Recent content by hannahthekoala

A useful way to understand Figure AI is to separate the goal, the input, the model or tool, and the way the output is evaluated. One important area is automation. Define what a good result looks like before changing tools or settings, then test one variable at a time so you can tell what...

A useful way to look at A practical overview of Learning AI for data quality is to focus first on learning and data. That tends to make the trade-offs easier to compare. That way you can improve the workflow without losing track of what caused the improvement.

For AI CPUs & NPUs, it helps to think in terms of inputs, constraints, verification, and iteration. One important area is learning curve. Define what a good result looks like before changing tools or settings, then test one variable at a time so you can tell what actually improved the outcome...