Technology Readiness Scale

The NYTimes has an interesting article about private industry buying high-tech expertise en masse from universities. Thus Uber recently decided to invest in robotics research, figuring that in the long run it's business will require a fleet of self-driving cars. So, what did they do? Natch, they highered 40 researchers from a robotics institute at Carnegie Mellon. Ouch!
But here's what first caught my eye:

There’s a useful high-tech concept called the Technology Readiness Level that helps explain why Uber pounced when it did. NASA came up with this scale to gauge the maturity of a given field of applied science. At Level 1, an area of scientific inquiry is so new that nobody understands its basic principles. At Level 9, the related technology is so mature it’s ready to be used in commercial products. ‘‘Basically, 1 is like Newton figuring out the laws of gravity, and 9 is you’ve been launching rockets into space, constantly and reliably,’’ says Jeff Legault, the director of strategic business development at the National Robotics Engineering Center.
Today’s early-stage inquiry — so-called basic research, the Level 1 work, where scientists are still puzzling over fundamental questions — is financed almost exclusively by the federal government. It’s too far out, too speculative, to attract much investment; it isn’t clear if anyone will make any money on it. This wasn’t always the case. Decades ago, corporations were more willing to engage in Level 1, moonshot research. Bell Labs supported the work that led to the transistor when it was far from clear that there would be a market for it; Xerox supported research into the ‘‘windows’’ style of computing years before the market existed for such an interface. But in the last few decades, the vista of corporate R.& D. has shrunk as markets and executives have focused more on short-term profit, says Marc Kastner, an M.I.T. physicist. The far-off research questions have been left to university labs, though they struggle, too: The percentage of the federal budget devoted to basic research is about half of what it was in 1968.

Still in the concept stage, robot hitching a ride. 

These days, private industry gets involved mostly when a field of research has matured to the midpoint of the NASA scale. In the ’90s and early ’00s this happened to ‘‘machine learning,’’ the science of getting machines to recognize patterns. It had long been an academic concern. But once online firms like Google began grappling with ‘‘big data’’ — search-engine requests, social-network behavior, email — the field became suddenly lucrative, and Silicon Valley started frantically hiring experts away from Stanford.

Here's how that scale worked for Carnegie-Mellon's robots efforts:

In 1979, the university founded its Robotics Institute to tackle the basic problems in the field, like how to interpret sensor data so a robot could ‘‘see.’’ But by the ’80s, government agencies and private firms struggling to create industrial and military robots were asking Carnegie Mellon’s roboticists for help. To capitalize on this demand, the school established its National Robotics Engineering Center in 1995 and staffed it with a few faculty members and a large complement of full-time engineers, often young robotics graduates.
In effect, Carnegie Mellon used the NASA scale to carve up its robotics research. The Robotics Institute would handle research from Levels 1 to 3 or 4, while the center would take technology from there and move it to 7. If John Deere approached the center for help with a self-driving tractor, for example, the center would produce a prototype that could be mass-produced while publishing its research publicly.

Uber raided the Engineering Center, with all that level 4 to 7 tech. Urber will, presumably, drive it up to 9 and cash in.