Some Scientists Have ‘Magic Hands’ in the Lab. This A.I. Is Learning Why.
On a recent afternoon, scientists buzzed around a lab in Cambridge, Mass., performing experiments. Their equipment was standard: a lab hood for working with dangerous chemicals, an incubator for growing cells. Their procedures were identical to those in biology labs everywhere.
A closer look revealed some oddities, though. Along with lab coats and gloves, most of the scientists wore miniature cameras on headbands. Three additional cameras peered down at each work station from a shelf.
Lab notebooks were strangely absent. Instead, the scientists quietly narrated their work, murmuring into microphones. A team huddled at one end of the lab, inspecting videos of the experiments.
This was the real research taking place in this lab. With a system of sensors and software intended to capture science as it happens, down to the millisecond, scientists were training artificial intelligence models to recognize every object the scientists used and every action they performed.
The A.I. even composed its own narrative, describing every few seconds of video with sentences like, “The operator resuspends the pellet by pipetting it up and down 10 times.”
The technology is the creation of a start-up called Transfyr, which came out of stealth mode this week with $25 million in seed funding. The company is tackling an age-old challenge in science: the hidden factors that make some experiments succeed and others fail.
Failure can take many forms. Researchers may spend months preparing a line of engineered cells, only to have them mysteriously die along the way. A government Covid test seems to work in one lab, but fails to detect the virus when others use it.
A biotech company creates a promising new drug; when it hands off the protocol for large-scale production, suddenly it just doesn’t work.
“This is just an incredibly painful problem,” said Anna Marie Wagner, a co-founder of Transfyr. “There’s finger-pointing back and forth. Was your protocol wrong? Or did you screw something up? These are very, very expensive mistakes in terms of time, money, and lives.”