Today in AI - May 8, 2025 by The Artificial Intelligence Podcast
Join Ron Green, CTO of KUNGFU.AI, as he discusses the evolution of artificial intelligence from its early underestimated beginnings to today's generative AI boom sparked by ChatGPT. Ron shares insights on common misconceptions about AI implementation, emphasizing the importance of clean first-party data and the need for human oversight when deploying AI solutions. He also highlights how proprietary data is the key to valuable AI investments, while cautioning against rushing implementation without proper safeguards.
AI Implementation Insights
Join Barr Moses, CEO of Monte Carlo, as she discusses the crucial role of data and AI observability in building reliable AI products. She explains how enterprises can gain competitive advantage by leveraging their first-party data and implementing proper data quality monitoring systems. Moses highlights that regardless of industry, organizations face similar challenges with data reliability, which can be traced to four root causes: problems with the data itself, code issues, system failures, and model output errors.
Agentic Systems and AI Tools
Join Diane Gutiw, VP Global AI Research at CGI, as she discusses agentic systems - collaborative ecosystems of specialized AI tools that work together to solve complex problems. She explains how RAG is evolving as one component within broader agentic workflows, addresses challenges in moving AI from POC to production, and emphasizes pragmatic AI governance. Diane also explains digital triplets - AI layers built on existing data infrastructures that enable natural language conversations with information ecosystems across healthcare, utilities, and infrastructure management.
Enterprise AI Adoption Trends
Join Lexi Reese, CEO of Lanai, as she shares insights on enterprise AI adoption, discussing the significant gap between AI interest and implementation where executives prioritize AI yet 90% of organizations remain observers. She explains how companies are evolving from blocking AI to selective enablement through governance committees, but this approach is breaking as AI proliferates across every SaaS application, creating backlogs and inefficiencies. Lexi emphasizes the importance of viewing AI as a teammate rather than just a tool, advocating for organizations to foster experimentation with appropriate guardrails while identifying and scaling successful use cases across departments.
Multimodal Foundation Model
Join Brandon Barbello, COO, and Nick Gillian, Head of AI at Archetype AI, as they discuss Newton, a multimodal foundation model that processes sensor data to power applications in home security, industrial automation, and more. Newton enables resource-constrained teams to build AI solutions effortlessly by leveraging existing sensors and historical data for smarter, adaptive systems. They explore the rise of physical AI, where foundation models integrate diverse sensors to enhance real-world decision-making across industries.
Evolution of Advertising
Join Hikari Senju, CEO of Omneky, as he discusses the evolution of advertising from ancient Roman signs to today's AI-driven personalized content generation. Senju explains how Omneky is revolutionizing the advertising industry by using AI to generate and optimize creative content at scale, while also addressing key challenges around copyright, enterprise adoption, and liability in the emerging AI advertising landscape. Looking toward the future of advertising, Senju emphasizes that while AI is creating abundance in content generation, human attention remains the scarcest and most valuable commodity in the modern marketing landscape.
AI Evolution and Graph RAG
Join Nikolaos Vasiloglou, VP of Research ML at RelationalAI, as he traces the evolution of AI from early neural networks through kernel methods, gradient boosted trees, and the deep learning revolution that transformed the field. He shares valuable insights on addressing hallucinations in AI systems through fact-checking, human annotation, and offline curation, while emphasizing the growing importance of Graph RAG (Retrieval-Augmented Generation) as a practical solution that bridges neural networks' generalization capabilities with symbolic AI's accuracy and speed. He explores agentic systems, both macro and micro approaches, and offers his perspective on recent developments like DeepSeek's R1 model.
