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Computers are very, very fast at making calculations. Moreover, they can be counted on to calculate faithfully—without getting tired or emotional or changing their mode of analysis in midstream. But this does not mean that computers produce perfect forecasts, or even necessarily good ones. The acronym GIGO (“garbage in, garbage out”) sums up this problem. If you give a computer bad data, or devise a foolish set of instructions for it to analyze, it won’t spin straw into gold. Meanwhile, computers are not very good at tasks that require creativity and imagination, like devising strategies or developing theories about the way the world works. Computers are most useful to forecasters, therefore, in fields like weather forecasting and chess where the system abides by relatively simple and well-understood laws, but where the equations that govern the system must be solved many times over in order to produce a good forecast. They seem to have helped very little in fields like economic or earthquake forecasting where our understanding of root causes is blurrier and the data is noisier. In each of those fields, there were high hopes for computer-driven forecasting in the 1970s and 1980s when computers became more accessible to everyday academics and scientists, but little progress has been made since then.

I banned laptops in the classroom after it became common practice to carry them to school. When I created my “electronic etiquette policy” (as I call it in my syllabus), I was acting on a gut feeling based on personal experience. I’d always figured that, for the kinds of computer-science and math classes that I generally teach, which can have a significant theoretical component, any advantage that might be gained by having a machine at the ready, or available for the primary goal of taking notes, was negligible at best. We still haven’t made it easy to type notation-laden sentences, so the potential benefits were low. Meanwhile, the temptation for distraction was high. I know that I have a hard time staying on task when the option to check out at any momentary lull is available; I assumed that this must be true for my students, as well. Over time, a wealth of studies on students’ use of computers in the classroom has accumulated to support this intuition. Among the most famous is a landmark Cornell University study from 2003 called “The Laptop and the Lecture,” wherein half of a class was allowed unfettered access to their computers during a lecture while the other half was asked to keep their laptops closed. The experiment showed that, regardless of the kind or duration of the computer use, the disconnected students performed better on a post-lecture quiz. The message of the study aligns pretty well with the evidence that multitasking degrades task performance across the board.

In a document written partly in Klingon, the society argued that while Paramount commissioned the creation of the language in 1984 from linguist Marc Okrand, the language “has taken on a life of its own.” There are groups of fans whose only shared language is Klingon. People have tried to raise children as native Klingon speakers. If a language is copyrighted, the group argued, then all ideas subsequently expressed in it could be too. Owning a language would mean having the right to block any future work in that language.

Today, machines seem to get better every day at digesting vast gulps of information—and they remain as emotionally inert as ever. But since the nineteen-nineties a small number of researchers have been working to give computers the capacity to read our feelings and react, in ways that have come to seem startlingly human. Experts on the voice have trained computers to identify deep patterns in vocal pitch, rhythm, and intensity; their software can scan a conversation between a woman and a child and determine if the woman is a mother, whether she is looking the child in the eye, whether she is angry or frustrated or joyful. Other machines can measure sentiment by assessing the arrangement of our words, or by reading our gestures. Still others can do so from facial expressions. Our faces are organs of emotional communication; by some estimates, we transmit more data with our expressions than with what we say, and a few pioneers dedicated to decoding this information have made tremendous progress. Perhaps the most successful is an Egyptian scientist living near Boston, Rana el Kaliouby. Her company, Affectiva, formed in 2009, has been ranked by the business press as one of the country’s fastest-growing startups, and Kaliouby, thirty-six, has been called a “rock star.” There is good money in emotionally responsive machines, it turns out. For Kaliouby, this is no surprise: soon, she is certain, they will be ubiquitous.

HERE’S an apparent paradox: Most Americans have taken high school mathematics, including geometry and algebra, yet a national survey found that 82 percent of adults could not compute the cost of a carpet when told its dimensions and square-yard price.

Just as the Industrial Revolution spurred the creation of powerful and destructive machines like airplanes and tanks that diminished the role of individual soldiers, artificial intelligence technology is enabling the Pentagon to reorder the places of man and machine on the battlefield the same way it is transforming ordinary life with computers that can see, hear and speak and cars that can drive themselves.

Translation, however, is an example of a field where this approach fails horribly, because words cannot be reduced to their dictionary definitions, and because languages tend to have as many exceptions as they have rules. More often than not, a system like this is liable to translate “minister of agriculture” as “priest of farming.” Still, for math and chess it worked great, and the proponents of symbolic A.I. took it for granted that no activities signaled “general intelligence” better than math and chess.

Digitization promises to make medical care easier and more efficient. But are screens coming between doctors and patients?

So what, exactly, did the algorithm “learn” about the process of dying? And what, in turn, can it teach oncologists? Here is the strange rub of such a deep learning system: It learns, but it cannot tell us why it has learned; it assigns probabilities, but it cannot easily express the reasoning behind the assignment. Like a child who learns to ride a bicycle by trial and error and, asked to articulate the rules that enable bicycle riding, simply shrugs her shoulders and sails away, the algorithm looks vacantly at us when we ask, “Why?”

As a curious race we have always liked to know where we are, but it is now almost impossible not to know - our phones, computers and sat navs keep us continually co-ordinated, and through them we are involuntarily tracked ourselves. Once the preserve and privilege of the rich and influential, maps and accurate wayfinding have suddenly come to feel like a birthright

But these days we are all really at the centre of our maps, which is both a useful and egocentric thing. A thousand years ago Jerusalem stood at the centre of the Christian world view, or if you lived in China it was Youzhou. But now it is us, a throbbing green dot on our handhelds. We no longer travel from A to B but from Me to B, and we spread out maps on the floor or on our laps in a car only with wistful nostalgia.

It is still too early to say whether a lessening in our spatial ability and perspective, and our ability to remember landmarks, will decrease that area in our hippocampus that serves as the engine room for such skills, but it is highly likely. An examination of the brains of cab drivers has shown a great expansion in that area due, it is thought, to the retention of many miles of street plans.

Among the defenders of the project, however, is Anne Churchland, a neuroscientist at Cold Spring Harbor Laboratory who studies how people make decisions. In 2001, she started Anne’s List, a directory of 170 women in computational neuroscience, intended to silence claims that no good female scientists existed in that field.Her research suggests that someone you recently had lunch with or someone from your hometown might spring to mind when selecting a speaker, even though neither has anything to do with science.“It doesn’t feel like irrelevant information influences our judgment, but it does,” Dr. Churchland said.

But, no matter how closely you watched the news reports or ogled Pantone’s Web site, you never actually saw the color Emerald: the vast majority of televisions, computer monitors, and mobile devices are unable to display it, as Jeff Yurek, a communications manager at Nanosys, a company that makes color-display technology, revealed in a blog post. That’s not the only color we’re missing. If you watched this year’s Super Bowl on television, you never really saw the true shade of the Broncos’ blue helmets (Pantone No. 289). And viewing online photos of London’s famous red double-decker buses (Pantone No. 485) while you plan your vacation falls far short of experiencing that color in person. It’s easy to assume that our constantly proliferating digital devices can easily generate any color we want. But, in fact, our screens paint from a depressingly small palette: most can only recreate about a third of all the colors that our eyes can perceive.

In “Consciousness Explained,” a 1991 best-seller, he described consciousness as something like the product of multiple, layered computer programs running on the hardware of the brain. Many readers felt that he had shown how the brain creates the soul. Others thought that he’d missed the point entirely. To them, the book was like a treatise on music that focussed exclusively on the physics of musical instruments. It left untouched the question of how a three-pound lump of neurons could come to possess a point of view, interiority, selfhood, consciousness—qualities that the rest of the material world lacks. These skeptics derided the book as “Consciousness Explained Away.”