This session of the MIT Deep Learning series explores the mechanics behind LLMs, framing them as advanced autoregressive systems for next-token prediction. The technical overview covers the evolution from basic statistical methods to trillion-parameter architectures with massive context windows.

Key takeaways include:

• The transition from Bayesian counting to modern self-supervised learning.
• How prompt engineering techniques like "Chain of Thought" unlock emergent reasoning.
• Architectural innovations like LoRA for efficient fine-tuning.
• Critical safety frontiers: Jailbreaks, hallucinations, and alignment through RLHF.

The future of AI lies in agents that don't just predict text but plan and execute tasks via external tools.

All my links: https://linktr.ee/learnbydoingwithsteven #learnbydoingwithsteven #MIT #DeepLearning #LLM #AI #MachineLearning #GenerativeAI #PromptEngineering #DataScience #AIAgent