> Eve independently screened some 1,600 chemicals and modelled how their structure related to their activity to predict which ones were worth testing. King and his group armed the robot with background knowledge and a machine-learning framework for developing hypotheses. Eve then used those elements to design experiments to test these hypotheses and, crucially, performed them itself.

> King plans to use the system — which occupies one-fifth of floor space than Eve does — to model how genes, proteins and small molecules interact in cells. Part of that will involve taking around 10,000 mass-spectrometry measurements each day.

The throughput here is astounding, especially when driven by researchers who really know how to chart a path. I feel every time a critical feedback loop is made both faster and cheaper, it makes everyone participating better. I wonder whether we will see many more "whiz kid" scientific researchers than we have today.

> So much advanced equipment is just sitting there in labs, waiting for humans to finally go and make experiments. Which they eventually get round to, sort of, when they can secure funding and when the grad student isn't ill or making mistakes or framing the problem the wrong way.

That's not really what the article is about though. Short of staffing it with humanoid robots, existing labs and their equipment will continue to be unused.

> AI-driven labs can iterate 'good enough' hypotheses way faster than human R&D systems.

Is there evidence of that?