E. Allen Emerson was the first graduate student of Edmund M. Clarke at Harvard University. After discussing several ideas for Allen's dissertation, they identified a promising candidate: verifying a finite-state system against a formal specification. According to Martha Clarke, Edmund's widow, it was during a walk across Harvard Yard that they decided to call it "model checking." Emerson received his Ph.D. in applied mathematics for this work in 1981. Twenty-five years later, he and Clarke (along with Joseph Sifakis) shared the ACM A.M. Turing Award in 2007 for this and related work.

With pre-training of vision-and-language models (VLMs) on large-scale datasets of image-text pairs, several recent works showed that these pre-trained models lack fine-grained understanding, such as the ability to count and recognize verbs, attributes, or relationships. The focus of this work is to study the ability of these models to understand spatial relations. Previously, this has been tackled using image-text matching (e.g., Visual Spatial Reasoning benchmark) or visual question answering (e.g., GQA or VQAv2), both showing poor performance and a large gap compared to human performance. In this work, we use explainability tools to understand the causes of poor performance better and present an alternative fine-grained, compositional approach for ranking spatial clauses. We combine the evidence from grounding noun phrases corresponding to objects and their locations to compute the final rank of the spatial clause. We demonstrate the approach on representative VLMs (such as LXMERT, GPV, and MDETR) and compare and highlight their abilities to reason about spatial relationships.

The week before heading off to college, Harry Emerson was diagnosed with type 1 diabetes. Without the ability to produce insulin, the hormone that transports blood sugar to fuel other cells, he'd need help from medical devices to survive, his doctors told him. Eager to get on with school, Emerson rushed through the process of familiarizing himself with the technology, then went off to university. Because people with type 1 diabetes make very little or no insulin on their own, they need to keep careful track of their blood sugar as it changes throughout the day. They inject insulin when their blood sugar is too high or when it's about to spike after a meal and keep fast-acting carbs ready to eat when it dips too low.

On July 13, 1833, during a visit to the Cabinet of Natural History at the Jardin des Plantes, in Paris, Ralph Waldo Emerson had an epiphany. Peering at the museum's specimens--butterflies, hunks of amber and marble, carved seashells--he felt overwhelmed by the interconnectedness of nature, and humankind's place within it. Check out more from this issue and find your next story to read. The experience inspired him to write "The Uses of Natural History," and to articulate a philosophy that put naturalism at the center of intellectual life in a technologically chaotic age--guiding him, along with the collective of writers and radical thinkers known as transcendentalists, to a new spiritual belief system. Through empirical observation of the natural world, Emerson believed, anyone could become "a definer and map-maker of the latitudes and longitudes of our condition"--finding agency, individuality, and wonder in a mechanized age. America was crackling with invention in those years, and everything seemed to be speeding up as a result.

Meet Emerson and discover how you can cash in on the next great gold rush of the century with Artificial Intelligence.

Despite the remarkable technological advances that fill our lives today, the ways we work with the metals that underlie these developments haven't changed significantly in thousands of years. This is true of everything from the metal rods, tubes, and cubes that provide cars and trucks with their shape, strength, and fuel economy, to wires that move electrical energy in everything from motors to undersea cables. But things are changing rapidly: The materials manufacturing industry is using new and innovative technologies, processes, and methods to improve existing products and create new ones. Pacific Northwest National Laboratory (PNNL) is a leader in this space, known as advanced manufacturing. For example, scientists working in PNNL's Mathematics for Artificial Reasoning in Science initiative are pioneering approaches in the branch of artificial intelligence known as machine learning to design and train computer software programs that guide the development of new manufacturing processes.

Despite the remarkable technological advances that fill our lives today, the ways we work with the metals that underlie these developments haven't changed significantly in thousands of years. This is true of everything from the metal rods, tubes, and cubes that provide cars and trucks with their shape, strength, and fuel economy, to wires that move electrical energy in everything from motors to undersea cables. But things are changing rapidly: The materials manufacturing industry is using new and innovative technologies, processes, and methods to improve existing products and create new ones. Pacific Northwest National Laboratory (PNNL) is a leader in this space, known as advanced manufacturing. For example, scientists working in PNNL's Mathematics for Artificial Reasoning in Science initiative are pioneering approaches in the branch of artificial intelligence known as machine learning to design and train computer software programs that guide the development of new manufacturing processes.

In the wake of the #MeToo and Black Lives Matter movements, HR leaders are increasingly being tasked with addressing the inequities that exist in their workplaces. This may involve brutal self-reflection and a willingness to admit that their companies are far from the equitable places they once thought they were. On a positive note, experts say, employers appear to be taking diversity, equity and inclusion efforts seriously--and they're getting some help from tech. For instance, some HR technology providers are creating AI-based solutions that shine a light on employers' often decades-old management practices that can allow them to overlook an all-white senior management team and C-suite or to ignore instances where minorities are leaving hostile workplaces. The role of technology should not be underestimated and can be a great enabler of DE&I, says Kay Formanek, founder and CEO of Diversity and Performance, a diversity education company.

In a proof-of-concept study, education and artificial intelligence researchers have demonstrated the use of a machine-learning model to predict how long individual museum visitors will engage with a given exhibit. The finding opens the door to a host of new work on improving user engagement with informal learning tools. "Education is an important part of the mission statement for most museums," says Jonathan Rowe, co-author of the study and a research scientist in North Carolina State University's Center for Educational Informatics (CEI). "The amount of time people spend engaging with an exhibit is used as a proxy for engagement and helps us assess the quality of learning experiences in a museum setting. It's not like school--you can't make visitors take a test."

Learn how KNet Analytics combines principles-driven analytics with artificial intelligence and machine learning to address asset maintenance management--from identifying problems and providing root cause analysis to providing corrective steps.