2018年上海（春考） Passage C

“Is data the new oil? asked proponents of big data back in 2012 in Forbes magazine. By 2016 , and the rise of big data's turbo-powered cousin deep learning, we had become more certain: “Data is the new oil,” stated Fortune.

Amazon’s Neil Lawrence has a slightly different analogy: Data, he says, is coal. Not coal today, though, but coal in the early days of the 18th century, when Thomas Newcomen invented the steam engine. A Devonian ironmonger, Newcomen built his device to pump water out of the south west’s prolific tin mines.

The Problem, as Lawrence told the Re-Work conference on Deep Learning in London. was that the pump was rather more useful to those who had a lot of coal than those who didn’t: it was good, but not good enough to buy coal in to run it. That was so true that the first of Newcomen’s steam engines wasn’t built in a tin mine, but in coal works near Dudley.

So why is data coal? The problem is similar: there are a lot of Newcomens in the world of deep learning. Startups like London’s Magic Pony and SwiftKey are coming up with revolutionary new ways to train machines to do impressive feats of cognition, from reconstructing facial data from grainy images to learning the writing style of an individual user to better predict which word they are going to type in a sentence.

And yet, like Newcomen, their innovations are so much more useful to the people who actually have copious(丰富的) amounts of raw material to work from. And so Magic Pony is acquired by Twitter, SwiftKey is acquired by Microsoft and Lawrence himself gets hired by Amazon from the University of Sheffield, where he was based until three weeks ago.

But there is a coda to the story：69 years later, James Watt made a nice tweak to the Newcomen steam engine, adding a condenser to the design. That change, Lawrence said, “made the steam engine much more efficient, and that’s what trigger the industrial revolution.

Whether data is oil or coal, then, there’s another way the analogy holds up. a lot of work is going into trying to make sure we can do more, with less. It’s not as impressive as teaching a computer to play Go or Pac-Man better than any human alive, but “data efficiency” is a crucial step if deep learning is going to move away from simply gobbling up oodles of data and spitting out the best correlations possible.

“If you look at all the areas where deep learning is successful, they’re all areas where there’s lots of data,” points out Lawrence. That’s great if you want to categorize images of cats, but less helpful if you want to use deep learning to diagnose rare illnesses. ‘‘It’s generally considered unethical to force people to become sick in order to acquire data.