A country's AI prowess has major implications for how its citizens live and work -- and its economic and military strength moving into the future. With so much at stake, the narrative of an AI "arms race" between the US and China has been brewing for years. Dramatic headlines suggest that China is poised to take the lead in AI research and use, due to its national plan for AI domination and the billions of dollars the government has invested in the field, compared with the US' focus on private-sector development. But the reality is that at least until the past year or so, the two nations have been largely interdependent when it comes to this technology. It's an area that has drawn attention and investment from major tech heavy hitters on both sides of the Pacific, including Apple, Google and Facebook in the US and SenseTime, Megvii and YITU Technology in China. Generation China is a CNET series that looks at the areas of technology where the country is looking to take a leadership position.

If your organization is implementing or thinking of implementing a contact-tracing app, it's wise to consider more than just workforce safety. Failing to do so could expose your company other risks such as employment-related lawsuits and compliance issues. More fundamentally, companies should be thinking about the ethical implications of their AI use. Contact-tracing apps are raising a lot of questions. For example, should employers be able to use them? If so, must employees opt-in or can employers make them mandatory?

Right hands become left hands. Intrigued by how reflection changes images in subtle and not-so-subtle ways, a team of Cornell University researchers used artificial intelligence to investigate what sets originals apart from their reflections. Their algorithms learned to pick up on unexpected clues such as hair parts, gaze direction and, surprisingly, beards -- findings with implications for training machine learning models and detecting faked images. "The universe is not symmetrical. If you flip an image, there are differences," said Noah Snavely, associate professor of computer science at Cornell Tech and senior author of the study, "Visual Chirality," presented at the 2020 Conference on Computer Vision and Pattern Recognition, held virtually June 14-19.

This is an updated version. Turing Award Winner and Facebook Chief AI Scientist Yann LeCun has announced his exit from popular social networking platform Twitter after getting involved in a long and often acrimonious dispute regarding racial biases in AI. Unlike most other artificial intelligence researchers, LeCun has often aired his political views on social media platforms, and has previously engaged in public feuds with colleagues such as Gary Marcus. This time however LeCun's penchant for debate saw him run afoul of what he termed "the linguistic codes of modern social justice." It all started on June 20 with a tweet regarding the new Duke University PULSE AI photo recreation model that had depixelated a low-resolution input image of Barack Obama into a photo of a white male.

Today, we are bombarded by messages about the ways in which artificial intelligence (AI) is changing our world and its future promises and perils. But today's AI, called machine learning, is very different from much of AI in the past. From the 1970s until the 1990s, a very different approach, called "expert systems," appeared poised to radically change society in many of the same ways that today's machine learning seems. Expert systems seek to encode into software systems the experience and understanding of the finest human specialists in everything from diagnosing an infectious disease to identifying the sonar fingerprint of enemy submarines, and then have these systems suggest reasoned decisions and conclusions in new, real-world cases. Today, many of these expert systems are commonplace in everything from systems for maintenance and repair, to automated customer support systems of various sorts.

In January 2017, a group of artificial intelligence researchers gathered at the Asilomar Conference Grounds in California and developed 23 principles for artificial intelligence, which was later dubbed the Asilomar AI Principles. The sixth principle states that "AI systems should be safe and secure throughout their operational lifetime, and verifiably so where applicable and feasible." Thousands of people in both academia and the private sector have since signed on to these principles, but, more than three years after the Asilomar conference, many questions remain about what it means to make AI systems safe and secure. Verifying these features in the context of a rapidly developing field and highly complicated deployments in health care, financial trading, transportation, and translation, among others, complicates this endeavor. Much of the discussion to date has centered on how beneficial machine learning algorithms may be for identifying and defending against computer-based vulnerabilities and threats by automating the detection of and response to attempted attacks.1 Conversely, concerns have been raised that using AI for offensive purposes may make cyberattacks increasingly difficult to block or defend against by enabling rapid adaptation of malware to adjust to restrictions imposed by countermeasures and security controls.2

In October, Amazon had to discontinue an artificial intelligence–powered recruiting tool after it discovered the system was biased against female applicants. In 2016, a ProPublica investigation revealed a recidivism assessment tool that used machine learning was biased against black defendants. More recently, the US Department of Housing and Urban Development sued Facebook because its ad-serving algorithms enabled advertisers to discriminate based on characteristics like gender and race. And Google refrained from renewing its AI contract with the Department of Defense after employees raised ethical concerns. Those are just a few of the many ethical controversies surrounding artificial intelligence algorithms in the past few years.

In a newly published paper on the preprint server Arxiv.org, Through techniques like compute-efficient machine learning, federated learning, and data sovereignty, the coauthors assert scientists and practitioners have the power to cut contributions to the carbon footprint while restoring trust in historically opaque systems. Sustainability, privacy, and transparency remain underaddressed and unsolved challenges in AI. In June 2019, researchers at the University of Massachusetts at Amherst released a study estimating that the amount of power required for training and searching a given model involves the emission of roughly 626,000 pounds of carbon dioxide -- equivalent to nearly 5 times the lifetime emissions of the average U.S. car. Partnerships like those pursued by DeepMind and the U.K.'s National Health Service conceal the true nature of AI systems being developed and piloted.

A few months ago, NASA unveiled its next-generation space suit that will be worn by astronauts when they return to the moon in 2024 as part of the agency's plan to establish a permanent human presence on the lunar surface. The Extravehicular Mobility Unit--or xEMU--is NASA's first major upgrade to its space suit in nearly 40 years and is designed to make life easier for astronauts who will spend a lot of time kicking up moon dust. It will allow them to bend and stretch in ways they couldn't before, easily don and doff the suit, swap out components for a better fit, and go months without making a repair. Instead, they're hidden away in the xEMU's portable life-support system, the astro backpack that turns the space suit from a bulky piece of fabric into a personal spacecraft. It handles the space suit's power, communications, oxygen supply, and temperature regulation so that astronauts can focus on important tasks like building launch pads out of pee concrete.

Kash: The police are supposed to use facial recognition identification only as an investigative lead. But instead, people treat facial recognition as a kind of magic. And that's why you get a case where someone was arrested based on flawed software combined with inadequate police work. Witness testimony is also very troubling. That has been a selling point for many facial recognition technologies.