WASHINGTON (The Borowitz Report)--Barack Obama recently passed a cognitive test that required him to recite the first fifty digits of Pi, the former President has disclosed. Obama took the test voluntarily, he said, in order to reassure his employers at Netflix that he was "of sound mind." "Netflix has made a big investment in me as a producer, and I thought it was important for them to know that I was all right upstairs," Obama said. The former President said that he enjoyed taking the test, including a section that required him to memorize and then recite a hundred verses of the Iliad. "That was actually a lot of fun," Obama said.

"Although scientists have a good understanding how much carbon is in the atmosphere, it's surprisingly tough to trace where those emissions come from," says Gavin McCormick, the founder of a nonprofit called WattTime that also makes technology that enables smart devices to automatically reduce emissions. The startup is working with several other climate and tech organizations and the former vice president Al Gore on the new project. Right now, McCormick says, most emissions data is self-reported, and it can sometimes take years for the data to be gathered. "We think that technology, in particular AI and satellites, have the potential to change that pretty profoundly, which can influence sort of any sector that depends on really knowing where emissions are coming from to make good decisions," he says. "The time lag in current data makes it often non-actionable," says Gore, who has been helping structure the project to have the maximum impact on the climate crisis and enlisting partners for financial and strategic support.

Kate Compton, an expert in artificial intelligence, responds to Holli Mintzer's "Legal Salvage." I've begun collecting vintage brooches. I started after reading a theory that Queen Elizabeth was communicating secret political shade through her choice of accessories. They also reminded me of my grandmother, a woman with that refined 1950s hostess style that I learned to associate with being an adult. I can wear one to feel like the sort of formidable grand dame that I imagine myself growing into as I age.

The capabilities of artificial intelligence and machine learning models are able to decipher anomalies in IT infrastructure quickly and precisely.

In this special guest feature, Joseph E. Mutschelknaus, a director in Sterne Kessler's Electronics Practice Group, addresses some of the top data privacy compliance issues that startups dealing with AI and ML applications face. He also assists with district court litigation and licensing issues. Based in Washington, D.C. and renown for more than four decades for dedication to the protection, transfer, and enforcement of intellectual property rights, Sterne, Kessler, Goldstein & Fox is one of the most highly regarded intellectual property specialty law firms in the world. Last year, the Federal Trade Commission (FTC) hit both Facebook and Google with record fines relating to their handling of personal data. The California Consumer Privacy Act (CCPA), which is widely viewed the toughest privacy law in the U.S., came online this year.

WASHINGTON, D.C. – Today, the Intelligence Community (IC) released the Principles of Artificial Intelligence (AI) Ethics for the Intelligence Community and the related Artificial Intelligence Ethics Framework for the Intelligence Community. These principles and framework, which the director of national intelligence (DNI) recently approved, will guide the IC's ethical development and use of AI. "The IC leads in developing and using technology crucial to our national security mission, and we cannot do so without recognizing and acting on its ethical implications," said DNI John Ratcliffe. "These principles and their accompanying framework will help guide our mission leads and data scientists as they implement technology to solve intelligence problems." The Principles of AI Ethics demonstrate the IC's commitment to ensuring its use and implementation of AI respect the law, protect privacy and civil liberties, are transparent and accountable, remain objective and equitable, appropriately incorporate human judgment, are secure and resilient by design, and incorporate the best practices of the science and technology communities. "In our increasingly complex digital world, the IC must adapt and adopt AI and related technologies to carry out its critical mission," said Dean Souleles, who founded ODNI's Augmenting Intelligence through Machines Innovation Hub.

A team from the University of Virginia School of Medicine is leveraging the power of quantum computing to gain better insight into genetic diseases with machine learning. Although quantum computers are still in their infancy, the researchers noted that when they do advance, they could offer computing power on a scale that's unimaginable on traditional computers. "We developed and implemented a genetic sample classification algorithm that is fundamental to the field of machine learning on a quantum computer in a very natural way using the inherent strengths of quantum computers," said Stefan Bekiranov, PhD. "This is certainly the first published quantum computer study funded by the National Institute of Mental Health and may be the first study using a so-called universal quantum computer funded by the National Institutes of Health." Quantum computers can consider significantly more possibilities than traditional computer programs.

The Trump administration announced on Friday that it would allow the sale of advanced armed drones to other nations and bypass part of an international weapons export control agreement that the United States helped forge more than three decades ago. Administration officials have debated for years how to sell the armed drones despite clauses in the agreement, known as the Missile Technology Control Regime, that prevent its 35 members from doing so. The administration announced in 2018 that it was expanding drone sales, but has not been able to do so because of the limits set by the deal. The agreement is not legally binding and is treated as an understanding among its member nations. But circumventing one part of the pact could undermine the agreement in general and encourage other nations to selectively ignore or reinterpret clauses that they find inconvenient.

In spatial statistics, a common objective is to predict the values of a spatial process at unobserved locations by exploiting spatial dependence. In geostatistics, Kriging provides the best linear unbiased predictor using covariance functions and is often associated with Gaussian processes. However, when considering non-linear prediction for non-Gaussian and categorical data, the Kriging prediction is not necessarily optimal, and the associated variance is often overly optimistic. We propose to use deep neural networks (DNNs) for spatial prediction. Although DNNs are widely used for general classification and prediction, they have not been studied thoroughly for data with spatial dependence. In this work, we propose a novel neural network structure for spatial prediction by adding an embedding layer of spatial coordinates with basis functions. We show in theory that the proposed DeepKriging method has multiple advantages over Kriging and classical DNNs only with spatial coordinates as features. We also provide density prediction for uncertainty quantification without any distributional assumption and apply the method to PM$_{2.5}$ concentrations across the continental United States.

The largely successful method of training neural networks is to learn their weights using some variant of stochastic gradient descent (SGD). Here, we show that the solutions found by SGD can be further improved by ensembling a subset of the weights in late stages of learning. At the end of learning, we obtain back a single model by taking a spatial average in weight space. To avoid incurring increased computational costs, we investigate a family of low-dimensional late-phase weight models which interact multiplicatively with the remaining parameters. Our results show that augmenting standard models with late-phase weights improves generalization in established benchmarks such as CIFAR-10/100, ImageNet and enwik8. These findings are complemented with a theoretical analysis of a noisy quadratic problem which provides a simplified picture of the late phases of neural network learning. Neural networks trained with SGD generalize remarkably well on a wide range of problems. A classic technique to further improve generalization is to ensemble many such models (Lakshminarayanan et al., 2017). At test time, the predictions made by each model are combined, usually through a simple average. Although largely successful, this technique is costly both during learning and inference. This has prompted the development of ensembling methods with reduced complexity, for example by collecting models along an optimization path generated by SGD (Huang et al., 2017), by performing interpolations in weight space (Garipov et al., 2018), or by tying a subset of the weights over the ensemble (Lee et al., 2015; Wen et al., 2020). An alternative line of work explores the use of ensembles to guide the optimization of a single model (Zhang et al., 2015; Pittorino et al., 2020). We join these efforts and develop a method that fine-tunes the behavior of SGD using late-phase weights: late in training, we replicate a subset of the weights of a neural network and randomly initialize them in a small neighborhood.