{"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.\nCheck out this GPT we trained on the conversation!\n\nTimestamps\n\n00:00 Introduction and Guest Welcome\n00:32 Understanding Knowledge Graphs\n02:03 Hybrid Systems and AI Models\n03:39 Philosophical Insights on AI\n05:01 RAG and Knowledge Graph Integration\n07:11 Challenges in AI and Knowledge Graphs\n11:40 Multimodal AI and Future Prospects\n13:44 Artificial Intelligence vs. Artificial Linear Algebra\n17:50 Silicon Valley and AI Hype\n30:44 Defining AGI and Embodied Intelligence\n32:29 Potential Risks and Mistakes of AI Agents\n35:04 The Role of Human Oversight in AI\n38:00 Understanding Vector Databases\n43:28 Building Solutions with Modern Tools\n46:52 The Future of Solution Development\n47:43 Personal Journey into Coding\n57:25 The Importance of Practical Learning\n59:44 Conclusion and Contact Information\n\nKey Insights\nThe 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.\nThe 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...","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}