Large Language Model (LLM) capabilities have reached new heights and are nothing short of mind-blowing! However, with so many advancements happening at once, it can be overwhelming to keep up with all the latest developments. To help us navigate through this complex terrain, we’ve invited Raj - one of the most adept at explaining State-of-the-Art (SOTA) AI in practical terms - to join us on the podcast.Raj discusses several intriguing topics such as in-context learning, reasoning, LLM options, and related tooling. But that’s not all! We also hear from Raj about the rapidly growing data science and AI community on TikTok.Sponsors:Fastly – Our bandwidth partner. Fastly powers fast, secure, and scalable digital experiences. Move beyond your content delivery network to their powerful edge cloud platform. Learn more at fastly.comFly.io – The home of Changelog.com — Deploy your apps and databases close to your users. In minutes you can run your Ruby, Go, Node, Deno, Python, or Elixir app (and databases!) all over the world. No ops required. Learn more at fly.io/changelog and check out the speedrun in their docs. Featuring:Rajiv Shah – Website, GitHub, LinkedIn, XChris Benson – Website, GitHub, LinkedIn, XDaniel Whitenack – Website, GitHub, XShow Notes:Solving AI Tasks with ChatGPT and its Friends in HuggingFace | GitHubGenerative Agents: Interactive Simulacra of Human BehaviorWolfram ChatGPTComparing LLMsLangChainLearn about LLMs: Emergence and reasoning in large language models (Jason Wei)Sparks of Artificial General IntelligenceLearning PromptingGetting Started with Transformers: Transformers course (free)Tasks at Hugging FaceTraining your own LLM Models: Efficient Large Language Model training with LoRA and Hugging FacePEFT (Parameter-Efficient Fine-Tuning)Dolly blog postIllustrating Reinforcement Learning from Human FeedbackUpcoming Events: Register for upcoming webinars here!