When Brandon Araki arrived at MIT in 2015 as a master's candidate in mechanical engineering, he brought along the picobug, a tiny robot that can fly, crawl, and grasp small objects. Before Araki joined Daniela Rus's Distributed Robotics Lab (DRL), he'd been working with collaborators at several universities on the diminutive autonomous machine, which weighs 30 grams and fits in the palm of his hand. He wasn't quite sure what he might do next with the picobug, but when his new boss watched it in action, she was smitten. "I want a hundred of them!" Rus said. Rus, who doubles as the director of the Computer Science and Artificial Intelligence Laboratory (CSAIL), imagines a future packed with autonomous machines capable of flying, driving, performing simple surgeries, and more.
Avitas Systems, a GE subsidiary based in Boston, is now using drones and robots to automate the inspection of infrastructure such as pipelines, power lines, and transportation systems. The company is using off-the-shelf machine-learning technology from Nvidia (50 Smartest Companies 2017) to guide the checkups, and to automatically identify anomalies in the data collected. The effort shows how low-cost drones and robotic systems--combined with rapid advances in machine learning--are making it possible to automate whole sectors of low-skill work. While there is plenty of worry about the automation of jobs in manufacturing and offices, routine security and safety inspections may be one of the first big areas to be undermined by advances in AI. Drones have been used on some industrial sites for a while (see "New Boss on Construction Sites Is a Drone"), and various companies, such as Kespry, Flyability, and CyPhy, offer aerial systems for monitoring mines, inspecting wind turbines, and assessing building insurance claims.
Before autonomous trucks and taxis hit the road, manufacturers will need to solve problems far more complex than collision avoidance and navigation (see "10 Breakthrough Technologies 2017: Self-Driving Trucks"). These vehicles will have to anticipate and defend against a full spectrum of malicious attackers wielding both traditional cyberattacks and a new generation of attacks based on so-called adversarial machine learning (see "AI Fight Club Could Help Save Us from a Future of Super-Smart Cyberattacks"). As consensus grows that autonomous vehicles are just a few years away from being deployed in cities as robotic taxis, and on highways to ease the mind-numbing boredom of long-haul trucking, this risk of attack has been largely missing from the breathless coverage. It reminds me of numerous articles promoting e-mail in the early 1990s, before the newfound world of electronic communications was awash in unwanted spam. Back then, the promise of machine learning was seen as a solution to the world's spam problems.
The race to build mass-market autonomous cars is creating big demand for laser sensors that help vehicles map their surroundings. But cheaper versions of the hardware currently used in experimental self-driving vehicles may not deliver the quality of data required for driving at highway speeds. Most driverless cars make use of lidar sensors, which bounce laser beams off nearby objects to create 3-D maps of their surroundings. Lidar can provide better-quality data than radar and is superior to optical cameras because it is unaffected by variations in ambient light. You've probably seen the best-known example of a lidar sensor, produced by market leader Velodyne.
Ride-hailing startup Lyft announced last week that it's making its own self-driving car technology--a move that could help it meet an audacious goal of having autonomous vehicles chauffeur most of its passengers around by 2021. It sounds a bit far-fetched, considering that autonomous cars are still largely in the testing stages, but Lyft is just one of many companies saying that 2021 will be the year that these vehicles finally get out on the roads en masse. So, sure, it could happen. And going along with that positive line of thinking--assuming that we will, in fact, have self-driving cars in 2021--we wondered what other technological marvels and milestones await us in that magical year. According to an array of predictions from tech companies and market researchers, plenty of changes are coming, including many more developments in transportation, lots of people spending time in virtual reality, lab-grown chicken, and, just maybe, male birth control.
A husband and wife team believe they have the solution to one of the biggest hurdles to drone package delivery. By law in the U.S., a dedicated pilot must maintain a line of sight to the vehicle in order to ensure safe operation. This setup is untenable for the large-scale rollout of drone fleets, such as those planned by Amazon to handle customer deliveries in urban areas, or for public service missions involving scanning for forest fires, search and rescue operations, or shark surveillance. Lou Glaab, an aerospace technologist and NASA researcher, and his wife, Trish Glaab, a software engineer, have developed a system that they believe solves the problem. Safe2Ditch is a package of software algorithms and logic that resides within the vehicle either as in a small separate flight computer or an integrated mode in an autopilot.
With cars becoming more connected and autonomous, cybersecurity is a constant worry for automakers. They dread the likelihood of intrusions into the connected car from hackers, terrorists, extortionists, and thieves (see "Your Future Self-Driving Car Will Be Way More Hackable")--not to mention the random 12-year-old with mischief in mind. Apprehensions about automotive cybersecurity came to a head when a pair of white-hat hackers broke into a Jeep Cherokee in 2015, leading to the recall of 1.4 million vehicles by Chrysler Fiat to fix a software bug in the Uconnect infotainment system (see "Carmakers Accelerate Security Efforts after Hacking Stunts"). Cars represent a fundamentally different sort of security challenge from laptops, servers, or mobile phones, in which corruption or theft of data is the hacker's objective. A cyber-attack on a moving vehicle may create a deadly safety hazard, and conventional antihacking software could be too slow or ineffective to avert an incident.
Ford Motor Co. this week tapped Jim Hackett--a former office furniture chief executive who has been running its ride- and vehicle-sharing division since March 2016--to assume leadership of the company. Hackett's assignment: to transform the 114-year-old automaker from a company that designs and sells vehicles driven by their owners into one that makes autonomous vehicles (see "What to Know Before You Get In a Self-Driving Car"). Today carmakers sell to individual drivers through an extensive network of dealers, which makes profits both selling and servicing cars. In a world of self-driving vehicles, individuals could stop buying cars, and instead use fleets owned and operated by a third party. Ford and its competitors could become the manufacturer and third-party owner, a seller of rides as well as vehicles.