Capabilities of LLMs 🤯 (Practical AI #219)

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.

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Featuring:

• Rajiv Shah – Website, GitHub, LinkedIn, X
• Chris Benson – Website, GitHub, LinkedIn, X
• Daniel Whitenack – Website, GitHub, X

Show Notes:

• Solving AI Tasks with ChatGPT and its Friends in HuggingFace | GitHub
• Generative Agents: Interactive Simulacra of Human Behavior
• Wolfram ChatGPT
• Comparing LLMs
• LangChain
• Learn about LLMs:
  • Emergence and reasoning in large language models (Jason Wei)
  • Sparks of Artificial General Intelligence
• Learning Prompting
• Getting Started with Transformers:
  • Transformers course (free)
  • Tasks at Hugging Face
• Training your own LLM Models:
  • Efficient Large Language Model training with LoRA and Hugging Face
  • PEFT (Parameter-Efficient Fine-Tuning)
• Dolly blog post
• Illustrating Reinforcement Learning from Human Feedback

Something missing or broken? PRs welcome!