Eye On A.I.

• #249 Brice Challamel: How Moderna is Using AI to Disrupt Modern Healthcare

  This episode is sponsored by Oracle. OCI is the next-generation cloud designed for every workload – where you can run any application, including any AI projects, faster and more securely for less.  On average, OCI costs 50% less for compute, 70% less for storage, and 80% less for networking. Join Modal, Skydance Animation, and today’s innovative AI tech companies who upgraded to OCI…and saved.    Offer only for new US customers with a minimum financial commitment. See if you qualify for half off at http://oracle.com/eyeonai     In this episode of Eye on AI, Craig Smith sits down with Brice Challamel, Head of AI Products and Innovation at Moderna, to explore how one of the world’s leading biotech companies is embedding artificial intelligence across every layer of its business—from drug discovery to regulatory approval.   Brice breaks down how Moderna treats AI not just as a tool, but as a utility—much like electricity or the internet—designed to empower every employee and drive innovation at scale. With over 1,800 GPTs in production and thousands of AI solutions running on internal platforms like Compute and MChat, Moderna is redefining what it means to be an AI-native company.   Key topics covered in this episode: How Moderna operationalizes AI at scale GenAI as the new interface for machine learning AI’s role in speeding up drug approvals and clinical trials The future of personalized cancer treatment (INT) Moderna’s platform mindset: AI + mRNA = next-gen medicine Collaborating with the FDA using AI-powered systems   Don’t forget to like, comment, and subscribe for more interviews at the intersection of AI and innovation.     Stay Updated: Craig Smith on X:https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI     (00:00) Preview  (02:49) Brice Challamel’s Background and Role at Moderna (05:51) Why AI Is Treated as a Utility at Moderna (09:01) Moderna's AI Infrastructure (11:53) GenAI vs Traditional ML (14:59) Combining mRNA and AI as Dual Platforms (18:15) AI’s Impact on Regulatory & Clinical Acceleration (23:46) The Five Core Applications of AI at Moderna (26:33) How Teams Identify AI Use Cases Across the Business (29:01) Collaborating with the FDA Using AI Tools (33:55) How Moderna Is Personalizing Cancer Treatments (36:59) The Role of GenAI in Medical Care (40:10) Producing Personalized mRNA Medicines (42:33) Why Moderna Doesn’t Sell AI Tools (45:30) The Future: AI and Democratized Biotech

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• #248 Pedro Domingos: How Connectionism Is Reshaping the Future of Machine Learning

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

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• #247 Barr Moses: Why Reliable Data is Key to Building Good AI Systems

  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, Craig Smith sits down with Barr Moses, Co-Founder & CEO of Monte Carlo, the pioneer of data and AI observability. Together, they explore the hidden force behind every great AI system: reliable, trustworthy data. With AI adoption soaring across industries, companies now face a critical question: Can we trust the data feeding our models? Barr unpacks why data quality is more important than ever, how observability helps detect and resolve data issues, and why clean data—not access to GPT or Claude—is the real competitive moat in AI today.   What You’ll Learn in This Episode: Why access to AI models is no longer a competitive advantage How Monte Carlo helps teams monitor complex data estates in real-time The dangers of “data hallucinations” and how to prevent them Real-world examples of data failures and their impact on AI outputs The difference between data observability and explainability Why legacy methods of data review no longer work in an AI-first world Stay Updated: Craig Smith on X:https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI     (00:00) Intro (01:08) How Monte Carlo Fixed Broken Data   (03:08) What Is Data & AI Observability?   (05:00) Structured vs Unstructured Data Monitoring   (08:48) How Monte Carlo Integrates Across Data Stacks (13:35) Why Clean Data Is the New Competitive Advantage   (16:57) How Monte Carlo Uses AI Internally   (19:20) 4 Failure Points: Data, Systems, Code, Models   (23:08) Can Observability Detect Bias in Data?   (26:15) Why Data Quality Needs a Modern Definition   (29:22) Explosion of Data Tools & Monte Carlo’s 50+ Integrations   (33:18) Data Observability vs Explainability   (36:18) Human Evaluation vs Automated Monitoring   (39:23) What Monte Carlo Looks Like for Users   (46:03) How Fast Can You Deploy Monte Carlo?   (51:56) Why Manual Data Checks No Longer Work   (53:26) The Future of AI Depends on Trustworthy Data 

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• #246 Will Granis: How Google Cloud is Powering the Future of Agentic 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     What happens when AI agents start negotiating, automating workflows, and rewriting how the enterprise world operates?   In this episode of the Eye on AI podcast, Will Grannis, CTO of Google Cloud, reveals how Google is leading the charge into the next frontier of artificial intelligence: agentic AI. From multi-agent systems that can file your expenses to futuristic R2-D2-style assistants in real-time race strategy, this episode dives deep into how AI is no longer just about models—it's about autonomous action. In this episode, we explore: How AgentSpace is transforming how enterprises build AI agents The evolution from rule-based workflows to intelligent orchestration Real-world use cases: expense automation, content creation, code generation Trust, sovereignty, and securing agentic systems at scale The future of multi-agent ecosystems and AI-driven scientific discovery How large enterprises can match startup agility using their data advantage   Whether you're a founder, engineer, or enterprise leader—this episode will shift how you think about deploying AI in the real world.   Subscribe for more deep dives with tech leaders and AI visionaries. Drop a comment with your thoughts on where agentic AI is headed!     (00:00) Preview and Intro (02:34) Will Grannis’ Role at Google Cloud (05:14) Origins of Agentic Workflows at Google (09:10) How Generative AI Changed the Agent Game (12:29) Agents, Tool Access & Trust Infrastructure (14:01) What is Agent Space? (16:30) Creative & Marketing Agents in Action (23:29) Core Components of Building Agents (25:29) Introducing the Agent Garden (28:06) The “Cloud of Connected Agents” Concept (33:53) Solving Agent Quality & Self-Evaluation (37:19) The Future of Autonomous Finance Agents (40:55) How Enterprises Choose Cloud Partners for Agents (43:50) Google Cloud’s Principles in Practice (46:27) Gemini’s Context Power in Cybersecurity (49:50) Robotics and R2D2-Inspired AI Projects (52:39) How to Try Agent Space Yourself  

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• #245 Rajat Taneja: Visa's President of Technology Reveals Their $3.3 Billion AI Strategy

  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 Visa’s President of Technology, Rajat Taneja, pulls back the curtain on the $3.3 billion AI transformation powering one of the world’s most trusted financial networks.   In this episode, Taneja shares how Visa—a company processing over $16 trillion annually across 300 billion real-time transactions—is leveraging AI not just to stop fraud, but to redefine the future of commerce.   From deep neural networks trained on decades of transaction data to generative AI tools powering next-gen agentic systems, Visa has quietly been an AI-first company since the 1990s. Now, with 500+ petabytes of data and 2,900 open APIs, it’s preparing for a future where agents, biometrics, and behavioral signals shape every interaction.   Taneja also reveals how Visa’s models can mimic bank decisions in milliseconds, stop enumeration attacks, and even detect fraud based on how you type. This is AI at global scale—with zero room for error.   What You’ll Learn in This Episode: How Visa’s $3.3B data platform powers 24/7 AI-driven decisioning The fraud models behind stopping $40 billion in criminal transactions What “agentic commerce” means—and why Visa is betting big on it How Visa uses behavioral biometrics to detect account takeovers Why Visa rebuilt its infrastructure for the AI era—10 years ahead of the curve The role of generative AI, biometric identity, and APIs in the next wave of payments   The future of commerce isn’t just cashless—it’s intelligent, autonomous, and trust-driven.   If you’re curious about how AI is redefining payments, security, and digital identity at massive scale, this episode is essential viewing.   Subscribe for more deep dives into the future of AI, commerce, and innovation. Stay Updated: Craig Smith on X:https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI (00:00) Introduction (02:57) Meet Rajat Taneja, Visa’s President of Technology (04:02) Scaling AI for 300 Billion Transactions Annually (05:27) The Models Behind Visa’s Fraud Detection (08:02) Visa’s In-House AI Models vs Open-Source Tools (10:54) Inside Visa’s $3.3B AI Data Platform (12:29) Visa’s Role in E-Commerce Innovation (16:24) Biometrics, Identity & Tokenization at Visa (21:14) Visa’s Vision for AI-Driven Commerce

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