#240 Manos Koukoumidis: Why The Future of AI is Open-Source

Eye On A.I.

#Artificial Intelligence #Artificialintelligence #Machinelearning #Newyorktimes #Programming #Research #Technology #Tech #Craig S Smith #OpenAI

1:15:36

In this episode, we welcome back David French, columnist for The New York Times , former constitutional attorney, and author of Divided We Fall . We discuss the current state of American democracy, the challenges of political division, and how we can engage in civil discourse despite deep ideological differences. David also shares a personal update on his family and reflects on the profound trials and growth that come with adversity. 📌 What We Discuss: ✔️ How David and his family navigated the challenges of a serious health crisis. ✔️ The rise of political polarization and the factors driving it. ✔️ Why distinguishing between “unwise, unethical, and unlawful” is crucial in analyzing political actions. ✔️ How consuming different perspectives (even opposing ones) helps in understanding political dynamics. ✔️ The role of Christian values in politics and how they are being redefined. ⏳ Episode Highlights 📍 [00:01:00] – David French’s background and his journey from litigation to journalism. 📍 [00:02:30] – Personal update: David shares his wife Nancy’s battle with cancer and their journey as a family. 📍 [00:06:00] – How to navigate personal trials while maintaining faith and resilience. 📍 [00:10:00] – The danger of political paranoia and the pitfalls of extreme polarization. 📍 [00:18:00] – The "friend-enemy" paradigm in American politics and its influence in Christian fundamentalism. 📍 [00:24:00] – Revisiting Divided We Fall : How America’s divisions have devolved since 2020. 📍 [00:40:00] – The categories and differences of unwise, unethical, and unlawful political actions. 📍 [00:55:00] – The balance between justice, kindness, and humility in political engagement. 📍 [01:00:00] – The After Party initiative: A Christian approach to politics focused on values rather than policy. 💬 Featured Quotes 🔹 "You don't know who you truly are until your values are tested." – David French 🔹 "If we focus on the relational, we can have better conversations even across deep differences." – Corey Nathan 🔹 "Justice, kindness, and humility—if you're missing one, you're doing it wrong." – David French 🔹 "The United States has a history of shifting without repenting. We just move on." – David French 📚 Resources Mentioned David French’s Writing: New York Times David’s Book: Divided We Fall The After Party Initiative – More Info Advisory Opinions Podcast (with Sarah Isgur & David French) – Listen Here 📣 Call to Action If you found this conversation insightful, please: ✅ Subscribe to Talkin' Politics & Religion Without Killin' Each Other on your favorite podcast platform. ✅ Leave a review on Apple Podcasts, Spotify, or wherever you listen: ratethispodcast.com/goodfaithpolitics ✅ Support the show on Patreon: patreon.com/politicsandreligion ✅ Watch the full conversation and subscribe on YouTube: youtube.com/@politicsandreligion 🔗 Connect With Us on Social Media @coreysnathan: Bluesky LinkedIn Instagram Threads Facebook Substack David French: 🔗 Twitter | BlueSky | New York Times Our Sponsors Meza Wealth Management: www.mezawealth.com Prolux Autogroup: www.proluxautogroup.com or www.granadahillsairporttransportation.com Let’s keep talking politics and religion—with gentleness and respect. 🎙️💡…

23 days ago 1:06:03

MP3•Episode home

This episode is brought to you by Sonar, the creators of SonarQube Server, Cloud, IDE, and the open source Community Build.

Sonar unlocks actionable code intelligence, helping to redefine the software development lifecycle by use of AI and AI agentic systems, to continuously improve quality and security

while reducing developer toil. By analyzing all code, regardless of who writes it—your internal team or genAI—Sonar enables more secure, reliable, and maintainable software. Join the over 7 million developers from organizations like the DoD, Microsoft, NASA, MasterCard, Siemens, and T-Mobile, who use Sonar.

Visit http://sonarsource.com/eyeonai to try SonarQube for free today.

————————————————————————————————————————

The Future of AI is Open-Source | Manos Koukoumidis on UMI & The AI Revolution

Is closed AI holding back innovation? In this episode, Manos Koukoumidis, CEO of Oumi, makes the case for why the future of AI must be open-source. OUMI (Open Universal Machine Intelligence) is redefining how AI is built—offering fully open models, open data, and open collaboration to make AI development more transparent, accessible, and community-driven.

Big Tech has dominated AI, but UMI is challenging the status quo by creating a platform where anyone can train, fine-tune, and deploy AI models with just a few commands. Could this be the Linux moment for AI?

What You’ll Learn in This Episode:

Why open-source AI is the only sustainable path forward
The difference between “open-source” AI and true open AI
How OUMI enables researchers and enterprises to build better AI models
Why Big Tech’s closed AI systems are losing their competitive edge
The impact of open AI on healthcare, science, and enterprise innovation
The future of AI models—will proprietary AI survive?

The AI revolution is happening—and it’s open-source. If you care about the future of AI, innovation, and ethical tech development, this episode is a must-watch.

————————————————————————————————————————

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

————————————————————————————————————————

(00:00) The True Meaning of Open-Source AI

(02:15) The Open vs. Closed AI Debate

(07:54) Why Open AI Models Are Safer

(10:34) Defining Open Data

(13:21)Beating GPT-4-O with an Open AI Model

(16:36) Open AI in Healthcare

(19:31) Why Open Models Will Dominate

(23:07) How OUMI Makes AI Training Fully Accessible & Reproducible

(28:44) UMI’s Collaboration with Universities

(32:29) The Shift Toward Open A

(36:41) Can We Build Truly Open AI Models from Scratch?

(40:20) The Role of Open AI in Eliminating Bias

(45:02) Will Open AI Replace Proprietary AI Models?

(50:19) How OUMI Works

(54:44) The Open AI Revolution Has Begun

245 episodes

Eye On A.I.

#240 Manos Koukoumidis: Why The Future of AI is Open-Source

Eye On A.I.

222 subscribers

published 23 days ago

MP3•Episode home

This episode is brought to you by Sonar, the creators of SonarQube Server, Cloud, IDE, and the open source Community Build.

Sonar unlocks actionable code intelligence, helping to redefine the software development lifecycle by use of AI and AI agentic systems, to continuously improve quality and security

Visit http://sonarsource.com/eyeonai to try SonarQube for free today.

————————————————————————————————————————

The Future of AI is Open-Source | Manos Koukoumidis on UMI & The AI Revolution

What You’ll Learn in This Episode:

Why open-source AI is the only sustainable path forward
The difference between “open-source” AI and true open AI
How OUMI enables researchers and enterprises to build better AI models
Why Big Tech’s closed AI systems are losing their competitive edge
The impact of open AI on healthcare, science, and enterprise innovation
The future of AI models—will proprietary AI survive?

The AI revolution is happening—and it’s open-source. If you care about the future of AI, innovation, and ethical tech development, this episode is a must-watch.

————————————————————————————————————————

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

————————————————————————————————————————

(00:00) The True Meaning of Open-Source AI

(02:15) The Open vs. Closed AI Debate

(07:54) Why Open AI Models Are Safer

(10:34) Defining Open Data

(13:21)Beating GPT-4-O with an Open AI Model

(16:36) Open AI in Healthcare

(19:31) Why Open Models Will Dominate

(23:07) How OUMI Makes AI Training Fully Accessible & Reproducible

(28:44) UMI’s Collaboration with Universities

(32:29) The Shift Toward Open A

(36:41) Can We Build Truly Open AI Models from Scratch?

(40:20) The Role of Open AI in Eliminating Bias

(45:02) Will Open AI Replace Proprietary AI Models?

(50:19) How OUMI Works

(54:44) The Open AI Revolution Has Begun

245 episodes

#Artificial Intelligence #Artificialintelligence #Machinelearning #Newyorktimes #Programming #Research #Technology #Tech #Craig S Smith #OpenAI

All episodes

Eye On A.I.

1
#243 Greg Osuri: Why the Future of AI Depends on Decentralized Cloud Platforms 59:19

9 days ago59:19

59:19

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 Greg Osuri’s Vision for Decentralized Cloud Computing | The Future of AI & Web3 Infrastructure The cloud is broken—can decentralization fix it? In this episode, Greg Osuri, founder of Akash Network, shares his groundbreaking approach to decentralized cloud computing and how it's disrupting hyperscalers like AWS, Google Cloud, and Microsoft Azure. Discover how Akash Network’s peer-to-peer marketplace is slashing cloud costs, unlocking unused compute power, and paving the way for AI-driven infrastructure without Big Tech’s control. What You'll Learn in This Episode: - Why AI training is hitting an energy bottleneck and how decentralization solves it - How Akash Network creates a global marketplace for underutilized compute power - The role of blockchain in securing cloud resources and enforcing smart contracts - The privacy risks of hyperscalers—and why sovereign AI in the home is the future - How Akash Network is evolving from a resource marketplace to a full-fledged services economy - The future of AI, energy-efficient cloud solutions, and decentralized infrastructure The battle for the future of cloud computing is on—and decentralization is winning. If you're interested in AI, blockchain, Web3, or the economics of cloud infrastructure, this episode is a must-watch! Stay Updated: Craig Smith Twitter: https://twitter.com/craigss Eye on A.I. Twitter: https://twitter.com/EyeOn_AI (00:00) Introduction & The Biggest Challenges in AI Training (02:36) Greg Osuri’s Background (04:50) The Problem with AWS, Google Cloud & Traditional Cloud Providers (06:40) How To Use Blockchain for a Decentralized Cloud (10:17) Akash Network’s Marketplace Matches Compute Buyers & Sellers (14:42) Security & Privacy: Protecting Users from Data Risks (18:25) The Energy Crisis: Why Hyperscalers Are Unsustainable (21:51) The Future of AI: Decentralized Cloud & Home AI Computing (26:42) How AI Workloads Are Routed & Optimized (30:24) Big Companies Using Akash Network: NVIDIA, Prime Intellect & More (45:49) Building a Decentralized AI Services Marketplace (55:09) Why the Future of AI Needs a Decentralized Cloud…

Eye On A.I.

1
#242 Dylan Arena: The AI Education Revolution: How AI is Changing the Way We Learn 57:58

15 days ago57:58

57:58

This episode is brought to you by Extreme Networks, the company radically improving customer experiences with AI-powered automation for networking. Extreme is driving the convergence of AI, networking, and security to transform the way businesses connect and protect their networks, delivering faster performance, stronger security, and a seamless user experience. Visit extremenetworks.com to learn more. ———————————————————————————————————————— The Role of AI in Education | Dylan Arena on Learning, AI Tutoring & The Future of Teaching How can AI enhance education without replacing the human touch? In this episode, Dylan Arena, Chief Data Science and AI Officer at McGraw Hill, shares his insights on the intersection of AI and learning. Dylan’s background in learning sciences and technology design has shaped his approach to AI-powered tools that help students and teachers—not replace them. He discusses how AI can augment human relationships in education, improve personalized learning, and assist teachers with real-time insights while avoiding the pitfalls of over-reliance on automation. With AI playing an increasingly central role in education, are we at risk of losing the essential human connections that define great learning experiences? What You’ll Learn in This Episode: - Why AI should be used to enhance not replace teachers - The risks and rewards of AI-powered tutoring - How AI-driven assessments can improve personalized learning - Why AI chatbots in education need careful ethical considerations - The future of gamification and AI-driven engagement in classrooms - How McGraw Hill is integrating AI into its learning platforms If you care about the future of education, AI, and ethical tech development, this episode is a must-watch. ———————————————————————————————————————— This episode is sponsored by Oracle. Oracle Cloud Infrastructure (OCI) is a blazing-fast and secure platform for your infrastructure, database, application development, plus all your AI and machine learning workloads. OCI costs 50% less for compute and 80% less for networking—so you’re saving a pile of money. Thousands of businesses have already upgraded to OCI, including MGM Resorts, Specialized Bikes, and Fireworks AI. Cut your current cloud bill in HALF if you move to OCI now: https://oracle.com/eyeonai ———————————————————————————————————————— Chapters: (00:00) The Role of AI in Augmenting Human Learning (02:10) Dylan’s Background in Learning Sciences & AI (08:23) The Risks of AI-Powered Education Tools (11:08) AI Tutoring: Can It Replace Human Teachers? (16:28) AI’s Role in Personalized Learning & Adaptive Assessments (22:47) How AI Can Assist, Not Replace, Teachers (29:36) The Future of AI-Driven Gamification in Education (36:41) Ethical Concerns Around AI Chatbots & Student Relationships (45:02) The Impact of AI on Student Learning & Memory Retention (50:19) How McGraw Hill is Innovating with AI in Education (54:44) Final Thoughts: AI’s Role in Shaping the Future of Learning…

Eye On A.I.

1
#241 Patrick M. Pilarski: The Alberta Plan’s Roadmap to AI and AGI 1:01:44

20 days ago1:01:44

1:01:44

This episode is sponsored by Netsuite by Oracle, the number one cloud financial system, streamlining accounting, financial management, inventory, HR, and more. NetSuite is offering a one-of-a-kind flexible financing program. Head to https://netsuite.com/EYEONAI to know more. Can AI learn like humans? In this episode, Patrick Pilarski, Canada CIFAR AI Chair and professor at the University of Alberta, breaks down The Alberta Plan—a bold roadmap for achieving Artificial General Intelligence (AGI) through reinforcement learning and real-time experience-based AI. Unlike large pre-trained models that rely on massive datasets, The Alberta Plan champions continual learning, where AI evolves from raw sensory experience, much like a child learning through trial and error. Could this be the key to unlocking true intelligence? Pilarski also shares insights from his groundbreaking work in bionic medicine, where AI-powered prosthetics are transforming human-machine interaction. From neuroprostheses to reinforcement learning-driven robotics, this conversation explores how AI can enhance—not just replace—human intelligence. What You’ll Learn in This Episode: Why reinforcement learning is a better path to AGI than pre-trained models The four core principles of The Alberta Plan and why they matter How AI-driven bionic prosthetics are revolutionizing human-machine integration The battle between reinforcement learning and traditional control systems in robotics Why continual learning is critical for AI to avoid catastrophic forgetting How reinforcement learning is already powering real-world breakthroughs in plasma control, industrial automation, and beyond The future of AI isn’t just about more data—it’s about AI that thinks, adapts, and learns from experience. If you're curious about the next frontier of AI, the rise of reinforcement learning, and the quest for true intelligence, this episode is a must-watch. Subscribe for more AI deep dives! (00:00) The Alberta Plan: A Roadmap to AGI (02:22) Introducing Patrick Pilarski (05:49) Breaking Down The Alberta Plan’s Core Principles (07:46) The Role of Experience-Based Learning in AI (08:40) Reinforcement Learning vs. Pre-Trained Models (12:45) The Relationship Between AI, the Environment, and Learning (16:23) The Power of Reward in AI Decision-Making (18:26) Continual Learning & Avoiding Catastrophic Forgetting (21:57) AI in the Real World: Applications in Fusion, Data Centers & Robotics (27:56) AI Learning Like Humans: The Role of Predictive Models (31:24) Can AI Learn Without Massive Pre-Trained Models? (35:19) Control Theory vs. Reinforcement Learning in Robotics (40:16) The Future of Continual Learning in AI (44:33) Reinforcement Learning in Prosthetics: AI & Human Interaction (50:47) The End Goal of The Alberta Plan…

Eye On A.I.

1
#240 Manos Koukoumidis: Why The Future of AI is Open-Source 1:06:03

23 days ago1:06:03

1:06:03

This episode is brought to you by Sonar, the creators of SonarQube Server, Cloud, IDE, and the open source Community Build. Sonar unlocks actionable code intelligence, helping to redefine the software development lifecycle by use of AI and AI agentic systems, to continuously improve quality and security while reducing developer toil. By analyzing all code, regardless of who writes it—your internal team or genAI—Sonar enables more secure, reliable, and maintainable software. Join the over 7 million developers from organizations like the DoD, Microsoft, NASA, MasterCard, Siemens, and T-Mobile, who use Sonar. Visit http://sonarsource.com/eyeonai to try SonarQube for free today. ———————————————————————————————————————— The Future of AI is Open-Source | Manos Koukoumidis on UMI & The AI Revolution Is closed AI holding back innovation? In this episode, Manos Koukoumidis, CEO of Oumi , makes the case for why the future of AI must be open-source. OUMI (Open Universal Machine Intelligence) is redefining how AI is built—offering fully open models, open data, and open collaboration to make AI development more transparent, accessible, and community-driven. Big Tech has dominated AI, but UMI is challenging the status quo by creating a platform where anyone can train, fine-tune, and deploy AI models with just a few commands. Could this be the Linux moment for AI? What You’ll Learn in This Episode: Why open-source AI is the only sustainable path forward The difference between “open-source” AI and true open AI How OUMI enables researchers and enterprises to build better AI models Why Big Tech’s closed AI systems are losing their competitive edge The impact of open AI on healthcare, science, and enterprise innovation The future of AI models—will proprietary AI survive? The AI revolution is happening—and it’s open-source. If you care about the future of AI, innovation, and ethical tech development, this episode is a must-watch. ———————————————————————————————————————— 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 ———————————————————————————————————————— (00:00) The True Meaning of Open-Source AI (02:15) The Open vs. Closed AI Debate (07:54) Why Open AI Models Are Safer (10:34) Defining Open Data (13:21)Beating GPT-4-O with an Open AI Model (16:36) Open AI in Healthcare (19:31) Why Open Models Will Dominate (23:07) How OUMI Makes AI Training Fully Accessible & Reproducible (28:44) UMI’s Collaboration with Universities (32:29) The Shift Toward Open A (36:41) Can We Build Truly Open AI Models from Scratch? (40:20) The Role of Open AI in Eliminating Bias (45:02) Will Open AI Replace Proprietary AI Models? (50:19) How OUMI Works (54:44) The Open AI Revolution Has Begun…

Eye On A.I.

1
#239 Tuhin Srivatsa: How Baseten is Disrupting AI Deployment & Scaling in 2025 46:17

29 days ago46:17

46:17

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 ————————————————————————————————————————— AI deployment is broken—can it be fixed? In this episode, Tuhin Srivatsa, CEO & Co-Founder of Baseten, reveals how his company is DISRUPTING AI infrastructure, making it easier, faster, and more cost-effective to deploy and scale AI models in production. As enterprises increasingly turn to open-source AI models and grapple with the high costs and complexity of scaling, Baseten offers a game-changing solution that eliminates bottlenecks and simplifies the process. Discover how Baseten is taking on AWS SageMaker, OpenAI, and cloud-based AI deployment platforms to reshape the future of AI model deployment. What You’ll Learn in This Episode: Why AI deployment & scaling is one of the biggest challenges in 2025 How Baseten enables enterprises to run AI models faster & more efficiently The shift from closed-source to open-source AI models—and why it matters The hidden costs of AI inference & how to optimize for performance Why most AI models fail in production and how to prevent it The future of AI infrastructure: What comes next for scalable AI Whether you’re a machine learning engineer, AI researcher, startup founder, or enterprise leader, this episode is packed with actionable insights to help you scale AI models without the headaches. Don’t miss this conversation on the next era of AI deployment! #AI #ArtificialIntelligence #MachineLearning #Baseten #AIDeployment #AIScaling #Inference #MLInfrastructure #TechPodcast Stay Updated: Craig Smith Twitter: https://twitter.com/craigss Eye on A.I. Twitter: https://twitter.com/EyeOn_AI ————————————————————————————————————————— (00:00) Tuhin Srivatsa’s Journey in AI & Baseten (01:50) What is AI Infrastructure & Why It Matters (03:30) How Baseten Optimizes AI Model Deployment (05:19) Why Most AI Deployments Fail (And How to Fix It) (09:17) The Future of Open-Source AI Models in Enterprise (11:01) How Baseten Automates AI Scaling & Inference (14:12) Why AI Developers Struggle with Cloud-Based AI Tools (18:47) The Real Cost of AI Inference (And How to Reduce It) (20:44) Why AI Scaling is the Biggest Challenge in 2025 (26:55) Can AI Run on Non-NVIDIA Chips? (The Hardware Debate) (31:23) The Future of AI Model Deployment & Inference (37:05) How AI Agents & Reasoning Models Are Changing the Game (40:39) The Truth About AI Hype vs. Reality (45:04) How to Get Started with Baseten (45:48) The Future of AI Infrastructure…

Eye On A.I.

1
#238 Dominic Williams Reveals His Vision for the Internet Computer (ICP) 1:14:52

5 weeks ago1:14:52

1:14:52

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 Dominic Williams’ Bold Vision for The Internet Computer (ICP) | The Future of Decentralized Computing The internet is broken—can blockchain fix it? In this episode, Dominic Williams, the visionary behind The Internet Computer (ICP) and founder of DFINITY, reveals his plan to build a decentralized alternative to cloud computing. Discover how ICP is challenging Big Tech, replacing traditional IT infrastructure, and creating a tamper-proof, autonomous internet powered by smart contracts. What You'll Learn in This Episode: Why Dominic Williams believes the current internet is flawed How ICP aims to replace centralized cloud providers like AWS & Google Cloud The role of smart contracts in making the internet more secure and censorship-resistant The mission of DFINITY and how it started in 2016 The future of Web3, decentralized applications (dApps), and blockchain governance Don't miss this deep dive into the future of the internet! If you're interested in blockchain, decentralization, and the next evolution of the web, this episode is for you. Stay Updated: Craig Smith Twitter: https://twitter.com/craigss Eye on A.I. Twitter: https://twitter.com/EyeOn_AI (00:00) The Origins of The Internet Computer (02:57) Dominic Williams’ Background in Tech (04:28) Early Innovations in Distributed Computing (07:08) The Birth of a 'World Computer' Concept (11:22) Reimagining IT: A Decentralized Alternative (13:45) The Creation of DFINITY and ICP (16:29) How ICP Differs from Traditional Blockchains (22:05) The Problem with Cloud-Based Blockchains (25:35) How ICP Ensures True Decentralization (29:25) AI & The Self-Writing Internet (35:24) How ICP Hosts AI & Smart Contracts (40:23) Understanding Reverse Gas and ICP’s Economy (45:03) The Vision: A Truly Decentralized Internet (49:09) How To Use The Internet Computer (52:01) The Role of Nodes & Incentives in ICP (56:53) The Future of Web3 & Decentralized Applications (01:05:49) The Misconception of ‘On-Chain’ & Blockchain Hype…

Eye On A.I.

1
#237 Pedro Domingos Breaks Down The Symbolist Approach to AI 48:12

5 weeks ago48:12

48:12

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?…

Eye On A.I.

1
#236 Pedro Domingo’s on Bayesians and Analogical Learning in AI 56:43

7 weeks ago56:43

56:43

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…

Eye On A.I.

11 weeks ago56:07