UCB Daniel Susskind on A Worl

Participation Writing Assignment 1

Due: Monday May 10th by 11am

Submit a word document to the “Assignment” link on your MyCC.

In approximately 120 words, write a topic sentence (author’s name, title of work, description of its topic and the author’s argument), followed by a statement, evidence and explanation that deals with at least one quote from the below article.

By Daniel Susskind

Fearing that a new technology would put them out of work, neighbors broke into the house of James Hargreaves, the inventor of the spinning jenny, and destroyed the machine and also his furniture in 18th-century England. Queen Elizabeth I denied an English priest a patent for an invention that knitted wool, arguing that it would turn her subjects into unemployed beggars. A city council dictated that Anton Möller, who invented the ribbon loom in the 16th century, should be strangled for his efforts.

Yet centuries of predictions that machines would put humans out of work for good — a scenario that economists call “technological unemployment” — have always turned out to be wrong. Technology eliminated some jobs, but new work arose, and it was often less grueling or dangerous than the old. Machines may have replaced weavers, but yesterday’s would-be weavers are now working jobs that previous generations couldn’t have imagined, as marketing managers and computer programmers and fashion designers. Over the past few centuries, technology has helped human workers become more productive than ever, bringing economic prosperity and raising living standards. The American economy, for instance, grew 15,241-fold between 1700 and 2000.

But if humans’ fears that technology would replace them have been unfounded in the past, this time is different. Machines are now getting so smart that they’ll soon replace humans at a growing list of jobs, potentially including doctors, bricklayers and insurance adjusters, as well as drivers and retail workers.

What’s different this time around is a new type of artificial intelligence that challenges the assumption that humans will always be better than machines at some jobs. In the past, humans programmed robots to mimic human behavior, and so robots could most easily do repeatable tasks that were easily explained. That’s meant automation has mostly impacted middle-skill jobs, while unpredictable ones, like building houses or diagnosing diseases, have been relatively unaffected. However, now people working at the frontiers of artificial intelligence are teaching machines to draw on vast amounts of processing power and data to solve problems in ways humans couldn’t. Thus an IBM system beat Garry Kasparov in chess not by copying his strategy, but by drawing on a database of 330 million moves in a second, and picking which ones had the highest likelihood of beating him. Future machines like this one will, for example, be able to diagnose diseases better than human doctors, or evaluate insurance customers with eye scans to determine whether they are lying (a method that is more efficient and accurate when measured against a human salesperson). In other words, computers are coming for all jobs: from agriculture to medicine, or legal counsel to transport, no one will be able to outperform machines.

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