In this episode, we sit down with Brian Lucena, Principal at Numeristical, and an experienced educator, consultant, and open-source contributor. Brian has advised companies of all sizes on applying modern machine learning techniques and is the creator of popular Python packages like StructureBoost, ML-Insights, and SplineCalib. He has also taught at UC-Berkeley, Brown University, and USF, bringing a unique mix of academic depth and real-world ML expertise.

Today, we explore how to make more reliable predictions in machine learning. From the dominance of gradient boosting for tabular data to the power of probabilistic regression and uncertainty quantification, Brian shares expert insights into building trustworthy ML models. We also dive into probability calibration, model drift, and best practices for ensuring model reliability in production.

Whether you're an ML engineer, data scientist, or business leader looking to improve your AI models, this episode is packed with practical takeaways you won’t want to miss.

 Key Topics Covered
✅ Gradient Boosting vs. Deep Learning – Why decision trees still dominate tabular data and structured business problems.
✅ Probabilistic Regression – Moving beyond point estimates to provide probability distributions and confidence intervals.
✅ Uncertainty Quantification – Understanding the limits of machine learning predictions and why it matters.
✅ Probability Calibration – How to ensure your model’s confidence scores are truly reliable.
✅ Handling Model Drift – Strategies to maintain model performance in a changing world.
✅ Real-World Use Cases – Applications in finance, healthcare, risk modeling, and business decision-making.

Resources & Tools Mentioned
Brian’s Youtube Channel: https://www.youtube.com/c/numeristical
Brian’s Linkedin: https://www.linkedin.com/in/brianlucena/
🛠️ StructureBoost – Brian’s open-source package for structured categorical variables in gradient boosting: https://github.com/numeristical/structureboost
📦 ML-Insights – Tools for better understanding ML models: https://github.com/numeristical/introspective
SplineCalib – A library for improving probability calibration: 🔗https://github.com/numeristical/splinecalib
📌 NGBoost – A gradient boosting approach for probabilistic regression: https://stanfordmlgroup.github.io/projects/ngboost/
🔗 GitHub for NGBoost: https://github.com/stanfordmlgroup/ngboost
📌 XGBoost – A powerful gradient boosting framework: https://github.com/dmlc/xgboost
📌 CatBoost – Gradient boosting with native support for categorical features: https://github.com/catboost/catboost
📌 LightGBM – A fast, efficient gradient boosting library: https://github.com/microsoft/LightGBM
📊 PyMC – A Bayesian probabilistic programming library for uncertainty modeling: https://github.com/pymc-devs/pymc

 Memorable Quotes
💬 "Businesses don’t just want a number—they need to understand a range of possible outcomes. That’s where probabilistic regression makes all the difference."
💬 "One of the biggest challenges in real-world ML is that the world doesn’t stay the same—models can drift, and retraining isn’t always the best solution."
💬 "Gradient boosting still outperforms deep learning for structured data because it handles sharp decision boundaries better."

This episode was sponsored by:
🎤 ODSC East 2025 – The Leading AI Builders Conference – https://odsc.com/boston/
Join us from May 13th to 15th in Boston for hands-on workshops, training sessions, and cutting-edge AI talks covering generative AI, LLMOps, and AI-driven automation.


How to Make More Reliable Predictions in Machine Learning with Brian Lucena

In this episode of the ODSC AI Podcast, host Sheamus McGovern, founder of ODSC, sits down with Hugo Shi, Co-Founder and CTO of Saturn Cloud, a platform that gives data scientists and ML engineers the tools and flexibility they need to work the way they want. From his early days as a desk quant during the 2008 financial crisis to founding Saturn Cloud, Hugo brings a wealth of experience across finance, open source, and AI infrastructure.

This conversation dives deep into the realities of building AI infrastructure at scale, advocating for self-service tools for AI practitioners, managing cloud costs, and why flexibility—not control—is the foundation for productive data teams. It’s a must-listen for anyone working with machine learning infrastructure, whether you're a beginner navigating your first platform or a seasoned engineer scaling multi-cloud operations.

🌟 Key Topics Covered:
- Hugo’s career journey: From quant finance to co-founding Anaconda and then Saturn Cloud
- What working as a desk quant during the 2008 crisis taught him about speed, impatience, and iteration
- The pivotal role Anaconda played in democratizing Python data science
- Why Saturn Cloud was founded: common infra pain points across data teams
- How Saturn Cloud empowers teams through:
- - Interactive compute environments
- - Scheduled jobs
- - Long-running deployments
- The importance of flexibility vs. opinionated platforms 
- Why data scientists should not suffer in silence over infra pain
- Hidden cloud costs: compute, storage, and network—and how to manage them
- Differences between AI cloud providers (CoreWeave, Lambda Labs) and traditional hyperscalers (AWS, Azure, GCP)
- Scaling AI: lessons from working with massive clusters and thousands of parallel jobs
- Security best practices in ML platforms, including role-based access and cost attribution
- Why ML teams should collaborate across IT, product, and data engineering
- Hard-won lessons from real-world AI infrastructure scaling

💬 Memorable Outtakes:
On infrastructure friction and self-advocacy: “Data scientists, ML engineers, and AI engineers suffer in silence… They don’t perceive themselves as tech experts, so they think they have to accept infrastructure pain. They shouldn’t.”
On why Saturn Cloud avoids being too opinionated: “Notebooks are fine—but making them the only way to work? That’s a career trap. People should graduate to full IDEs and better practices.”
On scaling AI operations: “What can be done, will be done. If it’s possible, someone will try it. At scale, low-probability failures become inevitable.”

💻 References & Resources:
Hugo Shi:
- LinkedIn: https://www.linkedin.com/in/hugo-shi
- CTO & Co-Founder, Saturn Cloud: https://www.saturncloud.io/
Mentioned Companies and Tools:
- Saturn Cloud: https://www.saturncloud.io/
- Anaconda: https://www.anaconda.com/
- Phoenix Framework( For evaluation) https://github.com/phoenixframework/phoenix
- Prometheus (for resource monitoring): https://prometheus.io/
- CoreWeave: https://www.coreweave.com/
- Lambda Labs: https://lambdalabs.com/
- Neurelo (Neas): https://www.neurelo.com/
- RunPod: https://www.runpod.io/
- Cursor IDE: https://www.cursor.so/
- Streamlit: https://streamlit.io/
- Jupyter: https://jupyter.org/
- PyCharm: https://www.jetbrains.com/pycharm/

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What No One Tells You About AI Infrastructure with Hugo Shi

AI Agents in Action: Memory, Messaging, and MCP with Michael Lanham

In this episode of the ODSC Podcast, Alex Landa, the head of content at ODSC, interviews Nick Walton, CEO and co-founder of Latitude and creator of the groundbreaking AI-powered storytelling game, AI Dungeon. Nick shares his journey from machine learning for self-driving cars to reimagining interactive fiction through generative models. They dive into the challenges and magic of building persistent, emergent game worlds with AI, and discuss how AI enables new modes of player agency and storytelling that go far beyond traditional game mechanics.
🔥 Key Topics Covered:
-The origin story of AI Dungeon and how GPT-2 sparked its creation
-How AI Dungeon enables open-ended, player-driven storytelling
-Technical and creative challenges in AI gaming: model curation, memory, and narrative tension
-What makes AI-generated characters compelling and emotionally resonant
-The upcoming game engine Latitude is building to power rich, persistent AI-driven worlds
-How AI can unlock new creative possibilities for indie and non-technical game creators
-Multiplayer and long-term visions for dynamic, socially evolving AI worlds
-Ethical considerations in using AI for gaming and storytelling
Memorable Outtakes:
💬 Nick Walton: “If every dwarf you meet is a gruff miner whose clan got eaten by goblins, it gets a little old after a while... you need higher variation.”
💬 Nick Walton: “You’re not just playing a game. You're building a story, and when loss happens, it becomes meaningful. That's the magic of AI Dungeon.”
💬 Nick Walton: “We’re building not just a game, but a living world—one where your choices shape everything, and the AI evolves with you.”
💬  Alex (Host): “It’s not about replacing anyone—it’s about creating something entirely new that couldn't exist without AI.”
🖥 References & Resources:
- AI Dungeon: https://aidungeon.com
-Latitude (Nick’s company): https://latitude.io
-Whispers from the Star (AI game mentioned): https://store.steampowered.com/app/3730100/Whispers_from_the_Star/
-Nick Walton’s X (Twitter) profile: https://twitter.com/nickwalton00
-Nick Walton’s LinkedIn: https://www.linkedin.com/in/waltonnick/
-Nick Walton's profile on Crunchbase: https://www.crunchbase.com/person/nick-walton
-AI Dungeon Dev Blog: https://latitude.io/blog


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ODSC West 2025 – The Leading AI Training Conference
Join us in San Francisco from October 28–30 for expert-led sessions on generative AI, LLMOps, and AI-driven automation.
🔥 Use code podcast for 10% off any ticket.
Learn more: https://odsc.com/california


Creating Unique Narrative Experiences with Generative AI in Gaming with Nick Walton

In this episode of the ODSC Podcast, host Sheamus McGovern is joined by Veronika Durgin, Vice President of Data at Saks. Veronika brings over two decades of experience in data engineering, platform architecture, data modeling, and analytics. She joins us to expand on her ODSC presentation, The 10 Most Neglected Data Engineering Tasks, offering practical insights into often-overlooked practices that can make or break modern data systems. From defining what “done” really means to preparing for seasonality and building self-recovering pipelines, Veronika shares hard-won lessons, entertaining stories, and a deep well of practical advice.

💻 Key Topics Covered:
- Veronika’s unconventional career journey from biology to data leadership
- The “bridge strategy” in build vs. buy decisions and how it improves agility
- Why “definition of done” must include more than just working code
- The value of empathy: walking a mile in the business team’s shoes
- The dangers of ignoring business seasonality in data pipelines
- What makes a truly self-healing data pipeline
- Testing with production data vs. data in production
- Handling date/time data with precision and naming conventions
- The forgotten bucket of work: tech debt, improvements, and innovation
- The environmental impact of data systems and responsible engineering
- The rise of “vibe coding” and the challenges it poses for data engineering

💬 Memorable Outtakes:
- “If you don't know why you're working on this and who needs it, you shouldn't be working on it.” 
- “Everything we build today is tomorrow's legacy. Build things to be replaceable.”  
- “We automate, not because we’re lazy—but because we care about sleeping at night.” 

🖥 References & Resources:
- Veronika Durgin on LinkedIn: https://www.linkedin.com/in/vdurgin/
- Veronika Durgin on Medium: https://veronikadurgin.medium.com
- The Phoenix Project (pdf of book): https://shorturl.at/Ce5gA
- “Definition of Done”: https://medium.com/@durginv/definition-of-done-eea52c472cc3
- Tsedal Neeley Digital Mindset (book referenced): https://www.tsedal.com/book/the-digital-mindset/
- Stargate AI data center project (referenced during energy discussion): https://openai.com/index/announcing-the-stargate-project/

Sponsored by:
🔥 ODSC West 2025 – The Leading AI Training Conference
Join us in San Francisco from October 28th–30th for expert-led sessions on generative AI, LLMOps, and AI-driven automation.
Use the code podcast for 10% off any ticket.
Learn more: https://odsc.com/california

The Most Neglected Tasks in Data Engineering with Veronika Durgin

In this episode of the ODSC AI Podcast, host Sheamus McGovern speaks with Ian Cairns, cofounder and CEO of Freeplay, a platform built to help teams evaluate, monitor, and iterate on LLM and agent-based systems in production. Ian brings a deep product background from Twitter, Gnip, and Mapbox, and offers an insider’s look into what it actually takes to make AI work beyond the prototype phase. The conversation centers on evaluation — widely regarded as one of the most difficult and underdeveloped aspects of deploying AI in 2025.

Key Topics Covered:
- The real-world AI maturity curve: from vibe prompting to production
- Offline vs. online evaluation: definitions, trade-offs, and tooling
- Why teams struggle post-deployment — and how to break through the “we don’t know what’s going wrong” phase
- Evaluation challenges with agents, memory, RAG, and tool use
- The role of observability, telemetry, and human-in-the-loop review
- Lessons learned from Freeplay customers, including Postscript
- The growing importance of domain experts in evaluation workflows
- Building multi-layer eval architectures for agent systems
- Voice agent challenges — like turn detection and latency
- Emerging roles like AI Evaluation Engineer and how orgs should staff for evaluation maturity

Memorable Outtakes:
- "The most mature teams start with their evals. They define what good looks like, then hill-climb toward that metric."
- "The breakthrough in quality comes from people getting close to the data. Sometimes, thousands of rows."

References & Resources:
- Freeplay website: https://www.freeplay.ai
- Deployed: The AI Product Podcast by Freeplay: https://open.spotify.com/show/6nZS3a7iYb2EzHcl78iNmi?si=de766e786a41461c&nd=1&dlsi=0cb3351f79644bfc
- Freeplay blog: https://www.freeplay.ai/blog
- Freeplay community newsletter: https://freeplay.ai/newsletter
- PipeCat (open-source voice agent toolkit): https://github.com/pipecat-ai/pipecat
- OpenTelemetry (agent observability framework): https://opentelemetry.io/
- Postscript (Freeplay customer case mentioned): https://www.postscript.io
- Colorado AI community meetups: https://www.boulderaibuilders.org/

Speaker Bio:
Ian Cairns is the CEO and co-founder of Freeplay. Previously, he served as Head of Product for Twitter’s Developer Platform, where he helped grow their enterprise data business from $40M to $400M ARR. He’s also worked at Gnip (acquired by Twitter), Mapbox, and in the Obama administration on open data initiatives. LinkedIn: https://www.linkedin.com/in/iancairns/

Sponsored by:
 🔥 ODSC West 2025 – The Leading AI Training Conference
 Join us in San Francisco from October 28th–30th for expert-led sessions on generative AI, LLMOps, and AI-driven automation.
 Use the code podcast for 10% off any ticket.
 Learn more: https://odsc.com/california

The Hardest Problem in AI: Evaluation in 2025 with Ian Cairns

In this episode of ODSC’s AiX Podcast, Sheamus McGovern sits down with Dan Huss, the Founder & CEO of Gravity AI, to unpack how AI is changing product management. The conversation explores how AI is reshaping product management, from evaluating AI’s true value, avoiding AI washing, and differentiating between AI as a feature versus AI as a core product, to Dan’s concept of the Minimum Viable Experiment (MVE) and Minimum Viable AI (MVAI ) as an alternative to MVP. They also dive into the evolving skill sets product managers need in the AI era, cross-functional collaboration with data science teams, and responsible AI implementation.

Key Topics Covered:
- MVAI (Minimum Viable AI)  and the future of product management.
- Why MVP gets bloated in the enterprise and why MVE (Minimum Viable Experiment) prioritizes learning over shipping.
- A product lens for AI decisions using desirability, feasibility, viability — including consequences when models are wrong.
- AI-as-a-feature vs. AI-as-a-product — what changes in data strategy and UI (e.g., conversational interfaces).
- Where AI impacts the PM workflow: user stories, specs, research synthesis, and “vibe coding” for rapid prototypes.
- Collaboration playbook for PMs ↔ Data Science / AI Engineering on hallucinations, accuracy, monitoring, and risk.
- Practical MLOps: catalogs, deployment, observability, and compliance (e.g., EU AI Act implications).
- Avoiding AI washing: How to find low‑risk, high‑learning internal use cases and build toward differentiation

Memorable Outtakes:
- “Ditch MVP. Let’s call it an MVE—Minimum Viable Experiment. Put the emphasis on learning, not building a ‘version one’ that gets bloated.”
- “Product management is fundamentally a communications role. Anywhere there’s a language task, AI will become a collaborator.”
References & Resources:

Guest & Company
- - Dan Huss (Founder & CEO, Gravity AI) — LinkedIn: https://www.linkedin.com/in/danielhuss/
Gravity AI: https://www.gravity-ai.com/
Concepts, Tools & Mentions from the Episode
- Minimum Viable Product: https://en.wikipedia.org/wiki/Minimum_viable_product
- The Lean Startup https://en.wikipedia.org/wiki/The_Lean_Startup
- AI Snake Oil (book/site): https://www.aisnakeoil.com
- Podcast Episode - The AI Superintelligence Myth with Arvind Narayanan: https://creators.spotify.com/pod/profile/ai-x-podcast/episodes/The-AI-Superintelligence-Myth-with-Arvind-Narayanan-e32it16
- EU AI Act (overview resource): https://artificialintelligenceact.eu/
- Notion: https://www.notion.so/ 
- Figma: https://www.figma.com/

Sponsored by:
 🔥 ODSC West 2025 – The Leading AI Training Conference
Join us in San Francisco from October 28th–30th for expert-led sessions on generative AI, LLMOps, and AI-driven automation.
Use the code podcast for 10% off any ticket.
Learn more: https://odsc.com/california

Minimum Viable AI: Redefining How We Build Products with Dan Huss

In this special episode of the ODSC AI+ Podcast, host Sheamus McGovern dives into the real-world impact of GPT-5—from routing and hallucination issues to cost savings and open-weight models.

Joining him are two expert guests:
- Ivan Lee: Founder and CEO of Datasaur, who helps enterprises build private LLM stacks and has deep experience evaluating model upgrades.
-Nir Gazit: Co-founder and CEO of Traceloop, and co-creator of the OpenTelemetry Generative AI SIG, who brings insight into model routing, evaluation strategies, and observability tooling.
Together, they unpack what GPT-5 actually changed—and what teams should do next.

Key Topics Covered:
-Why GPT-5’s biggest shift is routing, not reasoning
-What casual vs. power users gained (or lost) with the rollout
-Hallucination benchmarks vs. real-world results
-Evaluation strategies using open-source tools like Phoenix and LangChain
-OpenAI’s OSS model release and its enterprise implications
-Why developers worry about black-box routing and the lack of traceability
-How to migrate safely: pinning snapshots, running evals, shadow testing
-Whether GPT-5 gets us closer to AGI, or just better infrastructure
-What to expect from agent workflows, tool selection, and model specialization

Memorable Outtakes:
- Ivan Lee: “GPT-5 is an upgrade for 98% of users—but for the power users, the loss of model choice felt like control was taken away.”
- Nir Gazit: “Of course, every new model crushes it on benchmarks—they’re optimizing for the benchmarks. That doesn’t mean it works for your use case.”
- Ivan Lee: “OpenAI’s OSS release might be the bigger story than GPT-5. Suddenly, enterprises are back at the table.”

References & Resources:
Guests
- Ivan Lee – CEO of Datasaur
Website: https://www.datasaur.ai
LinkedIn: https://www.linkedin.com/in/iylee/
- Nir Gazit – CEO of Traceloop
Website: https://www.traceloop.com
Blog: https://www.traceloop.com/blog
LinkedIn: https://www.linkedin.com/in/nirga/

Resources Mentioned
- OpenAI GPT-5  https://openai.com/gpt-5
- OpenTelemetry Project: https://opentelemetry.io
- Traceloop OpenLLMetry: https://www.traceloop.com/openllmetry
- Phoenix (Arize AI open-source evals): https://github.com/Arize-ai/phoenix
- LangChain Evals: https://python.langchain.com/api_reference/langchain/evaluation.html
- GPT-OSS Open Weight Models by OpenAI: https://platform.openai.com/docs/models/gpt-oss
- Claude + Model Context Protocol (Anthropic): https://docs.anthropic.com/en/docs/tool-use
- ARC-AGI Leaderboard: https://arcprize.org/leaderboard

Sponsored by:
🔥 ODSC AI West 2025 – The Leading AI Training Conference
Join us in San Francisco from October 28th–30th for expert-led sessions on generative AI, LLMOps, and AI-driven automation.
Use the code podcast for 10% off any ticket.
Learn more: https://odsc.ai 


GPT-5 Unboxed: What Changed, What Broke, and What’s Next with Ivan Lee and Nir Gazit

In this episode of the ODSC Ai X Podcast, host Sheamus McGovern sits down with Dr. Nataliya Kosmyna, neuroscientist and researcher at MIT Media Lab, to explore her viral research paper “Your Brain on ChatGPT.” Dr. Kosmyna discusses the study’s startling findings on how AI writing tools like ChatGPT can impact memory, learning, cognitive engagement, and even long-term brain development. She also shares how science fiction inspired her career in brain-computer interfaces and offers powerful insights for educators, technologists, and everyday users of generative AI.

Key Topics Covered:
- What “cognitive debt” means and how it differs from “cognitive offloading” and the “Google effect”
- How over-reliance on LLMs like ChatGPT can reduce cognitive engagement and memory recall
- The role of EEG and neural connectivity in assessing cognitive load during essay writing
- Why essays written with ChatGPT lack originality, personal voice, and ownership
- The potential dangers of using LLMs in education—especially among developing brains
- What Session 4 of the study revealed about users’ inability to adapt once LLM support was removed
- Concrete strategies for using AI responsibly and mitigating long-term cognitive risk
- How AI tools should be designed with the human brain—and human development—in mind
- The hidden energy and environmental costs of constant AI use
- Why younger users may be the most vulnerable to long-term cognitive effects

Memorable Outtakes:
- "There is no cognitive credit card. You cannot pay this debt off." — Dr. Nataliya Kosmyna
- "You do not talk to a calculator about your feelings." — Dr. Nataliya Kosmyna, on why LLMs are fundamentally different from traditional tools
- "If you don’t feel ownership over your work, what is there left to remember?" — Dr. Kosmyna on the link between memory encoding and cognitive agency

References & Resources:
- Paper: Your Brain on ChatGPT https://arxiv.org/pdf/2506.08872
- Dr. Nataliya Kosmyna –https://www.media.mit.edu/people/nkosmyna/overview/
- MIT Media Lab – https://www.media.mit.edu/
- Brain & LLM Project – https://www.brainonllm.com/
- MIT OpenCourseWare (mentioned during the interview) – https://ocw.mit.edu/

Sponsored by:
 🔥 ODSC AI West 2025 – The Leading AI Training Conference
Join us in San Francisco from October 28th–30th for expert-led sessions on generative AI, LLMOps, and AI-driven automation.
Use the code podcast for 10% off any ticket.
Learn more: https://odsc.ai

Your Brain on ChatGPT with Nataliya Kosmyna

In this episode of the ODSC Ai X Podcast, host Alex Landa sits down with Julian Togelius, Associate Professor in the Department of Computer Science and Engineering at New York University and Co-Director of the NYU Game Innovation Lab.

Julian is a pioneer in the intersection of artificial intelligence and gaming, with groundbreaking work on AI for game design, computational creativity, and the role of AI as both a research tool and creative collaborator. He’s also the co-founder of modl.ai, a company developing AI for game quality assurance.

Together, Alex and Julian explore how AI is transforming game design, what’s remained the same since the early days of AI in games, and what the future may hold for creativity, interactivity, and NPCs.

Key Topics Covered:
Julian’s academic journey from philosophy and psychology into computer science and AI research.
The origins of AI in games, from Turing’s chess experiments to reinforcement learning milestones.
How AI can be used not only to play games but also to design them.
The evolution from the first edition of Artificial Intelligence and Games (2018) to the expanded second edition (2025).
The tension between human creativity and AI-generated content in the gaming industry.
Challenges and opportunities of using AI to create immersive NPCs and dynamic game worlds.
The future of AI in gaming, from neural game engines to experience-driven content generation.
Insights into ongoing projects at the NYU Game Innovation Lab and at modl.ai.

Memorable Outtakes:
“Games are designed not to need AI… Just putting AI solutions into existing designs is not going to help. You need to design from the ground up for the AI capabilities.” – Julian Togelius
“One of the most productive things you can do is to play with these models and allow yourself to be provoked and outraged—to do more out-there things you wouldn’t otherwise.” – Julian Togelius
“At some point, someone is going to do an RPG that features characters you can actually talk to—and that’s going to revolutionize how we think about RPGs.” – Julian Togelius

References and Resources:
About Julian:
Website: http://julian.togelius.com/
LinkedIn: https://www.linkedin.com/in/togelius/
NYU Page: https://engineering.nyu.edu/faculty/julian-togelius
Twitter/X: https://x.com/togelius
Blog: https://togelius.blogspot.com/
Artificial Intelligence and Games book: https://gameaibook.org/
modl AI Engine business: https://modl.ai/

Other Resources Mentioned:
Podcast with Nick Walton on AI and Games: https://opendatascience.com/ai-dungeon-and-the-future-of-ai-powered-storytelling-a-conversation-with-creator-nick-walton/
AI Dungeon: https://aidungeon.com/
ODSC talk “Playing Super Smash Bros with Agentic Gemini”: https://www.youtube.com/watch?v=AR0o9DLF0H0
Turochamp, the Alan Turing chess game: https://en.wikipedia.org/wiki/Turochamp
Reinforcement learning for games: https://opendatascience.com/the-paladin-the-cleric-and-the-reinforcement-learning/
The NYU Game Innovation Lab: https://game.engineering.nyu.edu/

Sponsored by:
🔥 ODSC AI West 2025 – The Leading AI Training Conference
Join us in San Francisco from October 28th–30th for expert-led sessions on generative AI, LLMOps, and AI-driven automation.
Use the code podcast for 10% off any ticket.


From Turing’s Chess to Neural Game Engines: AI in Video Games Today with Julian Togelius

In this episode of the ODSC AiX Podcast, host Sheamus McGovern reconnects with Paige Bailey, Engineering Lead at Google DeepMind for the Developer Experience team. Paige shares how the Gemini ecosystem has evolved since her last appearance, including the launch of Gemini 2.5 DeepThink, multimodal video generation with Veo 3, real-time music creation with Lyria RT, and groundbreaking advances in agentic and on-device AI systems. The conversation explores the rapid rise of agent-based workflows, AI-powered robotics, and the growing divide between cutting-edge tools and real-world adoption.

Key Topics Covered:
Gemini 2.5 DeepThink & Reasoning Models
The model that won gold at the International Mathematical Olympiad (IMO)
Use cases for DeepThink, Pro, Flash, and FlashLite variants
Using Gemini Live API for real-world robotics and decision planning
Role of multimodal inputs (video, audio, text) in enabling embodied AI
On-Device AI & Ubiquity
Implications for edge deployment, cost reduction, and accessibility
Veo 3: Multimodal Video Generation
Lyria RT: Real-Time Music Generation
Gemini Live API & Voice Interfaces
Real-time bidirectional voice, screen understanding, and tool calling
Rise of voice as the dominant AI interface
Use of SynthID and digital watermarking to detect deepfakes
Future of AI-agent orchestration via MCP servers

Memorable Outtakes:
On the pace of model development: “A 4-billion parameter model on-device now outperforms our best cloud model from six months ago. That’s pretty magical.”  — Paige Bailey 
On the role of AI agents in robotics: “You can say, ‘Hey robot, go get me that apple,’ and Gemini will plan the task, route it, and call the right control models.” — Paige Bailey 
On the AI adoption gap: “In the Bay Area, we use AI hourly. But when I talk to developers in the Midwest, they often aren’t using it at all.” — Paige Bailey 

References & Resources:
Paige Bailey
Dynamic Web Paige: https://webpaige.dev/
LinkedIn: https://www.linkedin.com/in/dynamicwebpaige
GitHub: https://github.com/dynamicwebpaige
Medium: https://medium.com/@dynamicwebpaige
Previous podcast with Paige: https://podcasters.spotify.com/pod/show/ai-x-podcast/episodes/Googles-AI-Powered-Tools-for-Data-Scientists-Building-the-Automated-Future-of-Data-Science-with-Paige-Bailey-e2p3t6e

Resources mentioned
International Mathematical Olympiad (IMO): https://www.imo-official.org
Model Context Protocol (MCP): https://modelcontextprotocol.io/docs/getting-started/intro
Gemini 2.5 Deep Think: https://blog.google/products/gemini/gemini-2-5-deep-think/
Veo 3: https://deepmind.google/technologies/veo/
Lyria RT & Music AI Sandbox: https://deepmind.google/technologies/lyria/
SynthID & Deepfake Watermarking: https://deepmind.google/technologies/synthid/
Gemma Models: https://ai.google.dev/gemma
Gemini Live API Docs: https://ai.google.dev/gemini-api/docs/live
Google AI Studio: https://ai.google.dev

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