Node-level random walk has been widely used to improve Graph Neural Networks. However, there is limited attention to random walk on edge and, more generally, on $k$-simplices.

When Face Recognition Doesn't Know Your Face Is a Face An estimated 100 million people live with facial differences. As face recognition tech becomes widespread, some say they're getting blocked from accessing essential systems and services. Autumn Gardiner thought updating her driving license would be straightforward. After getting married last year, she headed to the local Department of Motor Vehicles office in Connecticut to get her name changed on her license. While she was there, Gardiner recalls, officials said she needed to update her photo.

Recent studies suggest that artificial intelligence can help reshape healthcare, particularly in identifying patients before adverse events. Mayo Clinic researchers have found AI could help spot patients at risk of stroke or cognitive decline. Another Mayo Clinic study focused on using AI to identify complications in pregnant patients. Hal Wolf, the president and chief executive officer of the Health Information and Management Systems Society (HIMSS) told Chief Healthcare Executive in a recent interview that he sees health systems turning to AI to identify health risks earlier. "The applications for AI will help in predictive modeling of what to use, where to anticipate diagnoses, how do we maximize the resources in communities," Wolf said.

Socially assistive robots have previously demonstrated potential as a tool to improve the accessibility of care, the researchers explain in their paper. For instance, a 2020 study illustrated that robots may be helpful in assessing risk factors for autism spectrum disorder (ASD). "Robots have been used for various tasks -- and they've been shown to be effective in certain things because they have this physical embodiment, unlike a mobile phone or a virtual character or even videos," Prof. Gunes said. And despite the potential dangers of allowing a child too much time with an electronic device, working one-on-one with a robot is different from screentime, Prof. Gunes noted. "This is a physical interaction, right? It's not a video -- they're physically interacting with a physical entity," she said.

NATO defence ministers have agreed upon a new master plan to defend against any potential Russian attack on multiple fronts, reaffirming the alliance's core goal of deterring Moscow despite a growing focus on China. The confidential strategy aims to prepare for any simultaneous attack in the Baltic and Black Sea regions that could include nuclear weapons, hacking of computer networks and assaults from space. "We continue to strengthen our alliance with better and modernised plans," NATO Secretary General Jens Stoltenberg said after the meeting on Thursday, which also agreed a $1bn fund to provide seed financing to develop new digital technologies. Officials stressed that they do not believe any Russian attack is imminent. Moscow has denied any aggressive intentions and said it is NATO that risks destabilising Europe with such preparations.

It is certainly an understatement to say that Rashida Hodge is an inspiration. A tenacious, 18-year tech exec, Hodge has forged an impressive career centered on exploration, expanding representation, and philanthropy. In her current role at IBM, Hodge leads product integration of artificial intelligence and other emerging technologies for key IBM clients in North America. Hodge's story will certainly motivate anyone who has the pleasure of meeting her but may be especially useful to women and people of color looking to begin a career in STEM. After our powerful discussion, it became clear that the natural choice was to let Hodge's story be told in her own, kind and confident voice. We began our conversation by discussing Hodge's childhood and early career, during which she explained how family support propelled her towards a love for and career in engineering.

How will AI strategy in the enterprise be changed by the widespread attention to systemic racism? Like a lot of complicated topics, the discussion of racism in AI systems tends to be filtered through events that make headline news -- the Microsoft chatbot that Twitter users turned into a racist, the Google algorithm that labeled images of Black people as gorillas, the photo-enhancing algorithm that changed a grainy headshot of former President Barack Obama into a white man's face. Less sensational but even more alarming are the exposés on race-biased algorithms that influence life-altering decisions on who should get loans and medical care or be arrested. Stories like these call attention to serious problems with society's application of artificial intelligence, but to understand racism in AI -- and form a business strategy for dealing with it -- enterprise leaders must get beneath the surface of the news and beyond the algorithm. "I think that racism and bias are rampant in AI and data science from inception," said Desmond Upton Patton, associate professor of sociology at Columbia University. "It starts with how we conceive a problem [for AI to solve]. The people involved in defining the problem approach it from a biased lens. It also reaches down into how we categorize the data, and how the AI tools are created. What is missing is racial inclusivity into who gets to develop AI tools."

The federal government continues its halting effort to field an enterprise cloud strategy, with Lt. Gen. Jack Shanahan, who leads the Defense Department's Joint AI Center (JAIC), commenting recently that not having an enterprise cloud platform has made the government's efforts to pursue AI more challenging. "The lack of an enterprise solution has slowed us down," stated Shanahan during an AFCEA DC virtual event held on May 21, according to an account in FCW. However, "the gears are in motion" with the JAIC using an "alternate platform" for example to host a newer anti-COVID effort. This platform is called Project Salus, and is a data aggregation that is able to employ predictive modeling to help supply equipment needed by front-line workers. The Salus platform was used for the ill-fated Project Maven, a DOD effort that was to employ AI image recognition to improve drone strike accuracy.

Technology tools such as artificial intelligence (AI), machine learning (ML) and cloud-based analytics platforms, along with aggregated "big data" organized into informational dashboards, may have cracked the code for improving worker productivity. Data about how employees work and behave can be analyzed, predicted and subsequently used to drive decisions to allocate resources, monitor performance and make the workplace better. These solutions have evolved to shape the way workers work. Vadim Tabakman is the "technical evangelist" at Nintex, a Bellevue, Wash., firm providing end-to-end process management and workflow automation. He said AI and ML are used in many ways to improve performance by learning employee work patterns and habits.

Sooner or later, everyone working in applied forms of machine learning goes after a use case that is going to yield tons of data, examples off of which to train a neural network. It's the data, many believe, that very often is the biggest deciding factor in making a network useful. That's the premise of Owlcam, a Palo Alto-based startup that sells a $349 camera for your car dashboard. It has been able to gather millions of videos from its users to refine its ability to detect crashes, to know when to capture video that can be used to handle insurance claims, or to detect an intruder to potentially solve car theft. The product, in other words, is the young company's entrée into a big problem where there's lots to learn.