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

Eye On A.I.

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

28:24

In our second installment of the Small Business Starter Kit series - we’re tackling a topic that’s sometimes tricky, sometimes confusing, but ever-present: taxes. Hosts Austin and Jannese have an insightful conversation with entrepreneur Isabella Rosal who started 7th Sky Ventures , an exporter and distributor of craft spirits, beer, and wine. Having lived and worked in two different countries and started a company in a heavily-regulated field, Isabella is no stranger to navigating the paperwork-laden and jargon-infused maze of properly understanding taxes for a newly formed small business. Join us as she shares her story and provides valuable insight into how to tackle your business’ taxes - so they don’t tackle you. Learn more about how QuickBooks can help you grow your business: QuickBooks.com See omnystudio.com/listener for privacy information.…

20 days ago 1:01:44

MP3•Episode home

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

245 episodes

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

Eye On A.I.

222 subscribers

published 20 days ago

MP3•Episode home

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.

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

245 episodes

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

All episodes

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…

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…

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…

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…

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…

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…

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?…

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…

1
#235 Vall Herard: The Future of AI-Driven Compliance (Saifr.ai) 51:55

7 weeks ago51:55

51:55

1
#234 Tyler Xuan Saltsman: How AI is Shaping the Future of Combat & Warfare 38:48

8 weeks ago38:48

38:48

In this episode of the Eye on AI podcast, Tyler Xuan Saltsman, CEO of Edgerunner, joins Craig Smith to explore how AI is reshaping military strategy, logistics, and defense technology—pushing the boundaries of what’s possible in modern warfare. Tyler shares the vision behind Edgerunner, a company at the cutting edge of generative AI for military applications. From logistics and mission planning to autonomous drones and battlefield intelligence, Edgerunner is building domain-specific AI that enhances decision-making, ensuring national security while keeping humans in control. We dive into how AI-powered military agents work, including the LoRA (Low-Rank Adaptation) model, which fine-tunes AI to think and act like military specialists—whether in logistics, aircraft maintenance, or real-time combat scenarios. Tyler explains how retrieval-augmented generation (RAG) and small language models allow warfighters to access mission-critical intelligence without relying on the internet, bringing real-time AI support directly to the battlefield. Tyler also discusses the future of drone warfare—how AI-driven, vision-enabled drones can neutralize threats autonomously, reducing reliance on human pilots while increasing battlefield efficiency. With autonomous swarms, AI-powered kamikaze drones, and real-time situational awareness, the landscape of modern warfare is evolving fast. Beyond combat, we explore AI’s role in security, including advanced weapons detection systems that can safeguard military bases, schools, and public spaces. Tyler highlights the urgent need for transparency in AI, contrasting Edgerunner’s open and auditable AI models with the black-box approaches of major tech companies. Discover how AI is transforming military operations, from logistics to combat strategy, and what this means for the future of defense technology. Don’t forget to like, subscribe, and hit the notification bell for more deep dives into AI, defense, and cutting-edge technology! Stay Updated: Craig Smith Twitter: https://twitter.com/craigss Eye on A.I. Twitter: https://twitter.com/EyeOn_AI 00:00) Introduction – AI for the Warfighter (01:34) How AI is Transforming Military Logistics( 04:44) Running AI on the Edge – No Internet Required (06:49) AI-Powered Mission Planning & Risk Mitigation (14:32) The Future of AI in Drone Warfare (22:17) AI’s Role in Strategic Defense & Economic Warfare (26:34) The U.S.-China AI Race – Are We Falling Behind? (35:17) The Future of AI in Warfare…

1
#233 Matt Price: How Crescendo is Disrupting Customer Service with Gen AI 44:53

9 weeks ago44:53

44:53

In this episode of the Eye on AI podcast, Matt Price, CEO of Crescendo, joins Craig Smith to discuss how generative AI is reshaping customer service and blending seamlessly with human expertise to create next-level customer experiences. Matt shares the story behind Crescendo, a company at the forefront of revolutionizing customer service by integrating advanced AI technology with human-driven solutions. With a focus on outcome-based service delivery and quality assurance, Crescendo is setting a new standard for customer engagement. We dive into Crescendo’s innovative approach, including its use of large language models (LLMs) combined with proprietary IP to deliver consistent, high-quality support across 56 languages. Matt explains how Crescendo’s AI tools are designed to handle routine tasks while enabling human agents to focus on complex, empathy-driven interactions—resulting in higher job satisfaction and better customer outcomes. Matt highlights how Crescendo is redefining the BPO industry, combining AI and human capabilities to reduce costs while improving the quality of customer interactions. From enhancing agent retention to enabling scalable, multilingual support, Crescendo’s impact is transformative. Discover how Matt and his team are designing a future where AI and humans work together to deliver exceptional customer experiences—reimagining what’s possible in the world of customer service. Don’t forget to like, subscribe, and hit the notification bell for more insights into AI, technology, and innovation! Stay Updated: Craig Smith Twitter: https://twitter.com/craigss Eye on A.I. Twitter: https://twitter.com/EyeOn_AI (00:00) Introduction to Matt Price and Crescendo (01:49) The rise of AI in customer service (05:34) Using AI and human expertise for better customer experiences (07:47) How Gen AI reduces costs and improves engagement (09:37) Challenges in customer service design and innovation (11:32) Moving from hidden chatbots to front-and-center customer interaction (14:08) Training human agents to work seamlessly with AI (17:02) Using AI to analyze and improve service interactions (19:15) Outcome-based pricing vs traditional headcount models (21:53) Improving contact center roles with AI integration (25:08) The importance of curating accurate knowledge bases for AI (28:05) Crescendo’s acquisition of PartnerHero and its impact (30:39) Scaling customer service with AI-human collaboration (32:06) Multilingual support: AI in 56 languages (33:49) The vast market potential of AI-driven customer service (36:28) How Crescendo is reshaping customer service with AI innovation (42:42) Building customer profiles for personalized support…

1
#232 Sepp Hochreiter: How LSTMs Power Modern AI System’s 51:08

9 weeks ago51:08

51:08

In this special episode of the Eye on AI podcast, Sepp Hochreiter, the inventor of Long Short-Term Memory (LSTM) networks, joins Craig Smith to discuss the profound impact of LSTMs on artificial intelligence, from language models to real-time robotics. Sepp reflects on the early days of LSTM development, sharing insights into his collaboration with Jürgen Schmidhuber and the challenges they faced in gaining recognition for their groundbreaking work. He explains how LSTMs became the foundation for technologies used by giants like Amazon, Apple, and Google, and how they paved the way for modern advancements like transformers. Topics include: - The origin story of LSTMs and their unique architecture. - Why LSTMs were crucial for sequence data like speech and text. - The rise of transformers and how they compare to LSTMs. - Real-time robotics: using LSTMs to build energy-efficient, autonomous systems. The next big challenges for AI and robotics in the era of generative AI. Sepp also shares his optimistic vision for the future of AI, emphasizing the importance of efficient, scalable models and their potential to revolutionize industries from healthcare to autonomous vehicles. Don’t miss this deep dive into the history and future of AI, featuring one of its most influential pioneers. (00:00) Introduction: Meet Sepp Hochreiter (01:10) The Origins of LSTMs (02:26) Understanding the Vanishing Gradient Problem (05:12) Memory Cells and LSTM Architecture (06:35) Early Applications of LSTMs in Technology (09:38) How Transformers Differ from LSTMs (13:38) Exploring XLSTM for Industrial Applications (15:17) AI for Robotics and Real-Time Systems (18:55) Expanding LSTM Memory with Hopfield Networks (21:18) The Road to XLSTM Development (23:17) Industrial Use Cases of XLSTM (27:49) AI in Simulation: A New Frontier (32:26) The Future of LSTMs and Scalability (35:48) Inference Efficiency and Potential Applications (39:53) Continuous Learning and Adaptability in AI (42:59) Training Robots with XLSTM Technology (44:47) NXAI: Advancing AI in Industry…

1
#231 Paras Jain: The Future of AI Video Generation with Genmo 47:49

10 weeks ago47:49

47:49

In this episode of the Eye on AI podcast, Paras Jain, CEO and Co-founder of Genmo, joins Craig Smith to explore the cutting-edge world of AI-driven video generation, the open-source revolution, and the future of creative storytelling. Paras shares the story behind Genmo, a company at the forefront of advancing video generation technologies, and their groundbreaking model, Mochi One. With a focus on motion quality and prompt adherence, Genmo is redefining what's possible in generative AI for video, offering unmatched precision and creative possibilities. We delve into the innovative approach behind Mochi One, including its state-of-the-art architecture, which enables fast, high-quality video generation. Paras explains how Genmo’s commitment to open source empowers developers and researchers worldwide, fostering rapid advancements, customization, and the creation of new tools, like video-to-video editing. The conversation touches on key themes such as scalability, synthetic data pipelines, and the transformative potential of AI in creating immersive virtual worlds. Paras also explores how Genmo is bridging the gap between cutting-edge AI and practical applications, from TikTok-ready videos to future possibilities like interactive environments and real-time video game-like experiences. Discover how Paras and his team are shaping the future of video creation, blending art, science, and open collaboration to push the boundaries of generative AI. Don’t forget to like, subscribe, and hit the notification bell for more insightful conversations on AI, technology, and innovation! Stay Updated: Craig Smith Twitter: https://twitter.com/craigss Eye on A.I. Twitter: https://twitter.com/EyeOn_AI (00:00) Introduction to Paras Jain and Genmo (01:45) Video generation with Mochi One (04:41) Open-source AI in video generation (06:08) Building Mochi One (12:03) Simulating complex physics in video models (14:35) Reducing latency: Fast video generation at scale (20:34) Tackling cost challenges for longer videos (23:14) Character consistency in AI-generated videos (27:17) Why video models represent intelligence's next frontier (30:18) How diffusion models create sharp, realistic videos (34:02) Visualizing the denoising process in video generation (39:36) Exploring user-generated video creations with Mochi One (42:05) Monetizing open-source AI for video generation (45:47) Video generation's potential in the metaverse (47:19) Collaborating with universities to advance AI (48:39) The future of generative AI…

1
#230 Jamie Lerner: How Quantum Solves AI’s Need for Unstructured Data Solutions 53:23

10 weeks ago53:23

53:23

This episode is sponsored by Oracle. Oracle Cloud Infrastructure, or 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 In this episode of the Eye on AI podcast, Jamie Lerner, CEO of Quantum, joins Craig Smith to discuss the future of data storage, unstructured data management, and AI’s transformative role in modern workflows. Jamie shares his journey leading Quantum, a company revolutionizing the storage and management of unstructured data for industries like healthcare, media, and AI research. With decades of expertise in creating innovative data solutions, Quantum is at the forefront of enabling efficient, secure, and scalable data workflows. We dive into Quantum’s cutting-edge technologies, from high-speed flash storage systems like the Myriad file system to cost-effective, long-term archival solutions such as tape systems. Jamie unpacks how Quantum supports AI-powered workflows, enabling seamless data movement, metadata tagging, and policy-driven automation for unstructured data like medical imaging, genomics, and video archives. Jamie also explores the critical role of data sovereignty in today’s global landscape, the growing importance of "forever archives," and how Quantum’s tools help organizations balance exponential data growth with flat budgets. He sheds light on innovations like synthetic DNA and compressed storage mediums, providing a glimpse into the future of data storage. Don’t forget to like, subscribe, and hit the notification bell for more engaging discussions on AI, technology, and innovation! Stay Updated: Craig Smith Twitter: https://twitter.com/craigss Eye on A.I. Twitter: https://twitter.com/EyeOn_AI (00:00) Introduction to Jamie Lerner and Quantum (02:21) Quantum’s Focus on Unstructured Data Storage (05:19) Structured vs. Unstructured Data: Key Differences (07:52) Managing Data Workflows with AI and Automation (10:55) Quantum’s Role in Long-Term Data Archives (13:32) Data Sovereignty and Security (16:18) How Data is Stored and Protected Across Mediums (19:54) Metadata in AI and Data Management (21:29) Quantum’s Role in Building Forever Archives (24:16) Tape Storage: Efficiency and Longevity (29:11) Innovations in Data Storage (34:39) Competing in the Evolving Data Storage Industry (37:56) Innovations in Flash Storage (40:55) Balancing Cost and Efficiency in Data Storage (44:28) The Future of Data Storage and AI Integration (50:07) Quantum’s Vision for the Future…

1
#229 Mitesh Agrawal: Why Lambda Labs’ AI Cloud Is a Game-Changer for Developers 56:07

11 weeks ago56:07