单选题   Para. 1 ①During the summer before the presidential election, John Seymour and Philip Tully, two researchers with ZeroFOX, a security company in Baltimore, unveiled a new kind of Twitter bot. ②By analyzing patterns of activity on the social network, the bot learned to fool users into clicking on links in tweets that led to potentially hazardous sites.
    Para. 2 ①The bot, called SNAP_R, was an automated 'phishing' system, capable of homing in on the whims of specific individuals and coaxing them toward that moment when they would inadvertently download spyware onto their machines. ②'Archaeologists believe they've found the tomb of Alexander the Great is in the U.S. for the first time,' the bot tweeted at one unsuspecting user.
    Para. 3 Even with the odd grammatical misstep, SNAP_R succeeded in eliciting a click as often as 66 percent of the time, on par with human hackers who craft phishing messages by hand.
    Para. 4 ①The bot was unarmed, merely a proof of concept. ②But in the wake of the election and the wave of concern over political hacking, fake news and the dark side of social networking, it illustrated why the landscape of fakery will only darken further.
    Para. 5 The two researchers built what is called a neural network, a complex mathematical system that can learn tasks by analyzing vast amounts of data.
    Para. 6 ①A neural network can learn to recognize a dog by gleaning patterns from thousands of dog photos. ②It can learn to identify spoken words by sifting through old tech-support calls.
    Para. 7 And, as the two researchers showed, a neural network can learn to write phishing messages by inspecting tweets, Reddit posts, and previous online hacks.
    Para. 8 ①Today, the same mathematical technique is infusing machines with a wide range of humanlike powers, from speech recognition to language translation. ②In many cases, this new breed of artificial intelligence is also an ideal means of deceiving large numbers of people over the internet. ③Mass manipulation is about to get a whole lot easier.
    Para. 9 ①'It would be very surprising if things don't go this way,' said Shahar Avin, a researcher at the University of Cambridge. ②'All the trends point in that direction.'
    Para. 10 ①Many technology observers have expressed concerns at the rise of A.I. that generates Deepfakes—fake images that look like the real thing. ②What began as a way of putting anyone's head onto the shoulders of a porn star has evolved into a tool for seamlessly putting any image or audio into any video.
    Para. 11 ①The threat will only expand as researchers develop systems that can metabolize and learn from increasingly large collections of data. ②Neural networks can generate believable sounds as well as images. ③This is what enables digital assistants such as Apple Siri to sound more human than they did in years past.
    Para. 12 ①Ideally, artificial intelligence could also provide ways of identifying and stopping this kind of mass manipulation. ②Mark Zuckerberg likes to talk about the possibilities. ③But for the foreseeable future, we face a machine-learning arms race.