tl;dr:I think of the AI stack as consisting of 4 layers: (1) Infrastructure: Toolings for serving, vector search and database. (2) Model development: Toolings for developing models and anything that involves changing a model’s weights. (3) Application development with readily available models. This is the layer that has seen the most actions in the last 2 years and is still rapidly evolving. (4) Applications: Most popular types of applications are coding, workflow automation, information aggregation.
tl;dr:How exactly does RLHF work? Why does it work?” Chip discusses the answers to these questions. “RL has been notoriously difficult to work with, and therefore, mostly confined to gaming and simulated environments. Just five years ago, both RL and NLP were progressing pretty much orthogonally – different stacks, different techniques, and different experimentation setups. It’s impressive to see it work in a new domain at a massive scale.”
tl;dr:Specific to software engineering resumes, Chip looks for: (1) Demonstrated expertise, not keywords. (2) People who get things done i.e initiative and persistence. (3) Unique perspectives. (4) Impact, not meaningless metrics. Chips discusses each, sharing examples of how they show on a resume.
tl;dr:"With luck you shouldn’t have to build or maintain a streaming system yourself. Your company should have infrastructure to help you with this. However, understanding where streaming is useful and why streaming is hard could help you evaluate the right tools and allocate sufficient resources for your needs."
tl;dr:Chip discusses two approaches: (1) Online predictions, where an ML system makes predictions in real-time. (2) Online learning, where ML system incorporate new data and update models in real-time.
tl;dr:Chip ran an analysis to attempt to answer several questions including "how long does it take for software engineers to reach a certain level?" and "do women get paid less than men in tech?"