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?
Rohan Kodialam | Co-founder & CEO | Sphinx
