Eye On A.I.

Brice Challamel, Head of AI Products and Innovation at Moderna and former Google Cloud executive, discusses how AI is revolutionizing modern healthcare. He explains Moderna's approach to operationalizing AI across its business, treating it as a utility to drive innovation. Listeners learn how AI expedites drug approvals and clinical trials, as well as its role in personalized cancer treatment. Challamel also touches on the company's extensive AI solutions and collaborative efforts with the FDA, showcasing a future where AI and mRNA technology redefine medicine.

Pedro Domingos, a prominent AI researcher and author of 'The Master Algorithm,' dives into Connectionism, the key driver of modern neural networks. He explores the historical trajectory from the 1940s to today's AI giants like ChatGPT. Discussing the tension between Connectionists and Symbolists, he illuminates the real-world impact of supervised learning and the struggles of reinforcement learning. Domingos also addresses the evolution of deep learning and the rise and fall of Generative Adversarial Networks, urging for diverse approaches in AI research.

Barr Moses, Co-Founder & CEO of Monte Carlo and a leader in data observability, dives into the pivotal importance of reliable data in AI systems. They discuss the dangers of 'data hallucinations' and how to prevent them, stressing that clean data is the true competitive edge over mere access to AI models. Moses highlights the evolution of data quality assessment and the instrumental role real-time monitoring plays in ensuring data accuracy and trustworthiness. Real-world examples reveal the impact of data failures on AI outputs and the need for modern solutions.

Will Grannis, CTO of Google Cloud, shares insights on the revolutionary landscape of agentic AI. He discusses how multi-agent systems can streamline tasks like expense automation and enhance operational workflows. Grannis highlights the importance of trust in AI, emphasizing secure agent management and the potential for scientific discovery. He also explains how large organizations can leverage their data for agility, comparing their pace to startups. This conversation redefines our understanding of AI's future, focusing on autonomy and innovation in technology.

Rajat Taneja, President of Technology at Visa, shares insights into the company’s bold $3.3 billion AI strategy. He reveals how Visa stops over $40 billion in fraud annually using deep learning models trained on vast transaction data. Taneja discusses the exciting concept of agentic commerce and Visa's pioneering role in AI since the 1990s. With a focus on behavioral biometrics and real-time decision-making, he explores how AI is reshaping payment security and the future of financial interactions.

Yoav Shoham, co-founder of AI21 Labs, shares his fascinating journey through the evolution of AI, blending traditional methods with modern deep learning. He discusses innovative models like Jamba, which disrupts traditional architectures, and Maestro, designed to improve enterprise AI by enhancing efficiency and reliability. Yoav also delves into the philosophical implications of understanding AI and the future of decentralized computing, revealing how these advancements can tackle real-world challenges in the industry.

Greg Osuri, founder of Akash Network, discusses the transformative power of decentralized cloud computing. He explains how his platform disrupts traditional giants like AWS by unlocking underutilized compute power and slashing costs. The conversation highlights the energy bottlenecks faced in AI training and the pivotal role of blockchain in securing cloud resources. Osuri envisions a future where AI thrives in decentralized infrastructures, ensuring data privacy and fostering a vibrant, community-driven tech ecosystem.

Dylan Arena, Chief Data Science and AI Officer at McGraw Hill, specializes in learning sciences and technology design. He discusses how AI can revolutionize education by enhancing rather than replacing the human touch. Dylan emphasizes the potential of AI-powered tools to personalize learning experiences and assist teachers without creating over-reliance on automation. He raises important questions about maintaining essential human connections in a digitally-driven learning environment and explores the ethical implications of integrating AI into education.

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  

Manos Koukoumidis, CEO of OUMI, champions the open-source AI movement, advocating for a transparent and collaborative approach to AI development. He argues that open-source is the sustainable path forward, contrasting it with the limitations of closed AI systems. The discussion delves into how OUMI empowers innovators to train and deploy AI models effortlessly, potentially leading to a pivotal moment for the industry. Koukoumidis also highlights the importance of shared resources in fostering ethical practices and diversity in AI.