When he talks about where his fields of neuroscience and neuropsychology have taken a wrong turn, David Poeppel of New York University doesn’t mince words. “There’s an orgy of data but very little understanding,” he said to a packed room at the American Association for the Advancement of Science annual meeting in February. He decried the “epistemological sterility” of experiments that do piecework measurements of the brain’s wiring in the laboratory but are divorced from any guiding theories about behaviors and psychological phenomena in the natural world. It’s delusional, he said, to think that simply adding up those pieces will eventually yield a meaningful picture of complex thought. He pointed to the example of Caenorhabditis elegans, the roundworm that is one of the most studied lab animals. “Here’s an organism that we literally know inside out,” he said, because science has worked out every one of its 302 neurons, all of their connections and the worm’s full genome. “But we have no satisfying model for the behavior of C. elegans,” he said. “We’re missing something.” …

Cities worldwide face the problems and possibilities of “volume”: the stacking and moving of people and things within booming central business districts. We see this especially around mass public transport hubs. As cities grow, they also become more vertical. They are expanding underground through rail corridors and above ground into the tall buildings that shape city skylines. Cities are deep as well as wide. The urban geographer Stephen Graham describes cities as both “vertically stacked” and “vertically sprawled”, laced together by vertical and horizontal transport systems. People flow in large cities is not only about how people move horizontally on rail and road networks into and out of city centres. It also includes vertical transport systems. These are the elevators, escalators and moving sidewalks that commuters use every day to get from the underground to the surface street level. Major transport hubs are where many vertical and horizontal transport systems converge. It’s here that people flows are most dense. But many large cities face the twin challenges of ageing infrastructure and increased volumes of people flowing through transport …

It’s hard to send a message from Mars. When the Curiosity rover, currently active on the surface of the red planet, has something to tell NASA back on Earth, it formulates its communication in binary code and beams it our way. Noise inevitably creeps in during the long transmission, so that the message received by NASA is different from the one the rover sent. At that point it’s a game of telephone, as NASA engineers make their best guess about what Curiosity was trying to tell them. The situation from Mars is an exaggerated version of what happens whenever a message is communicated through any noisy channel — be it from a flash drive to your computer or an air traffic control tower to an airplane. In each case, the receiver has to estimate what the sender meant to say. One way to ensure that the message gets through is to use a geometric way of packaging information called a “spherical code.” A spherical code is a way of translating a message written in one …

In the late 1990s, Jun Ye, a young physicist at the research institute JILA in Boulder, Colorado, decided to dedicate much of his career to making the world’s best atomic clock. He spent some time getting to know different atoms — magnesium, calcium and barium. Eventually he settled on strontium for its internal stability. He then set to work building a laser that would tickle strontium atoms at just the right frequency. What Ye didn’t know is that he was also designing a dark matter detector. That realization came only in April 2015, when he got an email from Victor Flambaum, a physicist at the University of New South Wales in Sydney, Australia. Flambaum told Ye that according to certain theories, dark matter could subtly tweak the fundamental constants of nature, ever so slightly changing how fast clocks tick. Ye’s latest device — improved 20,000 times over his first version — was one of the few in the world sensitive enough to have a chance of picking up this faint signal. Ye is now carrying …

Until very recently, the machines that could trounce champions were at least respectful enough to start by learning from human experience. To beat Garry Kasparov at chess in 1997, IBM engineers made use of centuries of chess wisdom in their Deep Blue computer. In 2016, Google DeepMind’s AlphaGo thrashed champion Lee Sedol at the ancient board game Go after poring over millions of positions from tens of thousands of human games. But now artificial intelligence researchers are rethinking the way their bots incorporate the totality of human knowledge. The current trend is: Don’t bother. Last October, the DeepMind team published details of a new Go-playing system, AlphaGo Zero, that studied no human games at all. Instead, it started with the game’s rules and played against itself. The first moves it made were completely random. After each game, it folded in new knowledge of what led to a win and what didn’t. At the end of these scrimmages, AlphaGo Zero went head to head with the already superhuman version of AlphaGo that had beaten Lee Sedol. …