In this episode of ODSC’s Ai X Podcast, we are joined by Francis Govers, a contributor to the design of over 30 manned and unmanned land, sea, air, and space vehicles, and an expert in robotics and autonomy. 
Francis is an Air Force veteran, spent 10 years at NASA, was a lead engineer for the International Space Station, was Deputy Chief Engineer for the US Army Future Combat Systems, participated in the DARPA Grand Challenge, and managed a Zeppelin airship. He worked on the "Yellow Line" for football and designed telemetry systems for NASCAR and IndyCar. He designed RAMSEE, the robot security guard, as CTO of Gamma 2 Robotics. As a commercial pilot, writer, artist, musician, engineer, race car nut, and designer, Francis has a serious addiction to building things that frequently get him into trouble. 
He has published 48 magazine articles and contributed to five books. He received five outstanding achievement awards from NASA, the Explorer Award by the National Space Society, recognition from Scientific American for “World Changing Ideas”, and was recognized by the Vertical Flight Society as a "Titan of Autonomy". 
SHOW TOPICS:
- The second edition of “Artificial Intelligence for Robotics” which came out in February
- Tell us about your background and key career moments
- Tell us about your book - what’s the motivation and audience?
- The growing industry of robotics startups and their appeal to investors
- What is the fundamental difference between an AI-enabled robot and a traditional robot?
- How does autonomy differ from pre-programmed robotic behaviors?
- Why is supervised learning important in AI robotics, and how is it used in this book?
- You show the reader how to build a robot in your book. What are the primary technical components (hardware and software) needed to build an AI robot?
- Tell us about Robotic Operating Systems aka ROS
- How can deep learning be used for robotics and is transfer learning applicable?
- How can we teach a robot to listen and what are the main challenges in developing a robot capable of understanding human speech?
- What is Spectral Analysis?
- Beyond just listening, how do we give a robot a personality, known as artificial personality (AP)?
- What is the purpose of Monte Carlo modeling in creating human-like interactions for the robot?
- How does the robot’s personality system enable it to hold simple conversations with children?
- Why is it important for the robot to understand the mood of its human user during interactions?
- How do robots traditionally navigate unstructured envoirnments and what new methods does AI introduce?
SHOW  NOTES:
Francis Govers: ⁠https://www.linkedin.com/in/francisxgoversiii/⁠ 
Artificial Intelligence for Robotics: ⁠https://www.packtpub.com/en-us/product/artificial-intelligence-for-robotics-9781788835442⁠ 
NASA Space Station: ⁠https://www.nasa.gov/international-space-station/⁠ 
Q-Learning: https://en.wikipedia.org/wiki/Q-learning 
 
This episode was sponsored by:  
Ai+ Training⁠ ⁠https://aiplus.training/⁠⁠ 
And created in partnership with ODSC⁠ ⁠https://odsc.com/ ⁠⁠ 
Join us at our upcoming and highly anticipated conference ODSC West in South San Francisco October 29-31.

AI for Robotics and Autonomy with Francis X. Govers III

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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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/

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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/

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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:
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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/

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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/

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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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Inside Google’s New AI Stack with Paige Bailey

In this episode, Sheamus McGovern sits down with João “Joe” Moura, founder and CEO of CrewAI, to discuss the rapid evolution of multi-agent AI systems and their real-world impact in enterprise environments. From his early experiments with agent-based automations to supporting clients like PwC, PepsiCo, and the U.S. Department of Defense, Joe shares how CrewAI is enabling companies to move beyond hype and flashy demos toward scalable, secure agent workflows. This conversation covers everything from open-source momentum to agent orchestration, governance, deployment challenges, and what it takes to go from prototype to production.
Key Topics Covered:
* João’s background in AI and the early days of CrewAI
* The problem CrewAI was originally built to solve—and how it scaled from a personal tool to a global platform
* Definition of a true agent: “Agency is the key. If there’s no decision-making, it’s not an agent.”
* The evolution to robust multi-agent enterprise systems
* CrewAI’s dual product strategy: open source for developers, enterprise-grade platform for production use
* Use cases driving the most traction: go-to-market workflows, internal GPTs, code automation at scale
* The MIT report on AI adoption: why many enterprises are failing to see ROI—and how CrewAI is addressing the gap
* Empowering non-technical users through CrewAI Studio
* Security, governance, and the importance of deterministic control in agent deployment
* Why waiting for the next-gen LLM is a mistake: “The models we have today are good enough for 99% of enterprise use cases.”
* CrewAI’s take on interoperability, agent-to-agent protocols, and why standards matter
Memorable Outtakes:
* “If you want an agent, you’ve got to have agency. Otherwise, it’s just a script.”
* “LLMs today are good enough for 99% of enterprise use cases.”
*  “We’re now running over 475 million agent automations a month.”
References & Resources:
João “Joe” Moura – LinkedIn https://www.linkedin.com/in/joaomdmoura/CrewAI  https://www.crewai.com/
MIT Report on GenAI adoption (referenced in discussion) https://ide.mit.edu/research/95-of-generative-ai-projects-fail-how-to-make-yours-succeed
Humanity's Last Exam https://agi.safe.ai/
AgentBench⁠ https://github.com/THUDM/AgentBench⁠
HumanEval Benchmark⁠ https://github.com/openai/human-eval⁠
MCP – Model Coordination Protocolhttps://modelcontextprotocol.io/docs/getting-started/intro
Agent to Agent  Communication  https://developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability/
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 podcastfor 10% off any ticket. Learn more:⁠ https://odsc.ai⁠

CrewAI and the Rise of Autonomous Agents in Enterprise AI with João (Joe) Moura

With Ai X Podcast, Open Data Science Conference (ODSC) brings its vast experience in building community and its knowledge of the data science and AI fields to the podcast platform. The interests and challenges of the data science community are wide ranging. To reflect this Ai X Podcast will offer a similarly wide range of content, from one-on-one interviews with leading experts, to career talks, to educational interviews, to profiles of AI Startup Founders. Join us every two weeks to discover what’s going on in the data science community.

Find more ODSC lightning interviews, webinars, live trainings, certifications, bootcamps here - https://aiplus.training/  
Don't miss out on this exciting opportunity to expand your knowledge and stay ahead of the curve.

AI for Robotics and Autonomy with Francis X. Govers III

ODSC's Ai X Podcast