The ability to sequence genomes quickly has provided scientists with reams of data, but understanding how evolution has shaped humans is still a difficult task.Credit: Guy Tear/Wellcome Coll./CC Pinpointing where and how the human genome is evolving can be like hunting for a needle in a haystack. Each person's genome contains three billion building blocks called nucleotides, and researchers must compile data from thousands of people to discover patterns that signal how genes have been shaped by evolutionary pressures. To find these patterns, a growing number of geneticists are turning to a form of machine learning called deep learning. Proponents of the approach say that deep-learning algorithms incorporate fewer explicit assumptions about what the genetic signatures of natural selection should look like than do conventional statistical methods.

In this Oct. 31, 2018 photo, Huang Yongzhen, CEO of Watrix, demonstrates the use of his firm's gait recognition software at his company's offices in Beijing. Chinese authorities have begun deploying a new surveillance tool: "gait recognition" software that uses people's body shapes and how they walk to identify them, even when their faces are hidden from cameras.

On October 19, a Waymo Pacifica struck and injured a motorcyclist in California. As is often the case, the collision was caused by a human - in this instance, the safety driver in the Waymo vehicle. In an unusual twist, however, Waymo CEO John Krafcik revealed that if the safety operator had not taken control of the autonomous minivan, then the self-driving software would have avoided a collision. Our simulation shows the self-driving system would have responded to the passenger car by reducing our vehicle's speed, and nudging slightly in our own lane, avoiding a collision." Waymo Autonomous Vehicle ("WaymoAV") was traveling at approximately 21 MPH westbound in Lane 2 of El Camino Real in Mountain View in self-driving mode. A passenger vehicle in Lane 1, to the left of the Waymo AV, began to change lanes into Lane2 to avoid a box truck blocking two lanes of traffic, Waymo's test driver took manual control of the AV out of an abundance of caution, disengaged from self-driving mode, and began changing lanes into Lane 3. A motorcycle, traveling at approximately 28 MPH behind the Waymo AV, had just entered Lane 3 to overtake the Waymo AV on its right. The motorcyclist reported injuries and was transported to the hospital for treatment. The Waymo AV sustained minor damage to the rear bumper."

Back in 1958, in the earliest days of the computing revolution, the US Office of Naval Research organized a press conference to unveil a device invented by a psychologist named Frank Rosenblatt at the Cornell Aeronautical Laboratory. Rosenblatt called his device a perceptron, and the New York Times reported that it was "the embryo of an electronic computer that [the Navy] expects will be able to walk, talk, see, write, reproduce itself, and be conscious of its existence." Those claims turned out to be somewhat overblown. But the device kick-started a field of research that still has huge potential today. A perceptron is a single-layer neural network.

Simone Giertz is an inventor of useless robots. She finds joy and freedom in learning to build machines that are meant to fail. She shares that joy with millions through her YouTube channel. Simone Giertz is an inventor and robotics enthusiast, originally from Sweden. She began making robots in 2013, and two years later decided to start displaying her inventions on YouTube.

If you're a nefarious sort, you might use a commercial drone to smuggle drugs, carry explosives, or to just spy on your neighbors. Drones are appealing to criminals in part because they seem fairly anonymous, flitting through the sky with an invisible digital tether to its owner. But anonymity is no longer a safe bet. In the hands of crime investigators, a drone can reveal a range of personal and financial information about its owner. Most of these details are stored in memory chips inside the drone's circuit board.

Mobile phone carrier KDDI Corp. said Thursday it plans to start using drones to support rescue operations and search for missing hikers on Mount Fuji from next summer, aiming to eventually expand the service to other areas. KDDI successfully conducted a trial in conjunction with the Gotemba Municipal Government, a city to the west of the 3,776-meter-high peak; Yamap Inc., a developer of smartphone map applications; and weather information provider Weathernews Inc. During a simulated search operation carried out in late October, the drone was able to locate the position of a missing hiker who was carrying a device with global positioning capabilities, and help observe the status of the person in need of assistance. Coupled with a newly developed system that monitors and forecasts weather conditions, a drone operator selected the most suitable flight route to the location of the missing person so that rescuers could be mobilized. KDDI said it plans to add a microphone and a speaker to the drone in the future so that rescuers and hikers can communicate.

Another tech company doing something it said it wouldn't. Another eye roll, another shrug? On Tuesday, the London-based artificial intelligence company DeepMind announced that the team behind Streams – an app designed to monitor people in hospital with kidney disease – will be joining DeepMind's sister company Google. The tech giant wants to turn Streams into an AI-powered assistant for doctors and nurses. To create Streams, DeepMind used identifiable medical records of 1.6 million people obtained in a deal with the Royal …

Among creative applications for algorithms, writing lyrics and poetry has proved particularly challenging. The art of producing lyrics with a machine also differs greatly from other tasks in natural-language processing. The ultimate goal is to be creative rather than accurate, which can have a difficult-to-pin-down definition. That didn't stop two researchers at Google from trying to create an automated lyric-generation machine. They approached the task with two separate machine-learning models.

Body odor is a stubborn problem. Sensors and the computing attached to them struggle to perceive armpit odors in the way humans do, because B.O. is really a complex mix of dozens of gaseous chemicals. The UK's PlasticArmPit project is designing the first machine learning–enabled flexible plastic sensor chip. Its target audience: those who think they might stink. The prototype chip will be manufactured and tested in 2019.