Shifting artificial intelligence to the "edge" of the network could transform computing . . . and everyday life.

This is the first in a three-part series. In the already fast-changing world of HR, the ongoing COVID-19 pandemic is creating unimagined twists and turns as 2020 progresses, leading to unprecedented attention on HR technology to help employers manage these new challenges. No emerging technology arguably has had more impact on the evolution and refinement of the pandemic workplace than artificial intelligence--which is expected to continue in the months and years ahead. One HR area that has benefited the most from AI-based solutions is workforce management, mainly in recruiting for employers whose business sectors continued to thrive, or in managing challenges such as furloughs and layoffs for the sectors hit hardest by COVID-19. According to Greg Moran, CEO at OutMatch, a SaaS-based talent intelligence platform, the movement toward HR digitization, with the use of AI and machine learning, was already well underway at the start of the year.

The algorithms build on FAIR's work in January of this year, when an agent was trained in Habitat to navigate unfamiliar environments without a map. Using just a depth-sensing camera, GPS, and compass data, it learned to enter a space much as a human would, and find the shortest possible path to its destination without wrong turns, backtracking, or exploration. The first of these new algorithms can now build a map of the space at the same time, allowing it to remember the environment and navigate through it faster if it returns. The second improves the agent's ability to map the space without needing to visit every part of it. Having been trained on enough virtual environments, it is able to anticipate certain features in a new one; it can know, for example, that there is likely to be empty floor space behind a kitchen island without navigating to the other side to look.

It is an increasingly common modern annoyance: arriving at the front of the queue to pay in a shop, pulling out a smartphone for a hygienic contact-free payment, and staring down at an error message because your phone fails to recognise your masked face. As more and more nations mandate masks to prevent the spread of coronavirus, technology companies are scrambling to keep up with the changing world. But some experts are warning that the change may have to start with users themselves. Apple's Face ID is the most well-known example of a consumer facial verification system. The technology, which uses a grid of infrared dots to measure the physical shape of a user's face, secures access to the company's iPhones and iPads, as well as other features such as Apple Pay.

Artificial intelligence can help internet service providers prevent DDoS attacks before they happen, say researchers. Findings from the National University of Singapore and Ben-Gurion University of the Negev, Israel, presented a new method in the peer-reviewed journal Computers & Security. The method uses machine learning to detect vulnerable smart home devices, which are an attractive target for hackers who assemble botnets to launch DDoS attacks. The machine learning detector does not invade customers' privacy and can pinpoint vulnerable devices even if they're not compromised. "To the best of my knowledge, telcos monitor the traffic and can only detect DDoS attacks once they are executed, which might be too late," Yair Meidan, Phd student at Ben-Gurion and the research team lead, told The Daily Swig. "In contrast, our method proposes means to detect potentially vulnerable IoT devices before they are compromised and being used to execute such attacks.

A screen shows a demonstration of SenseTime Group's SenseVideo pedestrian and vehicle recognition system at the company's showroom in Beijing. Facial recognition supporters in the US often argue that the surveillance technology is reserved for the greatest risks -- to help deal with violent crimes, terrorist threats and human trafficking. And while it's still often used for petty crimes like shoplifting, stealing $12 worth of goods or selling $50 worth of drugs, its use in the US still looks tame compared with how widely deployed facial recognition has been in China. A database leak in 2019 gave a glimpse of how pervasive China's surveillance tools are -- with more than 6.8 million records from a single day, taken from cameras positioned around hotels, parks, tourism spots and mosques, logging details on people as young as 9 days old. The Chinese government is accused of using facial recognition to commit atrocities against Uyghur Muslims, relying on the technology to carry out "the largest mass incarceration of a minority population in the world today."

Facebook researchers have developed a general AI framework called Recursive Belief-based Learning (ReBeL) that they say achieves better-than-human performance in heads-up, no-limit Texas hold'em poker while using less domain knowledge than any prior poker AI. They assert that ReBeL is a step toward developing universal techniques for multi-agent interactions -- in other words, general algorithms that can be deployed in large-scale, multi-agent settings. Potential applications run the gamut from auctions, negotiations, and cybersecurity to self-driving cars and trucks. Combining reinforcement learning with search at AI model training and test time has led to a number of advances. Reinforcement learning is where agents learn to achieve goals by maximizing rewards, while search is the process of navigating from a start to a goal state.

Citizen Science Salon is a partnership between Discover and SciStarter.org. In 2016, a team of Alzheimer's disease researchers at Cornell University hit a dead end. The scientists were studying mice, looking for links between Alzheimer's and blood flow changes in the brain. For years, scientists have known that reduced blood flow in the brain is a symptom of Alzheimer's disease. More recent research has also shown that this reduced blood flow can be caused by clogged blood vessels -- or "stalls." And by reversing these stalls in mice, scientists were able to restore their memory.

Fintechs lead as Verdict lists the top five terms tweeted on disruptive tech in Q2 2020, based on data from GlobalData's Influencer Platform. The top tweeted terms are the trending industry discussions happening on Twitter by key individuals (influencers) as tracked by the platform. New technologies and increased collaboration with fintechs shaping payments and the role of fintechs startups in transforming financial services, and innovation, were popularly discussed in Q2 2020. According to an article shared by Antonio Grasso, a digital transformation advisor, new technologies and collaborations with fintehcs were defining the future of payments. For instance, payment companies were acquiring or collaborating with SaaS companies focused on serving such as students and restaurants, the article noted.

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.