Reprinted with permission from Quanta Magazine's Abstractions blog. It's very easy to break things in biology," said Loren Frank, a neuroscientist at the University of California, San Francisco. "It's really hard to make them work better." Yet against the odds, researchers at the New York University School of Medicine reported earlier this summer that they had improved the memory of lab animals by tinkering with the length of a dynamic signal in their brains--a signal that has fascinated neuroscientists like Frank for decades. The feat is exciting in its own right, with the potential to enhance recall in people someday, too. But it also points to a more comprehensive way of thinking about memory, and it identifies an important clue, rooted in the duration of a neural event, that could pave the way to a greater understanding of how memory works. Since the 1980s, scientists have been tuning in to short bursts of synchronized neural activity in the brain area called the hippocampus.

Algorithms are a black box. We can see them at work in the world. We know they're shaping outcomes all around us. But most of us have no idea what they are -- or how we're being influenced by them. Algorithms are invisible pieces of code that tell a computer how to accomplish a specific task.

Uber became the dominant ride-hailing company in the U.S. by operating under the principle that it's better to ask for forgiveness than permission. In city after city, the San Francisco company launched without first consulting local regulators, often in violation of rules governing taxi companies. As it grew popular, statewide bans were nearly always overturned and legislation crafted to legalize app-based ride-hailing services, helping Uber roll out in more than 45 states. Now, as Uber ventures into self-driving cars -- with the long-term goal of having fleets of vehicles that don't require a driver behind the wheel -- it appears to be embracing the same aggressive tactics. But what worked in Uber's war on taxis may not work in its war on drivers, according to technology and transportation analysts, who say regulators may prove less willing to cave now that the company is pushing technology that the public doesn't yet trust.

Rejecting unwanted suitors over text is an awkward challenge for a generation of online daters. Ghostbot will detect incoming texts from the person you've chosen to "ghost" – a modern (and cowardly) dating trend that involves ceasing all communication with an unwanted suitor – and send automated responses, lacking in warmth of enthusiasm, until the other person takes the hint. For example, if the other person pushes for a date, Ghostbot might reply: "Nope," "I just have no time right now," or "Sorry, just me and [pizza emoji] tonight." This continues until the pestering messages peter out. "We hear a lot of anecdotes about terrible texting from dating matches, but sometimes blocking someone creates an awkward social circumstance. Ghostbot helps you go through the motion of ghosting someone without negative consequences," said Will Carter, co-founder of Ad Hoc Labs, which makes Ghostbot.

When it gets near, it stops on its own, then Brandon Moak leans out and says, "Would you like a ride in a self-driving vehicle?" Moak doesn't work for Google or Tesla or any of the other research labs started by major automakers. Last year, he and some friends dropped out of the University of Waterloo and started Varden Labs, an automated vehicle startup based out of a rented house just north of San Jose. This spring, the company started loaning the vehicle to various college campuses around the state to give undergrads a ride to class. It doesn't go very fast and, therefore, doesn't have to predict how it will drive very far down the road, unlike Google cars cruising at highway speeds. As it moves down the a thoroughfare at California State University Sacramento it stops and starts often and the steering system is constantly adjusting itself.