As a growing number of companies set targets to zero out their emissions of planet-warming greenhouse gases, many are turning to carbon offsets to lower their climate impact. For example, an airline might buy an offset based on a project that plants trees to absorb carbon dioxide, and count it against its own footprint. But the market is unregulated and many offsets don't deliver on their climate promises. London-based startup Sylvera Ltd. is trying to boost transparency by assigning ratings to projects, similar to what S&P Global Ratings and Moody's Investors Service do for debt issuances. So far the company has analyzed about 35 projects and found that nearly half of them don't deliver what they claim. At least three projects received the equivalent of a junk rating, according to founder Samuel Gill.

Two years ago reports revealed that Amazon was providing facial recognition software for use by police departments, even as questions arose about the bias, privacy and potentially life-destroying errors that could result. A year ago Amazon announced a one year moratorium on such use of its Rekognition platform, and now Reuters reports that instead of letting the ban expire, Amazon has extended it "until further notice." In a statement, ACLU deputy director Nathan Wessler said "Face recognition technology fuels the over-policing of Black and Brown communities, and has already led to the false arrests and wrongful incarcerations of multiple Black men. We are glad that Amazon will extend its moratorium on law enforcement use of the company's face recognition technology. Now, the Biden administration and legislatures across the country must further protect communities from the dangers of this technology by ending its use by law enforcement entirely, regardless which company is selling it."

Los Alamos National Laboratory, a multidisciplinary research institution engaged in strategic science on behalf of national security, is managed by Triad, a public service oriented, national security science organization equally owned by its three founding members: Battelle Memorial Institute (Battelle), the Texas A&M University System (TAMUS), and the Regents of the University of California (UC) for the Department of Energy's National Nuclear Security Administration. Los Alamos enhances national security by ensuring the safety and reliability of the U.S. nuclear stockpile, developing technologies to reduce threats from weapons of mass destruction, and solving problems related to energy, environment, infrastructure, health, and global security concerns.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. An unidentified inspector who failed to discover a crack in the Hernando de Soto Bridge linking Arkansas and Tennessee that prompted the span's closure was fired Monday morning and may face charges, according to reports. Arkansas Department of Transportation Director Lorie Tudor said the inspector was fired after drone video showed the crack on the bridge spanning the Mississippi River in May 2019. "This is unacceptable," Tudor said at a news conference.

Council members in King County, Wash., where Amazon's Seattle headquarters is based, are considering a local ban this month. And in Virginia, where Amazon is building its second headquarters, known as HQ2, state lawmakers just enacted one of the strictest laws in the country, requiring local law enforcement to secure state legislative approval before using any facial recognition system.

Dermatology Assist has not yet been given clearance by the Food and Drug Administration (FDA) for use in the US, but a similar machine-learning model built by British firm Optellum was recently approved by the FDA for use as an assistant in the diagnosis of lung cancer.

On May 14, the National Oceanic and Atmospheric Administration (NOAA) ship Okeanos Explorer will depart from Port Canaveral in Florida on a two-week expedition led by NOAA Ocean Exploration, featuring the technology demonstration of an autonomous underwater vehicle. Called Orpheus, this new class of submersible robot will showcase a system that will help it find its way and identify interesting scientific features on the seafloor. Terrain-relative navigation was instrumental in helping NASA's Mars 2020 Perseverance Mars rover make its precision touch down on the Red Planet on Feb. 18. The system allowed the descending robot to visually map the Martian landscape, identify hazards, and then choose a safe place to land without human assistance. In a similar way, the agency's Ingenuity Mars Helicopter uses a vision-based navigation system to track surface features on the ground during flight in order to estimate its movements across the Martian surface.

Deep neural networks excel at finding hierarchical representations that solve complex tasks over large datasets. How can we humans understand these learned representations? In this work, we present network dissection, an analytic framework to systematically identify the semantics of individual hidden units within image classification and image generation networks. First, we analyze a convolutional neural network (CNN) trained on scene classification and discover units that match a diverse set of object concepts. We find evidence that the network has learned many object classes that play crucial roles in classifying scene classes. Second, we use a similar analytic method to analyze a generative adversarial network (GAN) model trained to generate scenes. By analyzing changes made when small sets of units are activated or deactivated, we find that objects can be added and removed from the output scenes while adapting to the context. Finally, we apply our analytic framework to understanding adversarial attacks and to semantic image editing. The code, trained model weights, and datasets needed to reproduce the results in this paper are public and available to download from GitHub at and at the project website at .

Cycle representatives of persistent homology classes can be used to provide descriptions of topological features in data. However, the non-uniqueness of these representatives creates ambiguity and can lead to many different interpretations of the same set of classes. One approach to solving this problem is to optimize the choice of representative against some measure that is meaningful in the context of the data. In this work, we provide a study of the effectiveness and computational cost of several $\ell_1$-minimization optimization procedures for constructing homological cycle bases for persistent homology with rational coefficients in dimension one, including uniform-weighted and length-weighted edge-loss algorithms as well as uniform-weighted and area-weighted triangle-loss algorithms. We conduct these optimizations via standard linear programming methods, applying general-purpose solvers to optimize over column bases of simplicial boundary matrices. Our key findings are: (i) optimization is effective in reducing the size of cycle representatives, (ii) the computational cost of optimizing a basis of cycle representatives exceeds the cost of computing such a basis in most data sets we consider, (iii) the choice of linear solvers matters a lot to the computation time of optimizing cycles, (iv) the computation time of solving an integer program is not significantly longer than the computation time of solving a linear program for most of the cycle representatives, using the Gurobi linear solver, (v) strikingly, whether requiring integer solutions or not, we almost always obtain a solution with the same cost and almost all solutions found have entries in {-1, 0, 1} and therefore, are also solutions to a restricted $\ell_0$ optimization problem, and (vi) we obtain qualitatively different results for generators in Erd\H{o}s-R\'enyi random clique complexes.

Artificial Intelligence techniques powered by deep neural nets have achieved much success in several application domains, most significantly and notably in the Computer Vision applications and Natural Language Processing tasks. Surpassing human-level performance propelled the research in the applications where different modalities amongst language, vision, sensory, text play an important role in accurate predictions and identification. Several multimodal fusion methods employing deep learning models are proposed in the literature. Despite their outstanding performance, the complex, opaque and black-box nature of the deep neural nets limits their social acceptance and usability. This has given rise to the quest for model interpretability and explainability, more so in the complex tasks involving multimodal AI methods. This paper extensively reviews the present literature to present a comprehensive survey and commentary on the explainability in multimodal deep neural nets, especially for the vision and language tasks. Several topics on multimodal AI and its applications for generic domains have been covered in this paper, including the significance, datasets, fundamental building blocks of the methods and techniques, challenges, applications, and future trends in this domain