growth of direct response ads; the big problem for companies like NBC, though, and the good news for Google, is that YouTube is starting to make major in-roads into attracting the pot of gold at the end of the TV rainbow — brand advertising. First, CFO Ruth Porat reported that “YouTube advertising revenues of $7.0 billion, were up 84%, driven by brand, followed by direct response.” This was Schindler’s explanation for that brand growth:Let’s move to YouTube, which had a great quarter with strong growth in both brand and direct response…First, brand. YouTube is helping advertisers reach audiences they can’t find anywhere else. According to Nielsen’s Total Ad Ratings Reach reporting, from Q4 ‘18 to Q4 ‘20, on average, 70% of YouTube’s reach was delivered to an audience not reached by the advertiser’s TV media. In other words, YouTube’s reach is becoming increasingly incremental to TV, anThese numbers, to be clear, are based on surveys — the biggest driver of YouTube’s improved brand numbers is almost certainly the COVID bounceback. At the same time, look again at those Olympics numbers. Sports may still be the linchpin of brand advertising, but that pin sure feels like it is about to pop, and YouTube is better placed than just about anyone to pick up the slack.🌊 Best Links of The Week 🔮💉 FDA Allows Pharmacies To Substitute Branded Insulin With Cheaper Biosimilar For The First Time🏙️ NYC to Require Vaccination for Many Indoor Activities Such as Restaurants and Gyms🧠 Embodied AI, Superintelligence, and The Master Algorithm⚖️ The Activision Blizzard Lawsuit Could Be a Watershed Moment for The Business World. Here’s Why.🌊 Canadian Companies Mentioned 🇨🇦Silofit - We are the world’s first network of private fitness spaces. We repurpose small offices into private studios you can rent by the hour to do what you came to do - whether it's to treat your clients, exercise with friends, or work out alone, we want you to make it happen. We hope that in building these spaces for you, you get the privacy, reliability, and independence you need to get it done. Get on the email list at www.reformedmillennials.com

I first became familiar with Pedro's work through his 2015 book, The Master Algorithm. But as it turns out, his existence extends prior to my familiarity with him—which is what the bulk of what we explore in this conversation. Pedro is a professor at the University of Washington and a venerable AI researcher. He has a great quote about how as fields grow, their textbooks become larger. Then, as they mature, the textbooks become smaller again. I don't know if that's true. But it's a nice line, coming from a guy who wrote a book about getting AI down to a single master algorithm.

Tim O'Reilly, the founder of O'Reilly Media and author of the book, What's the Future?, talks about how new technology can either be considered a scapegoat or a mirror and what this means for our future.

There is an inordinate amount of hype and fear around artificial intelligence these days, as a chorus of scholars, luminaries, media, and politicians nervously project that it could soon take our jobs and subjugate or even kills us off. Others are just as fanciful in hoping it is on the verge of solving all our problems. But the truth is AI isn’t nearly as advanced as most people imagine. What is the practical reality of AI today, and how should government approach AI policy to maximize its potential? To parse the hype, the hope, and the path forward for AI, Rob and Jackie sat down recently with Pedro Domingos, emeritus professor of computer science at the University of Washington and author of The Master Algorithm.Mentioned:Pedro Domingos, The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World (Basic Books, 2015).Robert D. Atkinson, “The 2015 ITIF Luddite Award Nominees: The Worst of the Year’s Worst Innovation Killers” (ITIF, December 2015).Richard Dawkins, The Selfish Gene (Oxford University Press, 1990).Carl Benedikt Frey and Michael A. Osbourne, “The Future of Employment: How Susceptible Are Jobs to Computerisation?” (University of Oxford, September 17, 2013).Michael McLaughlin and Daniel Castro, “The Critics Were Wrong: NIST Data Shows the Best Facial Recognition Algorithms Are Neither Racist Nor Sexist” (ITIF, January 2020).“The Case for Killer Robots,” ITIF Innovation Files podcast with Robert Marks, August 10, 2020.

Did we invent computers, robots, and A.I. because we are fundamentally lazy? It may look like it, but that is not the technological driver. On the opposite side, it has to do with doing more, faster, more effectively, and going above and beyond our human capabilities.This same thing can be said of a carved stone used as a tool by our ancestors, the wheel, electricity, engines, airplanes, and of the most advanced technologies of today and tomorrow.Most of us do not realize it, but at the core of all the digital technology we use today, there are algorithms, and those are the things that are driving a big part of our everyday life — from buying online to moving around — but it's not just in cyberspace. Our whole day — from the moment you wake up to the moment you fall asleep—is suffused with machine learning.In this podcast, we discover what the engine is that drives Machine Learning and what its fuel is — spoiler alert: data, and a lot of it.Our guest is Professor Pedro Domingos from the University of Washington, who wrote a book called The Master Algorithm. As dystopian as that may sound, it is actually quite the opposite.  "The book is a thought-provoking and wide-ranging exploration of machine learning and the race to build computer intelligences as flexible as our own.In the world's top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask.In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner--the Master Algorithm--and discusses what it will mean for business, science, and society. If data-ism is today's philosophy, this book is its bible."We hope you will join us in the quest to discover what this Master Algorithm really means and what it can do for us. We do this constructively and positively, with hope and excitement for a better future, but with an eye open to the risk that may come if we cannot control its outcome — if and when AI can do much more than what it already can. Which is not that little.Let's go!GuestPedro Domingos, Professor of computer science at the University of Washington and author of 'The Master Algorithm' (@pmddomingos on Twitter)This Episode's SponsorsBlackCloak: https://itspm.ag/itspbcwebRSA Security: https://itspm.ag/itsprsawebResourcesView Pedro's work as a professor: https://homes.cs.washington.edu/~pedrod/View Pedro's book, The Master Algorithm, How the Quest for the Ultimate Learning Machine Will Remake Our World: https://www.basicbooks.com/titles/pedro-domingos/the-master-algorithm/9780465061921/To see and hear more The Cyber Society content on ITSPmagazine, visit:https://www.itspmagazine.com/the-cyber-societyTo see and hear more Redefining Technology stories on ITSPmagazine, visit:https://www.itspmagazine.com/redefining-technologyAre you interested in sponsoring an ITSPmagazine Channel?https://www.itspmagazine.com/podcast-series-sponsorships

Juntem-se a nós para mais uma conversa muito interessante sobre AI. Neste episódio decidimos abordar o tópico da deteção de anomalias, falando dos mitos e espectativas que de vez em quando, são inflacionadas nestas abordagens. Vamos partilhar alguns casos práticos sobre este tema, acompanhados com uma conversa descontraída sobre este tópico tão falado do ML. Links da IA News: Uncovering secrets of the “black box”: Pedro Domingos, author of “The Master Algorithm,” shares new work examining the inner workings of deep learning models: https://news.cs.washington.edu/2020/12/02/uncovering-secrets-of-the-black-box-pedro-domingos-author-of-the-master-algorithm-shines-new-light-on-the-inner-workings-of-deep-learning-models/ Chinese AI chipmaker Horizon endeavours to raise $700M to rival NVIDIA: https://artificialintelligence-news.com/2020/12/22/chinese-ai-chipmaker-horizon-raise-700m-rival-nvidia/ Making datasets inclusive from the ground up: https://www.marketplace.org/shows/marketplace-tech/making-datasets-inclusive-from-the-ground-up/

How the Quest For the Ultimate Learning Machine Will Remake Our World. According to Pedro Domingos, one of the greatest mysteries of the universe is not how it begins or ends, or what infinitesimal threads it’s woven from, it’s what goes on in a small child’s mind: how a pound of gray jelly can grow into a seat of consciousness. Even more astonishing is how little role parents play in teaching the brain to go through this transformation, as it largely does it all on its own. Today, scientists, computer engineers, and more are working towards a machine that can do exactly what the human brain does: learn. With all the technology of today, machines may one day even become smarter than the human brain. Computers can learn from large sets of data that we may not even realize is getting collected. This means that our future can be run by technology, changing the way we live and interact with each other. As you read, you’ll learn how machines will one day be like the human brain, how there is no such thing as a perfect algorithm, and how a Master Algorithm is on its way to being created. *** Do you want more free audiobook summaries like this? Download our app for free at QuickRead.com/App and get access to hundreds of free book and audiobook summaries.

How the Quest For the Ultimate Learning Machine Will Remake Our World. According to Pedro Domingos, one of the greatest mysteries of the universe is not how it begins or ends, or what infinitesimal threads it’s woven from, it’s what goes on in a small child’s mind: how a pound of gray jelly can grow into a seat of consciousness. Even more astonishing is how little role parents play in teaching the brain to go through this transformation, as it largely does it all on its own. Today, scientists, computer engineers, and more are working towards a machine that can do exactly what the human brain does: learn. With all the technology of today, machines may one day even become smarter than the human brain. Computers can learn from large sets of data that we may not even realize is getting collected. This means that our future can be run by technology, changing the way we live and interact with each other. As you read, you’ll learn how machines will one day be like the human brain, how there is no such thing as a perfect algorithm, and how a Master Algorithm is on its way to being created. *** Do you want more free audiobook summaries like this? Download our app for free at QuickRead.com/App and get access to hundreds of free book and audiobook summaries.

Cristina flexes her critical thinking muscles and investigates the legitimacy of why 10,000 steps a day is promoted as the magical number to stay healthy and fit. Adam looks at statistics to check claims that fatal anti-transgender violence is a national epidemic in the U.S. Lastly, Darren gives an overview of Pedro Domingos’ book, “The Master Algorithm”, a thought-provoking look at machine learning.

Tommy discusses the Master Algorithm.

In this episode of BEST Alumni Podcast we talk with Nadina Busuioc about her work in data science and ambition to apply her experience for greater good. Nadina tells us about her 12-year long career at Procter & Gamble, how it is to be a woman in a male-dominated field and how Aspen Young Leaders Program helped her start a new project with the goal of doing something for society. References Aspen Young Leaders ProgramDataFramed (podcast)Work Life (podcast)Thrive Global (podcast)Becoming (book)The Master Algorithm (book)

What role can policy, government, and tech nonprofits play to build tech that is optimized to benefit society and our economy? Tim O’Reilly, Founder & CEO, O’Reilly Media, and James Slavet, Greylock Partner, discuss. This is a recording from their fireside chat at Fast Forward's tech for good summit, Accelerate Good Global. 

In this week's episode, I talk to Pedro Domingos, author of the bestselling book, The Master Algorithm, which is about the ongoing effort to unify machine-learning paradigms in a single model. But the conversation was much broader than that. Pedro believes strongly that the great powers are engaged in an AI arms race with America's Defense Advanced Research Projects Agency, or Darpa, pitted against China's military and industrial dynamo. We also talked about the future of democracy and authoritarianism in an AI-driven world.

Azamat Beknazarov is a solutions engineer at Google. He has been in the IT industry for the past 7 years working on web development, advertisement technology, and most recently machine learning (or predictive modeling), which is one of the many subfields within AI. Topics of Discussion with Azamat Beknazarov The difference between machine learning and artificial intelligence The software, hardware, and programming languages of machine learning The Google Translate app Amazon Mechanical Turk Captcha and visual computing technology How machine learning is being used today The importance of good and ethical data sets The future of machine learning The dangers of machine learning and AI Links from the Discussion Youtube Video: What is machine learning? Youtube Video: But what *is* a Neural Network? Youtube Channel: 3Blue1Brown Book: Superintelligence: Paths, Dangers, Strategies by Nick Bostrom Book: The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World  TED Talk: The Quest for the Master Algorithm fast.ai - Making neural nets cool again Coursera - Online learning from the world’s best universities and companies Khan Academy - Online learning

On this week’s show Chris & Matt speak about Judgement – judgement of the book that was this week’s Bookclub focus, The Master Algorithm, judgement of whether computers will ever possess judgement, and last but not least how to make a judgement of technology suppliers, especially in regard to information security. You can find the […]

On this week’s show, Matt interviews psychologist Nancy Doyle about neurodiversity and the tech industry. You can find Nancy’s BPS report here: https://www.bps.org.uk/news-and-policy/psychology-work-improving-wellbeing-and-productivity-workplace And Genius within can be found at https://www.geniuswithin.co.uk/ This week we also reviewed Seth Stephens-Davidowitz’s Everybody Lies. The next Bookclub book is The Master Algorithm.

Pedro Domingos, author of The Master Algorithm, on how AI will reshape businesses. 

Pedro Domingos joins Political Economy to discuss his new book "The Master Algorithm." The post https://www.aei.org/multimedia/ep-101-artificial-intelligence-and-the-hunt-for-the-master-algorithm/ (Ep. 101: Artificial intelligence and the hunt for the master algorithm— Political Economy with James Pethokoukis) appeared first on https://www.aei.org (American Enterprise Institute - AEI).

In this week’s episode, Kyle Polich interviews Pedro Domingos about his book, The Master Algorithm: How the quest for the ultimate learning machine will remake our world. In the book, Domingos describes what machine learning is doing for humanity, how it works and what it could do in the future. He also hints at the possibility of an ultimate learning algorithm, in which the machine uses it will be able to derive all knowledge — past, present, and future.

The Master Algorithm is the topic of today's AI Minute. Gigaom AI Minute – March 16

The Master Algorithm is the topic of today's AI Minute. Gigaom AI Minute – March 16

This week Matt and Adam review THE MASTER ALGORITHM by Pedro Domingos, a book about a universal algorithm capable of extracting ALL KNOWLEDGE from data. Seems useful, right? Your hosts are not convinced... If you'd like to support the show, consider buying us a coffee at https://www.buymeacoffee.com/randomtalkers. You can check out this episode and more on the Random Talkers YouTube channel: www.youtube.com/c/RandomTalkers. Peruse our old segments, leave an Internet comment, or maybe just admire Matt’s amazingly consistent outfit choices.

Today's theme: A.I. (Artificial Intelligence) Elon Musk has warned again about the dangers of artificial intelligence, saying that it poses “vastly more risk” than the apparent nuclear capabilities of North Korea does. You might have thought that Artificial Intelligence was a silly sci-fi concept. But let's clear things up! Stop thinking of robots. Let's take a close look at what the leading thinkers in the field believe this road looks like and why this revolution might happen way sooner than you might think. >>>The Conversationalist With Mark Zastrow - Science Journalist - Written for Nature, New Scientist and other outlets “We don't know very much about how AI makes the decisions that it does—even the people who program it. This can lead to all sorts of dangerous situations if we put too much faith in it, and can also lead to hidden biases.” & Pedro Domingos - Professor of Computer Science at University of Washington - Winner of 2014 SIGKDD Innovation Award, known as Nobel Prize in data science - Author of the bestseller Master Algorithm which is also available in Korean “As the data grows exponentially, and with computers able to learn, how convenient can a future life be? Do you think machines can mimic the way human brain works?” & Dr. Peter Asaro - Philosopher of science, technology and media - Affiliate Scholar from Center for Internet and Society at Stanford Law School - Co-founder & Vice Chair of the International Committee on Robot Arms Control - Spokesperson for Campaign to Stop Killer Robots “The open letter signed this week by 116 tech company leaders says that the arms race for killer robots is currently under way. Do you agree? Do you think killer robots rebelling against humans as in sci-fi movies can be possible in the future?” & >>>A Few Minutes with Amos I recently took a trip… the WORST are people who don't know that you need to have a passport to travel. They get to the desk, or security, or boarding and are like “OH! You need a passport? I think it's at the bottom of my suitcase. Let me get it.” So my solution is that is anyone is like “Passport? What is passport?” then they should immediately have their ticket revoked without refund…. >>>Next week: A Taxi Driver (택시 운전사)

James Hewitt is Head of Science & Innovation at Hintsa Performance. His work includes consulting with Formula 1 drivers and teams, work in elite sport and with global corporations, a wide-range of written articles, presentations, keynotes and workshops in Europe, the United States and Asia. In this interview with Dr Tommy Wood, James discusses a polarised approach to cognitive performance, arguing that time spent in the middle gear is time wasted. James also explains why smartphones are so compelling yet interfering with our ability to concentrate. Here’s the outline of this interview with James Hewitt: [00:01:15] Book: Exponential by James Hewitt and Aki Hintsa. [00:03:31] Website: Hintsa Performance. [00:04:20] Newsletter: Nourish Balance Thrive Highlights. [00:04:50] Article: A day in the life of Scott, hopelessly distracted office worker by James Hewitt. [00:05:38] Polarised training. [00:06:18] Cognitive task load model. [00:08:01] World Economic Forum Report: The Future of Jobs and Skills in the Middle East and North Africa: Preparing the Region for the Fourth Industrial Revolution. [00:09:18] Podcast: Pedro Domingos on Machine Learning and the Master Algorithm, TED Talk: The Wonderful and Terrifying Implications of Computers that Can Learn with Jeremy Howard. [00:11:00] Study: Frey, Carl Benedikt, and Michael A. Osborne. "The future of employment: how susceptible are jobs to computerisation?." Technological Forecasting and Social Change 114 (2017): 254-280. [00:11:10] Report: A Future That Works: Automation, Employment, and Productivity by McKinsey Global Institute. [00:12:29] Default mode network. [00:13:31] Smartphones. [00:14:59] Novelty seeking. [00:16:26] Study: Kushlev, Kostadin & Dunn, Elizabeth. (2015). Checking Email Less Frequently Reduces Stress. [00:17:11] Lecture: Dopamine Jackpot! Sapolsky on the Science of Pleasure by Robert Sapolsky. [00:19:25] Productivity without purpose. [00:19:45] Study: Levitas, Danielle. "Always connected: How smartphones and social keep us engaged." International Data Corporation (IDC). Retrieved from (2013). [00:21:05] Three questions: priority, opportunity, elimination. [00:22:30] Attention restoration. [00:24:40] Mornings. [00:25:21] Book: The Power of When: Discover Your Chronotype--and the Best Time to Eat Lunch, Ask for a Raise, Have Sex, Write a Novel, Take Your Meds, and More by Michael Breus. [00:25:43] Study: Akacem LD, Wright KP, LeBourgeois MK. Bedtime and evening light exposure influence circadian timing in preschool-age children: A field study. Neurobiology of sleep and circadian rhythms. 2016. [00:28:59] Study: Williamson AM, Feyer A Moderate sleep deprivation produces impairments in cognitive and motor performance equivalent to legally prescribed levels of alcohol intoxication Occupational and Environmental Medicine 2000;57:649-655. [00:30:06] Study: Van Dongen, Hans Pa, et al. "The Cumulative Cost of Additional Wakefulness: Dose-response Effects on Neurobehavioral Functions and Sleep Physiology From Chronic Sleep Restriction and Total Sleep Deprivation." Sleep 26.2 (2003): 117-126. [00:32:21] Galvanic skin response. [00:34:43] Sex differences in rapid switching. [00:37:46] Changing behaviour. [00:38:01] Derek Sivers. [00:39:25] Implementation intention. [00:42:15] Positive vision. [00:45:45] Apps: Depak Chopra Meditation Apps. [00:50:16] Device: The PIP stress tracker. [00:52:44] Device: Muse headband. [00:53:49] Ways to connect: Hinsta.com, JamesHewitt.net, James Hewitt on Twitter.

RR 319 Machine Learning with Tyler Renelle This episode of the Ruby Rogues Panel features panelists Charles Max Wood and Dave Kimura. Tyler Renelle, who stops by to talk about machine learning, joins them as a guest. Tyler is the first guest to talk on Adventures in Angular, JavaScript Jabber, and Ruby Rogues. Tune in to find out more about Tyler and machine learning! What is machine learning? Machine learning is a different concept than programmers are used to. There are three phases in computing technology. First phase – building computers in the first place but it was hard coded onto the physical computing machinery Second phase – programmable computers. Where you can reprogram your computer to do anything. This is the phase where programmers fall. Third phase – machine learning falls under this phase. Machine learning is where the computer programs itself to do something. You give the computer a measurement of how it’s doing based on data and it trains itself and learns how to do the task. It is beginning to get a lot of press and become more popular. This is because it is becoming a lot more capable by way of deep learning. AI – Artificial Intelligence Machine learning is a sub field of artificial intelligence. AI is an overarching field of the computer simulating intelligence. Machine learning has become less and less a sub field over time and more a majority of AI. Now we can apply machine learning to vision, speech processing, planning, knowledge representation. This is fast taking over AI. People are beginning to consider the terms artificial intelligence and machine learning synonymous. Self-driving cars are a type of artificial intelligence. The connection between machine learning and self-driving cars is abstract. A fundamental thing in self-driving cars is machine learning. You program the car as to how to fix its mistakes. Another example is facial recognition. The program starts learning about a person’s face over time so it can make an educated guess as to if the person is who they say they are. Once statistics are added then your face can be off by a hair or a hat. Small variations won’t throw it off. How do we start solving the problems we want to be solved? Machine learning has been applied since the 1950s to a broad spectrum of problems. Have to have a little bit of domain knowledge and do some research. Machine Learning Vs Programming Machine learning is any sort of fuzzy programming situation. Programming is when you do things specifically or statically. Why should you care to do machine learning? People should care because this is the next wave of computing. There is a theory that this will displace jobs. Self-driving cars will displace truck drivers, Uber drivers, and taxis. There are things like logo generators already. Machines are generating music, poetry, and website designs. We shouldn’t be afraid that we should keep an eye towards it. If a robot or computer program or AI were able to write its own code, at what point would it be able to overwrite or basically nullify the three laws of robotics? Nick Bostrom wrote the book Superintelligence, which had many big names in technology talking about the dangers of AI. Artificial intelligence has been talked about widely because of the possibility of evil killer robots in the Sci-Fi community. There are people who hold very potential concerns, such as job automation. Consciousness is a huge topic of debate right now on this topic. Is it an emergent property of the human brain? Is what we have with deep learning enough of a representation to achieve consciousness? It is suggested that AI may or may not achieve consciousness. The question is if it is able to achieve consciousness - will we be able to tell there isn’t a person there? If people want to dive into this where do they go? Machine Learning Guide Podcast: http://ocdevel.com/podcasts/machine-learning The Master Algorithm. https://www.amazon.com/Master-Algorithm-Ultimate-Learning-Machine/dp/0465065708 Andrew Ng course: coursera.org/machine/learning Machine Learning Language The main language used for machine learning is Python. This is not because of the language itself, but because of the tools built on top of it. The main framework is TensorFlow. Python in TensorFlow drops to C and executes code on the GPU for performing matrix algebra, which is essential for deep learning. You can always use C, C++, Java, and R. Data scientists mostly use R, while researchers use C and C++ so they can custom code their matrix algebra themselves. Picks Dave: 20-gallon Husky oil free air compressor: http://www.homedepot.com/p/Husky-20-Gal-Vertical-Oil-Free-Electric-Air-Compressor-0332013/207040335 Charles: Twitter T gem: https://rubygems.org/gems/t/versions/2.10.0> Ruby Dev Summit: www.rubydevsummit.com Rake: https://www.sitepoint.com/rake-automate-things/ Tyler: Machine Learning Guide Podcast: http://ocdevel.com/podcasts/machine-learning Philosophy of Mind: Brains, Consciousness, and Thinking Machines (The Great Courses): https://www.amazon.com/Great-Courses-Philosophy-Mind/dp/1598034243

RR 319 Machine Learning with Tyler Renelle This episode of the Ruby Rogues Panel features panelists Charles Max Wood and Dave Kimura. Tyler Renelle, who stops by to talk about machine learning, joins them as a guest. Tyler is the first guest to talk on Adventures in Angular, JavaScript Jabber, and Ruby Rogues. Tune in to find out more about Tyler and machine learning! What is machine learning? Machine learning is a different concept than programmers are used to. There are three phases in computing technology. First phase – building computers in the first place but it was hard coded onto the physical computing machinery Second phase – programmable computers. Where you can reprogram your computer to do anything. This is the phase where programmers fall. Third phase – machine learning falls under this phase. Machine learning is where the computer programs itself to do something. You give the computer a measurement of how it’s doing based on data and it trains itself and learns how to do the task. It is beginning to get a lot of press and become more popular. This is because it is becoming a lot more capable by way of deep learning. AI – Artificial Intelligence Machine learning is a sub field of artificial intelligence. AI is an overarching field of the computer simulating intelligence. Machine learning has become less and less a sub field over time and more a majority of AI. Now we can apply machine learning to vision, speech processing, planning, knowledge representation. This is fast taking over AI. People are beginning to consider the terms artificial intelligence and machine learning synonymous. Self-driving cars are a type of artificial intelligence. The connection between machine learning and self-driving cars is abstract. A fundamental thing in self-driving cars is machine learning. You program the car as to how to fix its mistakes. Another example is facial recognition. The program starts learning about a person’s face over time so it can make an educated guess as to if the person is who they say they are. Once statistics are added then your face can be off by a hair or a hat. Small variations won’t throw it off. How do we start solving the problems we want to be solved? Machine learning has been applied since the 1950s to a broad spectrum of problems. Have to have a little bit of domain knowledge and do some research. Machine Learning Vs Programming Machine learning is any sort of fuzzy programming situation. Programming is when you do things specifically or statically. Why should you care to do machine learning? People should care because this is the next wave of computing. There is a theory that this will displace jobs. Self-driving cars will displace truck drivers, Uber drivers, and taxis. There are things like logo generators already. Machines are generating music, poetry, and website designs. We shouldn’t be afraid that we should keep an eye towards it. If a robot or computer program or AI were able to write its own code, at what point would it be able to overwrite or basically nullify the three laws of robotics? Nick Bostrom wrote the book Superintelligence, which had many big names in technology talking about the dangers of AI. Artificial intelligence has been talked about widely because of the possibility of evil killer robots in the Sci-Fi community. There are people who hold very potential concerns, such as job automation. Consciousness is a huge topic of debate right now on this topic. Is it an emergent property of the human brain? Is what we have with deep learning enough of a representation to achieve consciousness? It is suggested that AI may or may not achieve consciousness. The question is if it is able to achieve consciousness - will we be able to tell there isn’t a person there? If people want to dive into this where do they go? Machine Learning Guide Podcast: http://ocdevel.com/podcasts/machine-learning The Master Algorithm. https://www.amazon.com/Master-Algorithm-Ultimate-Learning-Machine/dp/0465065708 Andrew Ng course: coursera.org/machine/learning Machine Learning Language The main language used for machine learning is Python. This is not because of the language itself, but because of the tools built on top of it. The main framework is TensorFlow. Python in TensorFlow drops to C and executes code on the GPU for performing matrix algebra, which is essential for deep learning. You can always use C, C++, Java, and R. Data scientists mostly use R, while researchers use C and C++ so they can custom code their matrix algebra themselves. Picks Dave: 20-gallon Husky oil free air compressor: http://www.homedepot.com/p/Husky-20-Gal-Vertical-Oil-Free-Electric-Air-Compressor-0332013/207040335 Charles: Twitter T gem: https://rubygems.org/gems/t/versions/2.10.0> Ruby Dev Summit: www.rubydevsummit.com Rake: https://www.sitepoint.com/rake-automate-things/ Tyler: Machine Learning Guide Podcast: http://ocdevel.com/podcasts/machine-learning Philosophy of Mind: Brains, Consciousness, and Thinking Machines (The Great Courses): https://www.amazon.com/Great-Courses-Philosophy-Mind/dp/1598034243

RR 319 Machine Learning with Tyler Renelle This episode of the Ruby Rogues Panel features panelists Charles Max Wood and Dave Kimura. Tyler Renelle, who stops by to talk about machine learning, joins them as a guest. Tyler is the first guest to talk on Adventures in Angular, JavaScript Jabber, and Ruby Rogues. Tune in to find out more about Tyler and machine learning! What is machine learning? Machine learning is a different concept than programmers are used to. There are three phases in computing technology. First phase – building computers in the first place but it was hard coded onto the physical computing machinery Second phase – programmable computers. Where you can reprogram your computer to do anything. This is the phase where programmers fall. Third phase – machine learning falls under this phase. Machine learning is where the computer programs itself to do something. You give the computer a measurement of how it’s doing based on data and it trains itself and learns how to do the task. It is beginning to get a lot of press and become more popular. This is because it is becoming a lot more capable by way of deep learning. AI – Artificial Intelligence Machine learning is a sub field of artificial intelligence. AI is an overarching field of the computer simulating intelligence. Machine learning has become less and less a sub field over time and more a majority of AI. Now we can apply machine learning to vision, speech processing, planning, knowledge representation. This is fast taking over AI. People are beginning to consider the terms artificial intelligence and machine learning synonymous. Self-driving cars are a type of artificial intelligence. The connection between machine learning and self-driving cars is abstract. A fundamental thing in self-driving cars is machine learning. You program the car as to how to fix its mistakes. Another example is facial recognition. The program starts learning about a person’s face over time so it can make an educated guess as to if the person is who they say they are. Once statistics are added then your face can be off by a hair or a hat. Small variations won’t throw it off. How do we start solving the problems we want to be solved? Machine learning has been applied since the 1950s to a broad spectrum of problems. Have to have a little bit of domain knowledge and do some research. Machine Learning Vs Programming Machine learning is any sort of fuzzy programming situation. Programming is when you do things specifically or statically. Why should you care to do machine learning? People should care because this is the next wave of computing. There is a theory that this will displace jobs. Self-driving cars will displace truck drivers, Uber drivers, and taxis. There are things like logo generators already. Machines are generating music, poetry, and website designs. We shouldn’t be afraid that we should keep an eye towards it. If a robot or computer program or AI were able to write its own code, at what point would it be able to overwrite or basically nullify the three laws of robotics? Nick Bostrom wrote the book Superintelligence, which had many big names in technology talking about the dangers of AI. Artificial intelligence has been talked about widely because of the possibility of evil killer robots in the Sci-Fi community. There are people who hold very potential concerns, such as job automation. Consciousness is a huge topic of debate right now on this topic. Is it an emergent property of the human brain? Is what we have with deep learning enough of a representation to achieve consciousness? It is suggested that AI may or may not achieve consciousness. The question is if it is able to achieve consciousness - will we be able to tell there isn’t a person there? If people want to dive into this where do they go? Machine Learning Guide Podcast: http://ocdevel.com/podcasts/machine-learning The Master Algorithm. https://www.amazon.com/Master-Algorithm-Ultimate-Learning-Machine/dp/0465065708 Andrew Ng course: coursera.org/machine/learning Machine Learning Language The main language used for machine learning is Python. This is not because of the language itself, but because of the tools built on top of it. The main framework is TensorFlow. Python in TensorFlow drops to C and executes code on the GPU for performing matrix algebra, which is essential for deep learning. You can always use C, C++, Java, and R. Data scientists mostly use R, while researchers use C and C++ so they can custom code their matrix algebra themselves. Picks Dave: 20-gallon Husky oil free air compressor: http://www.homedepot.com/p/Husky-20-Gal-Vertical-Oil-Free-Electric-Air-Compressor-0332013/207040335 Charles: Twitter T gem: https://rubygems.org/gems/t/versions/2.10.0> Ruby Dev Summit: www.rubydevsummit.com Rake: https://www.sitepoint.com/rake-automate-things/ Tyler: Machine Learning Guide Podcast: http://ocdevel.com/podcasts/machine-learning Philosophy of Mind: Brains, Consciousness, and Thinking Machines (The Great Courses): https://www.amazon.com/Great-Courses-Philosophy-Mind/dp/1598034243

If there’s one thing we can learn from histroy it’s everything that there is to know. Or at least that’s the promise of machine learning. The master algorithm by Pedro Domingos tells us how machines that learn are starting to transform the world, bringing us driverless cars and perhaps even bloodless wars. Hear an interview with Domingos, a reading from the book, and the thoughts of Royal Society of Chemistry data scientist Colin Batchelor and Chemistry World’s digital content producer, Sam Tracey, who join host Emma Stoye.

Machine learning is fast becoming a part of our lives. From the order in which your search results and news feeds are ordered to the image classifiers and speech recognition features on your smartphone. Machine learning may even have had a hand in choosing your spouse or driving you to work. As with cars, only the mechanics need to understand what happens under the hood, but all drivers need to know how to operate the steering wheel. Listen to this podcast to learn how to interact with machines that can learn, and about the implications for humanity. My guest is Dr. Pedro Domingos, Professor of Computer Science at Washington University. He is the author or co-author of over 200 technical publications in machine learning and data mining, and the author of my new favourite book The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Here’s the outline of this interview with Dr. Pedro Domingos, PhD: [00:01:55] Deep Learning. [00:02:21] Machine learning is affecting everyone's lives. [00:03:45] Recommender systems. [00:03:57] Ordering newsfeeds. [00:04:25] Text prediction and speech recognition in smart phones. [00:04:54] Accelerometers. [00:04:54] Selecting job applicants. [00:05:05] Finding a spouse. [00:05:35] OKCupid.com. [00:06:49] Robot scientists. [00:07:08] Artificially-intelligent Robot Scientist ‘Eve’ could boost search for new drugs. [00:08:38] Cancer research. [00:10:27] Central dogma of molecular biology. [00:10:34] DNA microarrays. [00:11:34] Robb Wolf at IHMC: Darwinian Medicine: Maybe there IS something to this evolution thing. [00:12:29] It costs more to find the data than to do the experiment again (ref?) [00:13:11] Making connections people could never make. [00:14:00] Jeremy Howard’s TED talk: The wonderful and terrifying implications of computers that can learn. [00:14:14] Pedro's TED talk: The Quest for the Master Algorithm. [00:15:49] Craig Venter: your immune system on the Internet. [00:16:44] Continuous blood glucose monitoring and Heart Rate Variability. [00:17:41] Our data: DUTCH, OAT, stool, blood. [00:19:21] Supervised and unsupervised learning. [00:20:11] Clustering dimensionality reduction, e.g. PCA and T-SNE. [00:21:44] Sodium to potassium ratio versus cortisol. [00:22:24] Eosinophils. [00:23:17] Clinical trials. [00:24:35] Tetiana Ivanova - How to become a Data Scientist in 6 months a hacker’s approach to career planning. [00:25:02] Deep Learning Book. [00:25:46] Maths as a barrier to entry. [00:27:09] Andrew Ng Coursera Machine Learning course. [00:27:28] Pedro's Data Mining course. [00:27:50] Theano and Keras. [00:28:02] State Farm Distracted Driver Detection Kaggle competition. [00:29:37] Nearest Neighbour algorithm. [00:30:29] Driverless cars. [00:30:41] Is a robot going to take my job? [00:31:29] Jobs will not be lost, they will be transformed [00:33:14] Automate your job yourself! [00:33:27] Centaur chess player. [00:35:32] ML is like driving, you can only learn by doing it. [00:35:52] A Few Useful Things to Know about Machine Learning. [00:37:00] Blood chemistry software. [00:37:30] We are the owners of our data. [00:38:49] Data banks and unions. [00:40:01] The distinction with privacy. [00:40:29] An ethical obligation to share. [00:41:46] Data vulcanisation. [00:42:40] Teaching the machine. [00:43:07] Chrome incognito mode. [00:44:13] Why can't we interact with the algorithm? [00:45:33] New P2 Instance Type for Amazon EC2 – Up to 16 GPUs. [00:46:01] Why now? [00:46:47] Research breakthroughs. [00:47:04] The amount of data. [00:47:13] Hardware. [00:47:31] GPUs, Moore’s law. [00:47:57] Economics. [00:48:32] Google TensorFlow. [00:49:05] Facebook Torch. [00:49:38] Recruiting. [00:50:58] The five tribes of machine learning: evolutionaries, connectionists, Bayesians, analogizers, symbolists. [00:51:55] Grand unified theory of ML. [00:53:40] Decision tree ensembles (Random Forests). [00:53:45] XGBoost. [00:53:54] Weka. [00:54:21] Alchemy: Open Source AI. [00:56:16] Still do a computer science degree. [00:56:54] Minor in probability and statistics.

Dr. Pedro Domingos, Professor at the University of Washington, joins us live from Intel Developer Forum in San Francisco. Dr. Domingos has conducted research in the field of machine learning for more than 20 years. In this interview, Dr. Domingos discusses the critical need for more people to better understand machine learning, progress towards a grand unified theory of machine learning, and how the world may be transformed by computers that are programmed to learn rather than programmed to do. For more information on Dr. Domingos' work, look for his book, "The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World" (http://amzn.to/2c8WrlG), and follow him on Twitter at https://twitter.com/pmddomingos.

What is machine learning? How is it transforming our lives and workplaces? What might the future hold? Pedro Domingos of the University of Washington and author of The Master Algorithm talks with EconTalk host Russ Roberts about the present and future of machine learning. Domingos stresses the iterative and ever-improving nature of machine learning. He is fundamentally an optimist about the potential of machine learning with ever-larger amounts of data to transform the human experience.

When Pedro Domingos was growing up in Lisbon, he loved to read and to learn. In fact, when he was 13 years old, he decided that he wanted to learn everything that there is to know. It did not take him long to decide that it might be a little more useful to, instead of knowing everything, to become deeply knowledgeable about a few topics. His university studies led him to computer science in the 1980s, just as personal computers were beginning to catch on. Today, Pedro is a professor at the University of Washington and the author of The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. In this book he describes the five schools of thought on machine learning and advocates for a grand unifying theory. In this episode of the Social Entrepreneur podcast, we discuss: The benefit of deep expertise in one field of knowledge, when balanced with a broad interests in other fields. The exponential growth of computer processing power. The equally explosive growth of software. The power of algorithms, and how three words, “and, or, not” are changing our world. How machine learning is a technology that builds itself. How machine learning becomes culturally acceptable. Examples of disruptions brought about my machine learning. The surprising impact of machine learning on knowledge workers. How the combination of machine learning with robotics is brining machine learning into the physical world. The business impact of being able to lease machine learning or even find algorithms for free on GitHub. Where machine learning is going as a practice. Resources: The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World: http://amzn.to/1PJinUa Pedro Domingos on the web: http://pedrodomingos.org Pedro Domingos on Twitter: https://twitter.com/pmddomingos GitHub: https://github.com Give Well: http://www.givewell.org The Abdul Latif Jameel Poverty Action Lab at MIT: http://www.povertyactionlab.org

SHOW SUMMARY – Pedro Domingos joins us for a fascinating discussion on machine learning and his new book “The Master Algorithm: How the Quest for the Ultimate […]

In episode 20 we chat with Pedro Domingos of the University of Washington, he's just published a book The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. We get some insight into Linear Dynamical Systems which the Datta Lab at Harvard Medical School is doing some interesting work with. Plus, we take a listener question about using video games to generate labeled data (spoiler alert, it's an awesome idea!)We're in the final hours of our Fundraising Campaign and we need your help!

Our guest on today’s episode of Modern Notion Daily is Pedro Domingos, a professor of computer science and the author of The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World (Basic Books, September 2015). Domingos is an expert in machine learning, which is the engine behind much of what happens in our…