As the technology advances, we might soon cross some threshold beyond which using AI requires a leap of faith. Sure, we humans can’t always truly explain our thought processes either—but we find ways to intuitively trust and gauge people. Will that also be possible with machines that think and make decisions differently from the way a human would? We’ve never before built machines that operate in ways their creators don’t understand. How well can we expect to communicate—and get along with—intelligent machines that could be unpredictable and inscrutable?
The internet and social media don’t create new personalities; they allow people to express sides of themselves that social norms discourage in the “real world”.
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We may come to see face-to-face conversation as the social medium that most distorts our personalities. It requires us to speak even when we don’t know what to say and forces us to be pleasant or acquiescent when we would rather not.
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Social media have turned a species used to intimacy into performers. But these performances are not necessarily false. Personality is who we are in front of other people. The internet, which exposes our elastic personalities to larger and more diverse groups of people, reveals the upper and lower bounds of our capacity for empathy and cruelty, anxiety and confidence.
The result is that modern machine learning offers a choice among oracles: Would we like to know what will happen with high accuracy, or why something will happen, at the expense of accuracy? The “why” helps us strategize, adapt, and know when our model is about to break. The “what” helps us act appropriately in the immediate future.
It can be a difficult choice to make. But some researchers hope to eliminate the need to choose—to allow us to have our many-layered cake, and understand it, too. Surprisingly, some of the most promising avenues of research treat neural networks as experimental objects—after the fashion of the biological science that inspired them to begin with—rather than analytical, purely mathematical objects.
In its first 10 years, the iPhone will have sold at least 1.2 billion units, making it the most successful product of all time. The iPhone also enabled the iOS empire which includes the iPod touch, the iPad, the Apple Watch and Apple TV whose combined total unit sales will reach 1.75 billion units over 10 years. This total is likely to top 2 billion units by the end of 2018.
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The revenues from iOS product sales will reach $980 billion by middle of this year. In addition to hardware Apple also books iOS services revenues (including content) which have totaled more than $100 billion to date.
This means that iOS will have generated over $1 trillion in revenues for Apple sometime this year.
President Jimmy Carter’s national-security adviser, Zbigniew Brzezinski, was asleep in Washington, D.C., when the phone rang. His military aide, General William Odom, was calling to inform him that two hundred and twenty missiles launched from Soviet submarines were heading toward the United States. Brzezinski told Odom to get confirmation of the attack. A retaliatory strike would have to be ordered quickly; Washington might be destroyed within minutes. Odom called back and offered a correction: twenty-two hundred Soviet missiles had been launched.
Brzezinski decided not to wake up his wife, preferring that she die in her sleep. As he prepared to call Carter and recommend an American counterattack, the phone rang for a third time. Odom apologized—it was a false alarm. An investigation later found that a defective computer chip in a communications device at NORAD headquarters had generated the erroneous warning. The chip cost forty-six cents.
The Maxforce concluded that Ireland allowed Apple to create stateless entities that effectively let it decide how much — or how little — tax it pays. The investigators say the company channeled profits from dozens of countries through two Ireland-based units. In a system at least tacitly endorsed by Irish authorities, earnings were split, with the vast majority attributed to a “head office” with no employees and no specific home base — and therefore liable to no tax on any profits from sales outside Ireland. The U.S., meanwhile, didn’t tax the units because they’re incorporated in Ireland.
Interesting detail about the secrecy surrounding the process of collecting such documents :
Three weeks after the Senate hearing, Lienemeyer’s team asked Ireland for details of Apple’s tax situation. The Irish tax authorities soon dispatched a representative carrying a briefcase filled with a bundle of bound pages. The Irish could have simply sent the material via e-mail, but they were cautious about sharing taxpayer’s information with the EU and have a ground rule to avoid leaks: never send such documents electronically.
And yet the rise of machine learning makes it more difficult for us to carve out a special place for us. If you believe, with Searle, that there is something special about human “insight,” you can draw a clear line that separates the human from the automated. If you agree with Searle’s antagonists, you can’t. It is understandable why so many people cling fast to the former view. At a 2015 M.I.T. conference about the roots of artificial intelligence, Noam Chomsky was asked what he thought of machine learning. He pooh-poohed the whole enterprise as mere statistical prediction, a glorified weather forecast. Even if neural translation attained perfect functionality, it would reveal nothing profound about the underlying nature of language. It could never tell you if a pronoun took the dative or the accusative case. This kind of prediction makes for a good tool to accomplish our ends, but it doesn’t succeed by the standards of furthering our understanding of why things happen the way they do. A machine can already detect tumors in medical scans better than human radiologists, but the machine can’t tell you what’s causing the cancer.