If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
In many ways this year's CES looked a lot more like an autonomous-car show than a consumer electronics show. There were announcements aplenty from the likes of Ford, Baidu, Toyota, and others about self-driving vehicles, upcoming driving tests, and new partners. In a parking lot across from the Las Vegas Convention Center, several companies offered rides; you could even schedule a ride in a self-driving Lyft through the company's app and get dropped off at one of many casinos on the Strip. A couple of miles away in downtown Las Vegas, an eight-passenger autonomous shuttle bus ran in a loop around Fremont Street. It was part of an ongoing test between commuter transit company Keolis, autonomous-car maker Navya, and the city.
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. Avitas uses drones, wheeled robots, and autonomous underwater vehicles to collect images required for inspection from oil refineries, gas pipelines, coolant towers, and other equipment. Nvidia's system employs deep learning, an approach that involves training a very large simulated neural network to recognize patterns in data, and which has proven especially good for image processing. It is possible, for example, to train a deep neural network to automatically identify faults in a power line by feeding in thousands of previous examples.
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"). When hackers demonstrated that vehicles on the roads were vulnerable to several specific security threats, automakers responded by recalling and upgrading the firmware of millions of cars. The computer vision and collision avoidance systems under development for autonomous vehicles rely on complex machine-learning algorithms that are not well understood, even by the companies that rely on them (see "The Dark Secret at the Heart of AI").
The race to build mass-market autonomous cars is creating big demand for laser sensors that help vehicles map their surroundings. Most driverless cars make use of lidar sensors, which bounce laser beams off nearby objects to create 3-D maps of their surroundings. Each beam is separated by an angle of 0.4 (smaller angles between beams equal higher resolution), with a range of 120 meters. Austin Russell, the CEO of lidar startup Luminar, says his company actively chose not to use solid-state hardware in its sensors, because it believes that while mechanically steering a beam is more expensive, it currently provides more finely detailed images that are critical for safe driving.
The system stores a database of potential ditch sites for safe emergency landings, and is able to choose the ideal site based on range, size, type of terrain, reliability, and time or day constraints. It's a much more advanced system than what is currently used in most commercial UAVs, which require a designated "home" point, to which the vehicle will attempt to return in the case of a hardware malfunction or drained battery. Current models are unable to safely ditch if, for example, the remaining battery charge is unable to return the drone to its home point, or if that home point is out of date. Once these remaining technological challenges are solved, Roy believes that Safe2Ditch, or similar systems, could become an FAA-mandated safety standard in UAV manufacturing.
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"). "Dealing with consumer safety, and not just with data security, requires different security methods to protect our cars, in contrast to technologies that protect servers and enterprise networks," says David Barzilai, executive chairman and cofounder of Karamba Security, a two-year-old startup based in Hod HaSharon, Israel, with an office in metropolitan Detroit. Harman International, maker of the Uconnect system in the hacked Jeep, acquired TowerSec, an Israeli cybersecurity firm, in early 2016. Glen De Vos, chief technical officer for the automotive parts maker Delphi Automotive, says that layers of security beyond what Karamba is proposing will become necessary as cars develop more connected properties, including autonomous driving, and therefore transmit more data wirelessly both to the cloud and to one another.
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"). Though driverless technology is under Ford research and development and the direction of Ken Washington, Ford's new chief technical officer, during Hackett's time as chairman of the mobility business he became more familiar with the technological challenges of automated driving. In February, he praised Ford's commitment to acquire and invest $1 billion over the next five years in Argo.ai, a small artificial intelligence startup created by former leaders in driverless tech at Uber and Google.
The technology could also help human-driven and automated vehicles stay safe, for example by listening for emergency sirens or sounds indicating road surface quality. OtoSense has developed machine-learning software that can be trained to identify specific noises, including subtle changes in an engine or a vehicle's brakes. Under a project dubbed AudioHound, OtoSense has developed a prototype tablet app that a technician or even car owner could use to record audio for automated diagnosis, says Guillaume Catusseau, who works on vehicle noise in PSA's R&D department. Tests have shown that the system can identify unwanted noises from the engine, HVAC system, wheels, and other components.
Many tech and auto companies have begun testing modified cars on the road in recent years. Now fully working, the system is similar in capabilities to the initial version of Tesla's AutoPilot (see "10 Breakthrough Technologies: Tesla AutoPilot"). Neodriven, a startup based in Los Angeles, recently started selling a pre-built Neo device that works with Comma's Openpilot; it costs $1,495. Bryant Walker Smith, a law professor at the University of South Carolina, says that federal and state laws probably don't pose much of a barrier to those with a desire to upgrade their vehicle to share driving duties.
Mining company Rio Tinto has 73 of these titans hauling iron ore 24 hours a day at four mines in Australia's Mars-red northwest corner. BHP Billiton, the world's largest mining company, is also deploying driverless trucks and drills on iron ore mines in Australia. Suncor, Canada's largest oil company, has begun testing driverless trucks on oil sands fields in Alberta. The company's driverless trucks have proven to be roughly 15 percent cheaper to run than vehicles with humans behind the wheel, says Atkinson--a significant saving since haulage is by far a mine's largest operational cost.