A truly kick-ass videogame combines clever code, gorgeous graphics, and artful animation--plus thousands of hours of hard work. Researchers at Electronic Arts--the company behind FIFA, Madden, and other popular games--are testing recent advances in artificial intelligence as a way to speed the development process and make games more lifelike. And in a neat twist, the researchers are harnessing an AI technique that proved itself by playing some of the earliest console videogames. A team from EA and the University of British Columbia in Vancouver is using a technique called reinforcement learning, which is loosely inspired by the way animals learn in response to positive and negative feedback, to automatically animate humanoid characters. "The results are very, very promising," says Fabio Zinno, a senior software engineer at Electronic Arts.

There is a growing consensus that artificial intelligence ethics instruction is critical, and must extend beyond computer sciences courses. Ethics and technology have always been tightly interwoven, but as artificial intelligence (AI) marches forward and impacts society in new and novel ways, the stakes--and repercussions--are growing. "There is potential for (AI) to be used in ways that society disapproves of," observes David S. Touretzky, a research professor in the computer science department at Carnegie Mellon University. One idea that's gaining momentum is AI ethics instruction in schools. Groups such as AI4K12 and the MIT Media Lab have begun to study the issue and develop AI learning frameworks for K-12 students.

As the economy begins the slow process of re-opening, advanced technologies such as artificial intelligence, machine learning, and natural language processing are playing a key role not only in monitoring COVID-19 outbreaks, but how companies manage the unchartered landscape before them. Use of AI during the earliest days of the pandemic centered on tracking the spread of the disease around the world. Today, AI is playing a critical role in how pharmaceutical and biotech companies research and test treatments, and in the development of a vaccine. And now, as states begin to reopen, and businesses try to find the best path forward, these advanced technologies are enabling them to figure out how to do this safely and effectively. "Companies don't have historical data to work from because they've never dealt with a crisis like this before," said Katie Stein, chief strategy officer for Genpact, a global professional services firm that specializes in digital transformation.

According to the World Health Organization, more than one billion people worldwide have disabilities. The field of disability studies defines disability through a social lens; people are disabled to the extent that society creates accessibility barriers. AI technologies offer the possibility of removing many accessibility barriers; for example, computer vision might help people who are blind better sense the visual world, speech recognition and translation technologies might offer real-time captioning for people who are hard of hearing, and new robotic systems might augment the capabilities of people with limited mobility. Considering the needs of users with disabilities can help technologists identify high-impact challenges whose solutions can advance the state of AI for all users; however, ethical challenges such as inclusivity, bias, privacy, error, expectation setting, simulated data, and social acceptability must be considered. The inclusivity of AI systems refers to whether they are effective for diverse user populations.

Machine vision coupled with artificial intelligence (AI) has made great strides toward letting computers understand images. Thanks to deep learning, which processes information in a way analogous to the human brain, machine vision is doing everything from keeping self-driving cars on the right track to improving cancer diagnosis by examining biopsy slides or x-ray images. Now some researchers are going beyond what the human eye or a camera lens can see, using machine learning to watch what people are doing on the other side of a wall. The technique relies on low-power radio frequency (RF) signals, which reflect off living tissue and metal but pass easily through wooden or plaster interior walls. AI can decipher those signals, not only to detect the presence of people, but also to see how they are moving, and even to predict the activity they are engaged in, from talking on a phone to brushing their teeth.

Despite the rapid advances it has made it over the past decade, deep learning presents many industrial users with problems when they try to implement the technology, issues that the Internet giants have worked around through brute force. "The challenge that today's systems face is the amount of data they need for training," says Tim Ensor, head of artificial intelligence (AI) at U.K.-based technology company Cambridge Consultants. "On top of that, it needs to be structured data." Most of the commercial applications and algorithm benchmarks used to test deep neural networks (DNNs) consume copious quantities of labeled data; for example, images or pieces of text that have already been tagged in some way by a human to indicate what the sample represents. The Internet giants, who have collected the most data for use in training deep learning systems, have often resorted to crowdsourcing measures such as asking people to prove they are human during logins by identifying objects in a collection of images, or simply buying manual labor through services such as Amazon's Mechanical Turk.

In its annual report, the AI Now Institute, an interdisciplinary research center studying the societal implications of artificial intelligence, called for a ban on technology designed to recognize people's emotions in certain cases. Specifically, the researchers said affect recognition technology, also called emotion recognition technology, should not be used in decisions that "impact people's lives and access to opportunities," such as hiring decisions or pain assessments, because it is not sufficiently accurate and can lead to biased decisions. What is this technology, which is already being used and marketed, and why is it raising concerns? Researchers have been actively working on computer vision algorithms that can determine the emotions and intent of humans, along with making other inferences, for at least a decade. Facial expression analysis has been around since at least 2003.

Brandon Moak felt as if a freight train had hit him. It was mid-March, and the cofounder and CTO of the autonomous- trucking startup Embark Trucks had been keeping tabs on the emergence of covid-19. As a shelter-in-place order went into effect throughout the San Francisco Bay Area, where Embark is based, Moak and his team were forced to ground almost all their 13 self-driving semi-trucks (a few stayed on the road moving essential freight but weren't in autonomous mode) and send home the majority of their workforce, with no idea how long it'd be before they could return. For safety reasons, autonomous vehicles typically have two operators apiece. That's a no-go in the age of social distancing, and leaders of autonomous-vehicle companies knew they'd have to mothball their fleets.

The New Zealand software company Rocos is training a Boston Dynamics-designed robot called Spot to work on farms to help'relieve the strain of worker shortages, and create precision in farming'.

The whistleblower who exposed in 2019 that Apple contractors listened to users' Siri recordings without their knowledge or consent has gone public to protest the lack of action taken against the technology giant. In a letter, sent to all European data protection regulators, Thomas le Bonniec said that Apple had conducted a "massive violation of the privacy of millions of citizens." He wrote that although news of the case had already gone public, the technology giant "has not been subject to any kind of investigation to the best of my knowledge." Mr Le Bonniec, who was hired by one of Apple's subcontractors in Ireland called Globe Technical Services, had to listen to recordings from users and correct transcription errors. Listening to hundreds of recortings from Apple's iPhones, iPads, and Apple Watches, many of them were taken "outside of any activation of Siri" – meaning that users were not aware of the action.