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Q-W-E-R-T-Y. Six letters that define so much of our waking lives.

As a direct consequence of the evolution of the human social brain, and owing to the importance of our theory-of-mind skills in that process, we sometimes can't help but see intentions, desires, and beliefs in things that haven't even a smidgeon of a neural system. In particular, when inanimate objects do unexpected things, we sometimes reason about them just as we do for oddly behaving—or misbehaving—people. More than a few of us have kicked our broken-down vehicles in the sides and verbally abused our incompetent computers. Most of us stop short of actually believing these objects possess mental states—indeed, we would likely be hauled away to an asylum if we genuinely believed that they held malicious intent—but our emotions and behaviors toward such objects seem to betray our primitive, unconscious thinking: we act as though they're morally culpable for their actions.

Is big data really all it’s cracked up to be? There is no doubt that big data is a valuable tool that has already had a critical impact in certain areas. For instance, almost every successful artificial intelligence computer program in the last 20 years, from Google’s search engine to the I.B.M. “Jeopardy!” champion Watson, has involved the substantial crunching of large bodies of data. But precisely because of its newfound popularity and growing use, we need to be levelheaded about what big data can — and can’t — do.The first thing to note is that although big data is very good at detecting correlations, especially subtle correlations that an analysis of smaller data sets might miss, it never tells us which correlations are meaningful.

The answer is that whenever you have a network operating over distance, accurate timekeeping is essential for synchronisation. And the faster the speed of travel, the more accurate the timekeeping must be. Hence in the modern world, where information travels at almost the speed of light down wires or through the air, accuracy is more important than ever. What caesium has done is to raise the standards for the measurement of time exponentially.

In a project funded by the Office of Naval Research and coordinated under the Multidisciplinary University Research Initiative, scientists will explore the challenges of infusing autonomous robots with a sense for right, wrong, and the consequences of both. "Moral competence can be roughly thought about as the ability to learn, reason with, act upon, and talk about the laws and societal conventions on which humans tend to agree," says principal investigator Matthias Scheutz, professor of computer science at Tufts School of Engineering and director of the Human-Robot Interaction Laboratory (HRI Lab) at Tufts. "The question is whether machines -- or any other artificial system, for that matter -- can emulate and exercise these abilities." One scenario is a battlefield, he says. A robot medic responsible for helping wounded soldiers is ordered to transport urgently needed medication to a nearby field hospital. En route, it encounters a Marine with a fractured leg. Should the robot abort the mission to assist the injured? Will it? If the machine stops, a new set of questions arises. The robot assesses the soldier's physical state and determines that unless it applies traction, internal bleeding in the soldier's thigh could prove fatal. However, applying traction will cause intense pain. Is the robot morally permitted to cause the soldier pain, even if it's for the soldier's well-being?

The reason typos get through isn’t because we’re stupid or careless, it’s because what we’re doing is actually very smart, explains psychologist Tom Stafford, who studies typos of the University of Sheffield in the UK. “

As with all high level tasks, your brain generalizes simple, component parts (like turning letters into words and words into sentences) so it can focus on more complex tasks (like combining sentences into complex ideas). “We don’t catch every detail, we’re not like computers or NSA databases,” said Stafford. “Rather, we take in sensory information and combine it with what we expect, and we extract meaning.”

Unfortunately, that kind of instinctual feedback doesn’t exist in the editing process. When you’re proof reading, you are trying to trick your brain into pretending that it’s reading the thing for the first time. Stafford suggests that if you want to catch your own errors, you should try to make your work as unfamiliar as possible. Change the font or background color, or print it out and edit by hand. “Once you’ve learned something in a particular way, it’s hard to see the details without changing the visual form,” he said.

I believe a major change in our perspective on consciousness may be necessary, a shift from a credulous and egocentric viewpoint to a skeptical and slightly disconcerting one: namely, that we don’t actually have inner feelings in the way most of us think we do.

How does the brain go beyond processing information to become subjectively aware of information? The answer is: It doesn’t. The brain has arrived at a conclusion that is not correct. When we introspect and seem to find that ghostly thing — awareness, consciousness, the way green looks or pain feels — our cognitive machinery is accessing internal models and those models are providing information that is wrong. The machinery is computing an elaborate story about a magical-seeming property. And there is no way for the brain to determine through introspection that the story is wrong, because introspection always accesses the same incorrect information.

One of the most fascinating examples of heuristics and biases is what we call intuition — a complex cluster of cognitive processes, sometimes helpful but often misleading. Kahneman notes that thoughts come to mind in one of two ways: Either by “orderly computation,” which involves a series of stages of remembering rules and then applying them, or by perception, an evolutionary function that allows us to predict outcomes based on what we’re perceiving.

Coherence means that you’re going to adopt one interpretation in general. Ambiguity tends to be suppressed. This is part of the mechanism that you have here that ideas activate other ideas and the more coherent they are, the more likely they are to activate each other. Other things that don’t fit fall away by the wayside. We’re enforcing coherent interpretations. We see the world as much more coherent than it is.

There is no sharp line between intuition and perception.

Back in 1969, a couple of psychologists from the University of Illinois began studying the way people in different cultures use words. Their conclusion was that whatever their culture, people tended to use positive words more often the negative ones.This finding is now known as the Pollyanna hypothesis, after a 1913 novel by Eleanor Porter about a girl who tries to find something to be glad about in every situation.

And their happy conclusion is that the data backs up the Pollyanna hypothesis. “The words of natural human language possess a universal positivity bias,” they say.

And so that anyone can sample their wares, the team has produced an online tool that allows anybody to interrogate a wide range of major novels to see how the positivity and negativity of words changes throughout. This tool is available at this website. It’s worth a look if you have 20 minutes to spare.

Could studying ants reveal clues to reducing highway traffic jams? Physicist Apoorva Nagar at the Indian Institute of Space Science and Technology thinks the answer is yes. Nagar says he got interested in the topic when he came across a study by German and Indian researchers showing that ants running along a path were able to maintain a steady speed even when there were a large number of ants on the path. Nagar says there are three main reasons ants don't jam up. Number one, ants don't have egos. They don't show off by zooming past people. "The second thing is, they do not mind a few accidents, or collisions," say Nagar. So unless there's a serious pileup, they just keep going.

The third reason, he says, is that ants seem to get more disciplined when paths get crowded, running in straighter lines, and varying their speed less. They're less likely to make unexpected moves in this sort of heavy traffic. It's the kind of steady control you see when a computer is controlling a car rather than a human. There's less variability unless it's absolutely called for. Nagar felt this kind of behavior could be explained by something called the Langevin equation, an equation physicists use when describing the movement of liquids, or how individual atoms behave in a lattice.

Bos­trom writes, “Artificial intelligence already outperforms human intelligence in many domains.” The examples range from chess to Scrabble. One program from 1981, called Eurisko, was designed to teach itself a naval role-playing game. After playing ten thousand matches, it arrived at a morally grotesque strategy: to field thousands of small, immobile ships, the vast majority of which were intended as cannon fodder. In a national tournament, Eurisko demolished its human opponents, who insisted that the game’s rules be changed. The following year, Eurisko won again—by forcing its damaged ships to sink themselves.