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By experimenting, computers are figuring out how to do things that no programmer could teach them.

Automobiles are starting to resemble robotic smartphones on wheels. Unfortunately, that makes them a pretty juicy target for would-be hackers. So far there have been relatively few incidents of car hacking beyond demonstrations by security researchers. However, GM CEO Mary Barra said today that car security would become a significant public safety issue in the years to come. “A cyber incident is not a problem just for the automaker involved,” Barra said at an industry conference held in Detroit.

Extraordinary progress has been made in artificial intelligence of late. But to achieve their full promise, machines must understand language, and that will be the most difficult step yet.

Google is using a powerful new machine-learning approach to save huge amounts of energy (and hundreds of millions of dollars) each year at its vast data centers. It might not be long before the technique, which involves a machine-learning algorithm gradually learning to perfect a task with positive reinforcement, catches on in a range of other areas.

Gary Marcus became fascinated by the mind in high school after reading The Mind’s I, a collection of essays on consciousness edited by the cognitive scientist Douglas Hofstadter and the philosopher Daniel Dennett, as well as Hofstadter’s metaphorical book on minds and machines, Gödel, Escher, Bach. Around the same time, he wrote a computer program designed to translate Latin into English.

Let's face it: the pace of progress in artificial intelligence can sometimes seem unsettling. Terminator-style machines remain science fiction, but AI could have a huge impact on employment, introduce bias into algorithms, and contribute to the development of autonomous weapons. But perhaps the biggest looming threat may be making sure we understand how these increasingly complex systems work when they go awry.

Most of us can recognize an object after seeing it once or twice. But the algorithms that power computer vision and voice recognition need thousands of examples to become familiar with each new image or word.

Spending thousands of hours playing Grand Theft Auto might have questionable benefits for humans, but it could help make computers significantly more intelligent. Several research groups are now using the hugely popular game, which features fast cars and various nefarious activities, to train algorithms that might enable a self-driving car to navigate a real road.

From the street, through Beijing’s heavy smog, it can sometimes be hard to make out IBM’s Chinese headquarters: a towering office building with a distinctive undulating architectural flourish and a large company logo at the top. But just a short distance away, on the northeast outskirts of the capital, IBM computer scientists are using artificial intelligence to develop what they think will be a way to manage China’s notorious and chronic pollution problem more successfully.

Two recent accidents involving Tesla’s Autopilot system may raise questions about how computer systems based on learning should be validated and investigated when something goes wrong. A fatal Tesla accident in Florida last month occurred when a Model S controlled by Autopilot crashed into a truck that the automated system failed to spot. Tesla tells drivers to pay attention to the road while using Autopilot, and explains in a disclaimer that the system may struggle in bright sunlight.

An unusual fleet of self-driving cars will take to the road in coming months. Unlike most automated vehicles, which are programmed to deal with the situations they may encounter, these cars will have taught themselves, in simulation, how to handle tricky scenarios safely.

No one really knows how the most advanced algorithms do what they do. That could be a serious problem as computers become more responsible for making important decisions.

Algorithms usually need thousands of examples to learn something. Researchers at Google DeepMind found a way around that.

Face-detecting systems in China now authorize payments, provide access to facilities, and track down criminals. Will other countries follow?

Uber is moving quickly. The company created its Advanced Technology Center, where it’s developing its driverless cars, in February 2015 by hiring a number of researchers from the robotics department at nearby Carnegie Mellon University.

Inside a large, windowless room in an electronics factory in south Shanghai, about 15 workers are eyeing a small robot arm with frustration. Near the end of the production line where optical networking equipment is being packed into boxes for shipping, the robot sits motionless.

If Tesla wins the race to commercialize automated driving, it might have less to do with some technological edge than its crazy customers. At the unveiling of Tesla’s sleek and “affordable” new car, the Model 3, Musk hinted that some “next level” features had yet to be revealed. This has led some to speculate that the Model 3 might be the first car equipped to hit the road with full autonomy. Tesla has some unique technological advantages in pursuing autonomy.

A startup called Zipline will use a fleet of long-distance drones to airdrop precious blood and medicines to remote medical facilities across Rwanda. The potentially life-saving project hints at the potential for unmanned aerial vehicles to revolutionize the delivery of some goods. But it also highlights the fact that drone delivery currently makes most sense only in extreme situations.

For all the remarkable progress being made in artificial intelligence, and warnings about the upheaval this might bring, the smartest computer would still struggle to make it through the eighth grade.

The robots didn’t really take over in 2015, but at times it felt as if that might be where we’re headed. There were signs that machines will soon take over manual work that currently requires human skill. Early in the year details emerged of a contest organized by Amazon to help robots do more work inside its vast product fulfillment centers.