STR is seeking a Software Engineer to join our growing Autonomy team, with a focus on building User Interfaces (UIs) for controlling teams of autonomous robotic systems. As part of STR's Autonomy team, you will design, develop, and test software that ranges from early prototypes to fielded systems and will revolutionize the way that humans interact with robotic systems. You will be able to develop and test your software in simulation, live demonstration, and field experiments. This hybrid position is based in STR's state-of-the-art Autonomy lab, which is located steps from STR's Woburn, MA corporate headquarters. We are seeking candidates at all levels – from established technical leads with proven experience to talented recent graduates seeking impactful research and development opportunities.

The University of Maryland Strategic Partnership this week announced a transformative partnership to establish the University of Maryland 3 Institute for Health Computing, or UM-3-IHC. WHY IT MATTERS The effort is led by the University of Maryland, Baltimore, and the University of Maryland, College Park, in collaboration with the University of Maryland Medical System and Montgomery County, Maryland. The goal is to harness advances in artificial intelligence and machine learning to evaluate secure and deidentified and health data to diagnose, prevent and treat diseases in patients across the state of Maryland. The new institute will focus on algorithms to enable precision patient care for diabetes, high blood pressure, risk of opioid overdose and other health risks. "Scaling up research to address grand challenges in the life sciences has shifted from collecting data to using cutting-edge technology to discover meaningful patterns hidden in the data," said Darryll J. Pines, president of the University of Maryland, College Park, in a statement.

The hit BBC thriller The Capture has thrown the scale of camera surveillance into the spotlight. I ask Gus Hosein, director of Privacy International, just how advanced the technology is – and if it's possible to avoid it. You've got to watch The Capture, Gus. It's about a police officer who finds people hacking into the CCTV network, and it hit home how omnipresent cameras are. It started with stadiums and transport, because of football hooliganism, and was later expanded by Tony Blair as a solution to crime.

The next big VR avatar performance will be the late Notorious B.I.G., East Coast rap legend. Broadcast in Meta's Horizon Worlds, the show will use a virtual recreation of '90s Brooklyn as a backdrop and will have performances by guest artists like Bad Boy Records founder Sean "Diddy" Combs. It will also feature a narrative journey of Biggie's life by music journalist Touré. Bringing an artist back from the dead in avatar form often meets a wave of criticism – and that was true this time as well. Meta responded, saying it received the blessing of the Notorious B.I.G. We've also seen holograms of Michael Jackson and Whitney Houston.

The Monitor is a weekly column devoted to everything happening in the WIRED world of culture, from movies to memes, TV to Twitter. On Tuesday morning, Ruben Martinez Jr. was staring at his computer screen, calculating his chances. He was on a group chat trying to strategize the best way to score Taylor Swift tickets, and it was looking bleak. Everyone seemed to have 2,000-plus people ahead of them in line. Martinez, a software engineer at OkCupid, checked the browser developer tools to see if he could figure out his actual place in the queue. He thought he could find a percentage for how far back he was.

Engineering has a white-male problem. Women make up just 14.5 percent of the engineering workforce in the United Kingdom, with ethnic minorities constituting just 8 percent. For Lila Ibrahim, chief operating officer at DeepMind, and Hayaatun Sillem, CEO of the Royal Academy of Engineering, being both female and people of color meant the odds were stacked against them in their industry. But for Sillem, who is the first woman and ethnic minority to hold her position, coming from such a diverse background helped her "to build empathy into her life"--a trait she describes as a superpower. And as for Ibrahim, the daughter of immigrants to the United States, she always felt like the "oddball" growing up in midwestern America.

Nepal will hold national and provincial elections on Sunday, which the ruling coalition, led by the centrist Nepali Congress party, is expected to win. About 18 million people are eligible to vote for the 275-member parliament, as well as the 550 members of seven provincial assemblies through a mix of first past the post and the proportional representation system. Here are key issues that will determine how the Nepalis vote. The economy of the Himalayan nation, wedged between Asian giants China and India, is slowing down, hit by rising energy and food prices, monetary tightening and fears of a global recession. The $38bn economy is expected to expand 4.7 percent in the current fiscal year starting in mid-July, according to Asian Development Bank (ADB), down from the previous year's estimate of 5.8 percent.

This quarter marked demonstrable progress toward sector-specific approaches to the regulation of artificial intelligence and machine learning ("AI"). As the EU continues to inch toward finalizing its draft Artificial Intelligence Act--the landmark, cross-sector regulatory framework for AI/ML technologies--the White House published a "Blueprint for an AI Bill of Rights," a non-binding set of principles memorializing the Biden administration's approach to algorithmic regulation. The AI Bill of Rights joins a number of recent U.S. legislative proposals, both at the federal and state levels,[1] and the Federal Trade Commission's ("FTC") Advanced Notice of Proposed Rulemaking to solicit input on questions related to potentially harmful data privacy and security practices, including automated decision-making systems. Our 3Q22 Artificial Intelligence and Automated Systems Legal Update focuses on these regulatory efforts and also examines other policy developments within the U.S. and Europe. The past several years have seen a number of new algorithmic governance initiatives take shape at the federal level, building on the December 2020 Trustworthy AI Executive Order that outlined nine distinct principles to ensure agencies "design, develop, acquire and use AI in a manner that fosters public trust and confidence while protecting privacy."[2]

This work aims to produce landslide density estimates using Synthetic Aperture Radar (SAR) satellite imageries to prioritise emergency resources for rapid response. We use the United States Geological Survey (USGS) Landslide Inventory data annotated by experts after Hurricane Mar\'ia in Puerto Rico on Sept 20, 2017, and their subsequent susceptibility study which uses extensive additional information such as precipitation, soil moisture, geological terrain features, closeness to waterways and roads, etc. Since such data might not be available during other events or regions, we aimed to produce a landslide density map using only elevation and SAR data to be useful to decision-makers in rapid response scenarios. The USGS Landslide Inventory contains the coordinates of 71,431 landslide heads (not their full extent) and was obtained by manual inspection of aerial and satellite imagery. It is estimated that around 45\% of the landslides are smaller than a Sentinel-1 typical pixel which is 10m $\times$ 10m, although many are long and thin, probably leaving traces across several pixels. Our method obtains 0.814 AUC in predicting the correct density estimation class at the chip level (128$\times$128 pixels, at Sentinel-1 resolution) using only elevation data and up to three SAR acquisitions pre- and post-hurricane, thus enabling rapid assessment after a disaster. The USGS Susceptibility Study reports a 0.87 AUC, but it is measured at the landslide level and uses additional information sources (such as proximity to fluvial channels, roads, precipitation, etc.) which might not regularly be available in an rapid response emergency scenario.

Two years after publicly launching the AI Incident Database (AIID) as a collection of harms or near harms produced by AI in the world, a backlog of "issues" that do not meet its incident ingestion criteria have accumulated in its review queue. Despite not passing the database's current criteria for incidents, these issues advance human understanding of where AI presents the potential for harm. Similar to databases in aviation and computer security, the AIID proposes to adopt a two-tiered system for indexing AI incidents (i.e., a harm or near harm event) and issues (i.e., a risk of a harm event). Further, as some machine learning-based systems will sometimes produce a large number of incidents, the notion of an incident "variant" is introduced. These proposed changes mark the transition of the AIID to a new version in response to lessons learned from editing 2,000+ incident reports and additional reports that fall under the new category of "issue."