Jay Alammar

In this dynamic discussion, Jonathan Frankle, Chief Scientist at MosaicML, shares insights on their $1.3 billion acquisition by Databricks. Lin Qiao, CEO of Fireworks AI, talks about optimizing PyTorch for inference. Aman Sanger from Cursor reveals innovative memory strategies for AI coding. Aravind Srinivas discusses the impressive growth of Perplexity AI, hitting 1 million installs, while Jeremy Howard emphasizes the need for accessible AI. Together, they explore the vibrant AI startup landscape showcased at NeurIPS 2023, reflecting on innovation, collaboration, and the future of technology.

Jay Alammar, a prominent AI educator and researcher at Cohere, dives into the latest on large language models (LLMs) and retrieval augmented generation (RAG). He explores how RAG enhances data interactions, helping reduce hallucination in AI outputs. Jay also addresses the challenges of implementing AI in enterprises, emphasizing the importance of education for developers. The conversation highlights semantic search innovations and the future of AI architectures, offering insights on effective deployment strategies and the need for continuous learning in this rapidly evolving field.

Jay Alammar, a prominent AI educator and applied NLP practitioner at co:here, shares insights on effective NLP solutions and the role of public writing in mastering machine learning. He discusses the balance between the reliability of advanced NLP models and their accessible integration into real-world applications. The conversation also covers the exciting advancements in multimodal models that merge text and image generation. Alammar emphasizes the importance of educational resources to foster innovation in AI.

My interview with Jay Alammar, widely known inthe AI and NLP field mainly through his great blog on transformers and attention. ►Watch on YouTube: https://youtu.be/TO0IV9e2MMQ ►LLM University: https://docs.cohere.com/docs/llmu ►Jay's blog: http://jalammar.github.io/illustrated-transformer/ ►Twitter: https://twitter.com/JayAlammar, https://twitter.com/Whats_AI ►My Newsletter (A new AI application explained weekly to your emails!): https://www.louisbouchard.ai/newsletter/ ►Support me on Patreon: https://www.patreon.com/whatsai ►Join Our AI Discord: https://discord.gg/learnaitogether How to start in AI/ML - A Complete Guide: ►https://www.louisbouchard.ai/learnai/ Become a member of the YouTube community, support my work and get a cool Discord role : https://www.youtube.com/channel/UCUzGQrN-lyyc0BWTYoJM_Sg/join Chapters: 0:00 Hey! Tap the Thumbs Up button and Subscribe. You'll learn a lot of cool stuff, I promise. 00:43 Introduction of Jay Alammar 04:00 Why Jay got into AI 8 years ago? 08:07 Why teach after learning 16:12 What is a Transformer? 21:03 What the blocks are made of and how they work? 26:27 Training steps of LLM explained in simple words 39:31 Re-rank Systems 41:47 How to know that your problem can be solved by LLM? 45:31 Chatbot on private or proprietary data 47:10 Challenges with AI Apps 50:51 The requirement to create AI apps 56:11 Mitigate model hallucination from your side 59:05 How does ChatGPT work with any language you type in? 01:02:56 AI evolution in next few years 01:05:38 Jay wants AI to be able to do this 01:08:10 AI apps used by Jay 01:10:10 Projects of Jay

Jay Alammar, Director and Engineering Fellow at Cohere and co-author of "Hands-on Large Language Models," delves into enterprise AI. He discusses the challenges in understanding large language models and the adoption of GraphRag, emphasizing the gap between vendor enthusiasm and real-world application. Alammar highlights the balance between self-directed and collaborative learning in AI, and the critical role of evaluation processes for effective AI development. He also explores the potential of smaller AI models, showcasing their efficiency in addressing specific tasks.