Artificial intelligence is guesswork — software that can look at a confusing situation, venture a guess about what’s going on and learn from what happens when it acts. AI is at once the stuff of sci-fi movies and dystopian, end-of-humanity nightmares, and now, of mind-numbingly dull white-collar work. After decades of premature promises, artificial intelligence is finding its way into businesses from hedge funds to law firms to beer makers, as the line between ordinary software and AI software has blurred and cloud computing makes AI available to small companies as well as large. This revolution has come accompanied by massive investment and increasing anxiety about the future of jobs. AI could usher in an era of unprecedented prosperity or unprecedented inequality. Bill Gates, Stephen Hawking and Elon Musk, among others, have a deeper fear: That we may be, in Musk’s words, “summoning the demon.”
Venture capital funding to AI-related startups topped $4.8 billion in 2016, from just $559 million in 2012, according to a report by CB Insights. U.S. employers are expected to spend $650 million to lure AI talent, said hiring data firm Paysa. Microsoft Corp., reorganized some of its businesses to create a 5,000-person strong AI group. Musk is both warning about the dangers of unchecked AI and investing in Open AI, a non-profit research group, while his Tesla Inc. works on autonomous vehicles. With the popularity of Amazon.com Inc.’s Alexa, chatbots have taken off with a variety of tech companies from Facebook Inc. to Microsoft taking up the idea. Here are things AI systems can do that would have been considered overly ambitious or unaffordable five years ago:
- Beat a top-ranked human player at Go, a board game with many more possible moves than chess.
- Translate crudely but coherently from one language to another rapidly enough to have a conversation.
- Achieve parity with humans in speech recognition.
- Learn how to drive a car with training from the video game Grand Theft Auto.
- Develop small-scale financial trading systems that mimic the human brain.
In the 1950s, academics flush with the rapid early success of computers turned their thoughts to teaching machines to think. Progress came easily at first, with the invention of neural networks — software that can process data with some of the pattern-recognition capabilities of our own brains. After that came a more refined program called a perceptron, which its creator claimed would soon create a talking, walking and thinking machine. This bout of over-promising was succeeded by the first of several “AI Winters” as researchers hit a wall and funding dried up. Then in the last decade a new class of industrial research labs took root in companies such as Google, Microsoft and Facebook. With vast concentrations of user data and computing power, deep pockets for hiring cadres of AI scientists and an unusually open attitude toward publishing research, these companies started breaking records in speech recognition and image analysis. While the field has been dominated by U.S. companies, Baidu, China’s most popular search engine, has also joined its top ranks.
Artificial intelligence can help scientists solve the world’s “hard problems,” like climate change, says Alphabet Inc.’s chairman, Eric Schmidt. But what if we created a super-smart, autonomous artificial intelligence that ran a paperclip factory and, due to some poor programming or a cyberattack, tried to turn everything it could grab into paperclips? It’s a fanciful scenario proposed to crystallize concerns, but already technology exists that has the potential to manipulate markets or sway elections. In February 2017, a group of leading AI and computer science experts met to consider six doomsday scenarios and discuss how they would combat them. Less far-fetched is the question of whether AI will kill good middle-class jobs. Many economists argue that technological change has so far led to the creation of new and better jobs. But even AI proponents acknowledge that its rapid development could make its growth harder for society to digest. A future in which trucks drive themselves, mammograms are read by computers and the crowds at sporting events are scanned for suspected terrorists can sound great or terrifying depending on whether you’re a truckdriver, radiologist or somebody concerned about privacy. Another worry is AI’s possible effect on inequality. Anything that reduces labor costs is likely to disproportionately benefit holders of capital. If the race to develop artificial intelligence depends on huge amounts of data and computing power, a big chunk of the future economy could be controlled by a handful of companies.
The Reference Shelf
- An overview of artificial intelligence by three prominent researchers, Yann LeCun of Facebook, Yoshua Bengio of the University of Montreal and Geoffrey Hinton of Google and the University of Toronto.
- In 2006, for the 50th anniversary of the coining of the term “artificial intelligence,” AI Magazine published this “Brief History.”
- The Allen Institute for Artificial Intelligence, founded by Microsoft co-founder Paul Allen, argues that fears of AI’s effects are exaggerated; the Future of Life Institute explores them.
- A Bloomberg Businessweek column on the limits of AI.
- The 1943 paper that laid the groundwork for neural networks, by Warren McCulloch and Walter Pitts.
First published July 9, 2015
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