How did a machine trained to predict the next token become capable of writing software, solving mathematics, analyzing images, using tools, and carrying out complex reasoning?

In Episode 2 of Zero to Singularity, we go inside the machinery of modern large language models and build an intuitive understanding of how today’s most advanced AI systems actually work.

We explore tokens and embeddings, transformers and self-attention, pretraining and post-training, gradient descent, reinforcement learning, context windows, memory, mixture-of-experts, multimodality, inference-time compute, and modern reasoning models.

We also tackle some of the biggest questions in artificial intelligence:

• What is an LLM actually doing when it generates an answer? • Why is self-attention so powerful? • Where is a model’s knowledg ...