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.

• 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

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

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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?

• 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.

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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

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(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

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

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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.

• 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

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(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

This episode is sponsored by Indeed.

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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.

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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

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!

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Craig Smith Twitter: https://twitter.com/craigss

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(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!

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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

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.

In this episode of Eye on AI, Vall Herard, CEO of Saifr.ai, joins Craig Smith to explore how AI is transforming compliance in financial services.

Saifr.ai acts as a "grammar check" for regulatory compliance, ensuring AI-generated content meets SEC, FINRA, and global financial regulations. Vall explains how Saifr integrates into Microsoft Word, Outlook, and Adobe, reducing compliance risks in marketing, emails, and AI chatbots.

We also discuss Saifr.ai’s partnership with Microsoft, AI’s role in regulated industries, and how businesses can safely adopt generative AI without violating compliance laws.
- How does AI reduce compliance friction?
- Why is regulatory oversight a barrier to AI adoption?
- What does AI safety really mean for financial services?

Find out in this deep dive into AI, compliance, and the future of regulation.

Like, subscribe, and hit the notification bell for more AI insights!

Strengthen your compliance controls with AI: https://saifr.ai/

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Craig Smith Twitter: https://twitter.com/craigss
Eye on A.I. Twitter: https://twitter.com/EyeOn_AI

(00:00) Introduction to Generative AI and Compliance
(02:47) Meet Vall Herard, CEO of Saifr.ai
(05:28) What Saifr.ai Does and Its Mission
(08:25) How Saifr.ai Ensures Regulatory Compliance
(12:13) Overcoming AI Adoption Barriers in Finance
(19:58) Saifr.ai’s Partnership with Microsoft
(24:11) How SaferAI Integrates with Microsoft Office
(29:33) AI in Podcast and Audio Compliance Review
(33:54) Saifr.ai’s Business Model and Pricing
(38:09) How Saifr.ai Works with Generative AI Chatbots
(42:36) Supporting Multiple Languages for Compliance
(50:08) Future Outlook

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!

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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