{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Crazy Wisdom","title":"Episode #395: How to Teach an AI to Think: A Conversation About Knowledge and Intelligence","html":"","width":"100%","height":180,"duration":3663,"description":"In this episode of Crazy Wisdom, Stewart Alsop chats with Ian Mason, who works on architecture and delivery of AI and ML solutions, including LLMs and retrieval-augmented generation (RAG). They explore topics like the evolution of knowledge graphs, how AI models like BERT and newer foundational models function, and the challenges of integrating deterministic systems with language models. Ian explains his process of creating solutions for clients, particularly using RAG and LLMs to support automated tasks, and discusses the future potential of AI, contrasting the hype with practical use cases. You can find more about Ian on his LinkedIn profile.Check out this GPT we trained on the conversation!Timestamps00:00 Introduction and Guest Welcome00:32 Understanding Knowledge Graphs02:03 Hybrid Systems and AI Models03:39 Philosophical Insights on AI05:01 RAG and Knowledge Graph Integration07:11 Challenges in AI and Knowledge Graphs11:40 Multimodal AI and Future Prospects13:44 Artificial Intelligence vs. Artificial Linear Algebra17:50 Silicon Valley and AI Hype30:44 Defining AGI and Embodied Intelligence32:29 Potential Risks and Mistakes of AI Agents35:04 The Role of Human Oversight in AI38:00 Understanding Vector Databases43:28 Building Solutions with Modern Tools46:52 The Future of Solution Development47:43 Personal Journey into Coding57:25 The Importance of Practical Learning59:44 Conclusion and Contact InformationKey InsightsThe evolution of AI models: Ian Mason discusses how foundational models like BERT have been overtaken by newer, more capable language models, which can perform tasks that once required multiple models. He highlights that while earlier models like BERT still have their uses, foundational models have simplified and expanded AI’s capabilities.The role of knowledge graphs: Knowledge graphs provide structured, deterministic ways of handling data, which can complement language models. Ian explains that while LLMs are great for articulating responses...","thumbnail_url":"https://img.transistorcdn.com/UZbrDrlO5VTfDNcq188THwbv0T09vcmLyzx3BcPI9bs/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS81Y2Rj/OGFiMTYyMGFkNTM5/N2NjOWI2MWM5YzQ1/YTc2Ny5qcGc.webp","thumbnail_width":300,"thumbnail_height":300}