Distilling 200+ Hours of NeurIPS: What’s Next for AI // Nikolaos Vasiloglou // #336

Distilling 200+ Hours of NeurIPS: What’s Next for AI // MLOps Podcast #336 with Nikolaos Vasiloglou, VP of Research ML at RelationalAI.Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletter// AbstractNikolaos' widely shared analysis on LinkedIn highlighted key insights across agentic AI, scaling laws, LLM development, and more. Now, he’s exploring how AI itself might be trained to automate this process in the future, offering a glimpse into how researchers could harness LLMs to synthesize conferences like NeurIPS in real-time.// BioNikolaos Vasiloglou is VP of Research-ML for RelationalAI, the industry's first knowledge graph coprocessor for the data cloud. Nikolaos has over 20 years of experience implementing high-value machine learning and AI solutions across various industries. // Related LinksWebsite: https://relational.ai/~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Nikolaos on LinkedIn: /vasiloglou/Timestamps:[00:00] Nik's preferred coffee[01:05] Distilling NeurIPS insights[06:43] Choosing research papers[16:49] Agent patterns at NeurIPS[21:16] Interest in agent-based innovation[25:54] Time series forecasting models[28:15] Tabular foundation models[36:25] Verifier challenges and complexity[39:36] Knowledge graph[45:00] Knowledge graph data challenges[47:14] Worldview in knowledge graphs[50:30] Self-serve analytics challenges[56:22] Llama model adaptation comparison[56:59] Wrap up