Jay Alammar on LLMs, RAG, and AI Engineering
Jay Alammar, renowned AI educator and researcher at Cohere, discusses the latest developments in large language models (LLMs) and their applications in industry. Jay shares his expertise on retrieval augmented generation (RAG), semantic search, and the future of AI architectures. MLST is sponsored by Brave: The Brave Search API covers over 20 billion webpages, built from scratch without Big Tech biases or the recent extortionate price hikes on search API access. Perfect for AI model training and retrieval augmentated generation. Try it now - get 2,000 free queries monthly at http://brave.com/api. Cohere Command R model series: https://cohere.com/command Jay Alamaar: https://x.com/jayalammar Buy Jay's new book here! Hands-On Large Language Models: Language Understanding and Generation https://amzn.to/4fzOUgh TOC: 00:00:00 Introduction to Jay Alammar and AI Education 00:01:47 Cohere's Approach to RAG and AI Re-ranking 00:07:15 Implementing AI in Enterprise: Challenges and Solutions 00:09:26 Jay's Role at Cohere and the Importance of Learning in Public 00:15:16 The Evolution of AI in Industry: From Deep Learning to LLMs 00:26:12 Expert Advice for Newcomers in Machine Learning 00:32:39 The Power of Semantic Search and Embeddings in AI Systems 00:37:59 Jay Alammar's Journey as an AI Educator and Visualizer 00:43:36 Visual Learning in AI: Making Complex Concepts Accessible 00:47:38 Strategies for Keeping Up with Rapid AI Advancements 00:49:12 The Future of Transformer Models and AI Architectures 00:51:40 Evolution of the Transformer: From 2017 to Present 00:54:19 Preview of Jay's Upcoming Book on Large Language Models Disclaimer: This is the fourth video from our Cohere partnership. We were not told what to say in the interview, and didn't edit anything out from the interview. Note also that this combines several previously unpublished interviews from Jay into one, the earlier one at Tim's house was shot in Aug 2023, and the more recent one in Toronto in May 2024. Refs: The Illustrated Transformer https://jalammar.github.io/illustrated-transformer/ Attention Is All You Need https://arxiv.org/abs/1706.03762 The Unreasonable Effectiveness of Recurrent Neural Networks http://karpathy.github.io/2015/05/21/rnn-effectiveness/ Neural Networks in 11 Lines of Code https://iamtrask.github.io/2015/07/12/basic-python-network/ Understanding LSTM Networks (Chris Olah's blog post) http://colah.github.io/posts/2015-08-Understanding-LSTMs/ Luis Serrano's YouTube Channel https://www.youtube.com/channel/UCgBncpylJ1kiVaPyP-PZauQ Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks https://arxiv.org/abs/1908.10084 GPT (Generative Pre-trained Transformer) models https://jalammar.github.io/illustrated-gpt2/ https://openai.com/research/gpt-4 BERT (Bidirectional Encoder Representations from Transformers) https://jalammar.github.io/illustrated-bert/ https://arxiv.org/abs/1810.04805 RoPE (Rotary Positional Encoding) https://arxiv.org/abs/2104.09864 (Linked paper discussing rotary embeddings) Grouped Query Attention https://arxiv.org/pdf/2305.13245 RLHF (Reinforcement Learning from Human Feedback) https://openai.com/research/learning-from-human-preferences https://arxiv.org/abs/1706.03741 DPO (Direct Preference Optimization) https://arxiv.org/abs/2305.18290