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").
When it comes to digital assistants like Amazon's Alexa, my four-year-old niece Hannah Metz is an early adopter. "Alexa, play'It's Raining Tacos,'" she commanded on a recent sunny afternoon, and the voice-controlled helper immediately complied, blasting through its speaker a confection of a song with lines like "It's raining tacos from out of the sky" and "Yum, yum, yum, yum, yumidy yum." These things are most popular among people age 25 to 34, which includes a ton of parents of young children and parents-to-be. Her interest in her digital assistant jibes with some findings in a recent MIT study, where researchers looked at how children ages three to 10 interacted with Alexa, Google Home, a tiny game-playing robot called Cozmo, and a smartphone app called Julie Chatbot.
If there aren't enough examples of a particular accent or vernacular, then these systems may simply fail to understand you (see "AI's Language Problem"). "If you analyze Twitter for people's opinions on a politician and you're not even considering what African-Americans are saying or young adults are saying, that seems problematic," O'Connor says. Solon Barocas, an assistant professor at Cornell and a cofounder of the event, says the field is growing, with more and more researchers exploring the issue of bias in AI systems. Shared Goel, an assistant professor at Stanford University who studies algorithmic fairness and public policy, says the issue is not always straightforward.
The next level will be using artificial intelligence in election campaigns and political life. This highly sophisticated micro-targeting operation relied on big data and machine learning to influence people's emotions. Typically disguised as ordinary human accounts, bots spread misinformation and contribute to an acrimonious political climate on sites like Twitter and Facebook. For example, if a person is interested in environment policy, an AI targeting tool could be used to help them find out what each party has to say about the environment.
In the mid-1990s, Douglas Eck worked as a database programmer in Albuquerque while moonlighting as a musician. Last spring, a few years after taking a research job at Google, Mr. Eck pitched the same idea he pitched Mr. Hofstadter all those years ago. Called deep neural networks, these complex mathematical systems allow machines to learn specific behavior by analyzing vast amounts of data. By looking for common patterns in millions of bicycle photos, for instance, a neural network can learn to recognize a bike.
A bill that would speed up development of self-driving cars and establish a federal framework for their regulation, the Highly Automated Vehicle Testing and Deployment Act of 2017, is now working its way through Congress. But they're also willing to expose vehicles via online software updates because the logistical challenges posed by physical downloads (car drives to shop, shop downloads new software) would make the frequent improvements required to millions and millions of lines of code virtually impossible to effect. Geater explained that some of the measures being taken to improve security include separating functions – the sound system can communicate with the vehicle speed system (to modulate sound volume according to vehicle speed), but neither can communicate with the transmission, for example. "People prove time and time again to be absolutely terrible, dangerous drivers," Geater said, adding that the risks posed by an actual human behind the wheel of a car far outweigh those posed by a potential hacker.
DeepMind and Blizzard Entertainment, the company behind StarCraft, just released the tools to let AI researchers create bots capable of competing in a galactic war against humans. "From a scientific point of view, the properties of StarCraft are very much like the properties of real life," says David Churchill, a professor at Memorial University of Newfoundland who advised DeepMind on its StarCraft tools who has organized a leading StarCraft bot competition. Google's AlphaGo Trounces Humans--But It Also Gives Them a Boost How Google's AI Viewed the Move No Human Could Understand In chess and Go, you can see all your opponent's moves and pieces, making them so-called perfect information games. And simply pointing current machine-learning algorithms at the new tranches of past games to copy humans won't be enough.
Instagram posts made by individuals diagnosed with depression can be reliably distinguished from posts made by healthy controls, using only measures extracted computationally from posted photos and associated metadata. In studies associating mood, color, and mental health, healthy individuals identified darker, grayer colors with negative mood, and generally preferred brighter, more vivid colors [16–19]. Instagram posts made by depressed individuals prior to the date of first clinical diagnosis can be reliably distinguished from posts made by healthy controls. The authors analyzed 118 studies that evaluated general practitioners' abilities to correctly diagnose depression in their patients, without assistance from scales, questionnaires, or other measurement instruments.
Deeplearning.ai is home to a series of online courses Ng says will help spread the benefits of recent advances in machine learning far beyond big tech companies such as Google and Baidu. His prominence comes from work on deep learning at Stanford and Google's X Labs that helped prove machine learning could do transformational things for businesses. Now there aren't enough people with machine learning skills to go around. More than 100,000 people have started a deep learning course offered by Fast.ai, a startup focused on widening use of AI.
The airline industry could save an estimated $35 Billion with pilotless planes, but the public does not like the idea. A link has been sent to your friend's email address. A link has been posted to your Facebook feed. The airline industry could save an estimated $35 Billion with pilotless planes, but the public does not like the idea.