Software engineering has seen a massive lift from AI augmentation over the last 3 years. However, data teams have been left behind. The fundamental loop of software engineering differs significantly from that of data science, making distinct agents necessary.
- Why does AI struggle with quantitative data, and where are there divergences from human experts?
- What data representations are most meaningful for today's frontier AI models?
- How can better data representation let AI become a partner in data science?
- What are the best practices for deploying AI to teams to maximize acceleration without compromising quality or security?
