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/

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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

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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

In this episode of the ODSC AiX Podcast, host Sheamus McGovern sits down with Kwindla Hultman Kramer, co-founder of Daily and creator of the open-source project PipeCat. Kwindla is a pioneer in real-time audio, video, and human-computer interaction. He shares his journey from MIT Media Lab to building voice interfaces that are now shaping the future of software interaction.
Together, they explore why voice is poised to become the primary interface for AI, what makes voice agents so challenging to build, and how real-world use cases—from call centers to appointment scheduling—are proving the value of well-designed voice AI systems.
Key Topics Covered:
Why voice is at a “2007 iPhone moment” for user interfaces
What makes a great voice AI experience (and why Siri/Alexa missed the mark)
The rise of voice agents in production use: healthcare, banking, customer service, and more
Real-world engineering challenges in building voice-first AI agents
How latency, streaming, turn detection, and interruptibility define user experience
The architecture and motivation behind the PipeCat open-source toolkit
Why LLMs need context engineering in voice workflows
Differences between text-first vs. voice-first agent design
Hybrid models: routing between cloud and on-device inference
What’s missing in voice AI today: memory, context, and bi-directional streaming
Synthetic data and the future of training audio models
Advice for developers just starting with voice AI
Memorable Outtakes:
“Voice is at the tipping point—like mobile was in 2007.”
“All UI is going to become liquid, generated on the fly by natural voice speech.”
“You’ve probably already spoken to an AI agent—you just didn’t realize it.”
References & Resources:
PipeCat GitHub: https://github.com/pipecat-ai/pipecat
Kwindla Hultman Kramer: https://www.linkedin.com/in/kwkramer/
Daily (video/audio infrastructure company): https://www.daily.co
Voice AI and Voice Agents Guide: https://voiceaiandvoiceagents.com
Smart Turn Detection Model: https://github.com/pipecat-ai/smart-turn
Gemini 1.5 and Google Live API: https://blog.google/products/gemini/gemini-live-updates-august-2025/
NotebookLM by Google: https://notebooklm.google/
Suno AI (music generation): https://www.suno.ai/
Whisper (OpenAI ASR Model): https://github.com/openai/whisper
Minority_Report: https://en.wikipedia.org/wiki/Minority_Report_(film)
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Voice AI is About to Get Loud with Kwindla Kramer

In this episode of the ODSC AI Podcast, we sit down with Paco Nathan, veteran AI practitioner, data science leader, and currently with Senzing. Paco shares how AI, entity resolution, and graph analytics are being deployed to uncover massive financial crime networks, from shell companies and money laundering to cybercrime and trafficking. Drawing from real-world cases like the Azerbaijani Laundromat and Danske Bank scandal, Paco explains how cutting-edge AI techniques, RAG, and open data are transforming the fight against hidden financial networks while also powering legitimate business intelligence.

Key Topics Covered:
Paco Nathan’s career journey: AI, neural networks, Databricks, and now Senzing
Senzing’s origins in casinos, card counting detection, and “non-obvious relationship awareness”
Industrial-scale money laundering: Azerbaijani Laundromat, Danske Bank Estonia branch, and $200B flows exceeding Estonia’s GDP
Entity resolution as the backbone of fraud detection and why accuracy is critical in high-stakes domains
How graph analytics expose hidden structures in money laundering networks
DSPy, BAML, and MLflow: AI tooling for programmatic prompt engineering and evaluation
The rise of Graph-RAG: combining retrieval-augmented generation with knowledge graphs
Forensic accounting, fraud tradecraft patterns, and synthetic data simulators
Open-source and open-data initiatives like OpenSanctions, OpenOwnership, and GLEIF
Opportunities for data professionals to engage in combating financial crime and supporting transparency

Memorable Outtakes:
Paco Nathan: “Defenders think in lists. Attackers think in graphs — and as long as that holds, attackers win.”
Paco Nathan: “Sometimes your best customers and your worst criminals look much the same.”
Paco Nathan: “One Estonian bank branch moved more money than the GDP of its host country.”

References & Resources:
Paco Nathan’s LinkedIn: https://www.linkedin.com/in/ceteri/
Senzing: https://senzing.com/
DSPy: https://github.com/stanfordnlp/dspy
BAML: https://github.com/BoundaryML/baml
MLflow: https://mlflow.org/
OpenSanctions: https://www.opensanctions.org/
OpenOwnership: https://www.openownership.org/
GLEIF (Global Legal Entity Identifier Foundation): https://www.gleif.org/en

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Follow the (Dark) Money: AI and the Hunt for Hidden Networks with Paco Nathan

In this episode of the ODSC Ai X Podcast, host Alex Landa speaks with Georgios N. Yannakakis, Professor at the Institute of Digital Games, University of Malta, co-founder of Modl.ai and Human Feedback AI, and Editor-in-Chief of IEEE Transactions on Games. A leading researcher in AI and games, Georgios shares insights from two decades of work at the intersection of artificial intelligence, player experience, and creative design. The discussion spans his seminal paper Choose Your Weapons: Survival Strategies for Depressed AI Academics, the value of human feedback in AI development, and the transformative role of AI in game research and design.

Key Topics Covered:
Georgios’s background in AI and game research, including co-founding Modl.ai and Human Feedback AI
The challenges facing AI academics today, including scaling maximalism and the need for smaller, human-centered models
Insights from the satirical yet practical paper Choose Your Weapons: Survival Strategies for Depressed AI Academics
The underrated importance of human data and human-in-the-loop approaches for AI alignment
Applications of AI in gaming, from procedural content generation to adaptive player modeling
How reinforcement learning and emotional modeling can create personalized, player-centric game experiences
The shift from building superhuman AI agents toward co-creative systems that assist game designers
Strategies like “try things that shouldn’t work” and how disruptive, risky research can lead to breakthroughs
Current projects in affective computing, user modeling, and generative AI for co-creation in games

Memorable Outtakes:
“As recently as 10 years ago, if you had a decent desktop computer and an internet connection, you had everything you needed to compete with the best researchers. Well, guess what? It’s not the case anymore.” – Georgios N. Yannakakis
“Humans are the next frontier of AI. We need human-centered models to build safer, explainable, and more trustworthy AI.” – Georgios N. Yannakakis
“The best use of generative AI in games is as a creative partner—an assistant in the design process—not as a fully autonomous game designer.” – Georgios N. Yannakakis

References & Resources:
Georgios Yannakakis University of Malta profile: https://www.um.edu.mt/profile/georgiosyannakakis
Georgios’ LinkedIn: https://www.linkedin.com/in/georgios-n-yannakakis-67a5b05
Modl.ai: https://modl.ai/
Human Feedback AI: https://humanfeedback.ai/
IEEE Transactions on Games: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7782673
Book Artificial Intelligence and Games: http://gameaibook.org/
Paper: Choose Your Weapons: Survival Strategies for Depressed AI Academics: https://ieeexplore.ieee.org/document/10458714
Paper: Pixels and Sound of Emotion: General Purpose Representations of Arousal in Games (IEEE Transactions on Affective Computing): https://arxiv.org/abs/2101.10706
Paper: BehAVE: Behaviour Alignment of Video Game Encodings: https://arxiv.org/html/2402.01335v3
Podcast with his colleague, Julian Togelius - From Turing’s Chess to Neural Game Engines: AI in Video Games Today: https://youtu.be/f_z2PpR3GaU

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Gaming Research in AI and the Need for Human-Centered Design with Georgios Yannakakis

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.

Inside Google’s New AI Stack with Paige Bailey

ODSC's Ai X Podcast