An in-depth look at Gemma 4's novel transformer architecture with per-layer embeddings, enabling efficient parameter offloading for on-device inference. The discussion also covers its native multimodality, the state of fine-tuning, text-based diffusion models, and the growing intersection of research and engineering.
Predictive AI forecasts what will happen next based on historical data, while Generative AI creates new content by asking what something could look like. This summary explores their fundamental differences in outputs, data types, underlying models like transformers and diffusion systems, and how they can be used together in enterprise applications.
Adrian Kosowski from Pathway introduces the Baby Dragon Hatchling (BDH), a groundbreaking, post-transformer architecture inspired by neuroscience. BDH leverages sparse, positive activation to mimic brain function, offering a path to limitless context, superior reasoning, and unprecedented computational efficiency, potentially solving key limitations of current large language models.
A deep dive into the internal workings of Large Language Models like GPT, explaining the journey from a text prompt through tokenization, embeddings, and the attention mechanism to generate a response.