Since its launch 15 years ago, Google Maps has evolved from a tool to help you find your destination to a destination in and of itself. To commemorate its 15 years, Google this week is giving Maps an update and sharing insight into how technologies like AI have helped make Maps more useful and engaging. "Major breakthroughs in AI have transformed our approach to mapmaking, helping us bring high-quality maps and local information to more parts of the world faster," Jen Fitzpatrick, SVP of Google Maps, wrote in a blog post published Thursday. What is AI? Everything you need to know about Artificial Intelligence For instance, using machine learning Google added as many buildings to Maps in 2019 as it did using all techniques in the previous decade. Google's Maps team worked with its data operations team to manually trace common building outlines, Fitzpatrick explained.

True collectors are fascinating people; they're smart and persistent. As time goes on, everyday objects fall out of fashion and then, years later, clever collectors swoop in. Scouring the auction sites is a good way to find valuables and evaluate treasures. Tap or click here for 5 sneaky eBay scams to watch out for. When you're ready to look beyond eBay, I have you covered with links to government, law enforcement and Department of Treasury auctions.

"In the world of imagination, it is possible to envisage a cognitively and emotionally intelligent chief executive, who happens also to be an inspiring public communicator... and the possessor of exceptional political skill and vision. In the real world, human imperfection is inevitable, but some imperfections are more disabling than others .... Beware the presidential contender who lacks emotional intelligence. Fred Greenstein, an emeritus professor of politics at Princeton, wrote this in his book "The Presidential Difference" (third edition, 2009), which surveys the characters of American presidents from Franklin D. Roosevelt to Barack Obama and seeks to glean the characteristics needed to be a good leader. In his book, Greenstein goes deeper into the popular American habit of ranking presidents. The genesis of this method is usually ascribed to the American historian Arthur Schlesinger. In the 1940s, Schlesinger discovered a relatively empty niche in his field, American history, ...

BAGHDAD – Iraq and Russia discussed prospects for deepening military coordination, Iraq's Defense Ministry said Thursday, amid a strain in Baghdad-Washington relations after a U.S. airstrike killed a top Iranian general inside Iraq. The ministry statement followed a meeting in Baghdad between Iraqi army chief of staff Lt. Gen. Othman Al-Ghanimi and Iraq's Russian Ambassador Maksim Maksimov, as well as a newly arrived defense attache. The meeting comes during an uncertain moment in the future of Iraq-U.S. military relations, following the Jan. 3 U .S. drone strike that killed Iran's most powerful military commander, Gen. Qassem Soleimani, and Iraqi senior militia leader Abu Mahdi al-Muhandis near Baghdad airport. The attack continues to create friction, prompting powerful Shiite parties to call for an overhaul of the existing strategic set-up between Iraq and the U.S.-led coalition. Al-Ghanimi praised Moscow's role in the battle against the Islamic State group, saying they had provided "our armed forces with advanced and effective equipment and weapons that had a major role in resolving many battles," according to the ministry statement.

As the nations of Earth attempt to invent and implement solutions to the growing threat of climate change, just about every option is on the table. Investing in renewable sources of energy and dropping emissions around the globe are the dominant strategies, but utilizing artificial intelligence can help reduce the damage done by climate change. As reported by Live Mint, artificial intelligence algorithms can help conservationists limit deforestation, protect vulnerable species of animals from climate change, fight poaching, and monitor air pollution. The data science company Gramener has employed machine learning to help get estimates of the number of penguin colonies across Antarctica by analyzing images taken by camera traps. The size of penguin colonies in Antarctica has decreased dramatically over the course of the past decade, impacted by climate change.

A team of researchers have mapped the mysterious Dragon's Breath Cave in Namibia, one of the world's largest underground lakes located below the Kalahari Desert. The lake's size and depth had been a problem for human divers who attempted to document it in the past. These weren't problems for the AI-powered underwater drone, nicknamed SUNFISH, which the team from Stone Aerospace, a company in Austin, Texas, used to create the first fully realized 3D map of the mysterious cave. A team of engineers from Austin traveled to Namibia to try and map one of the world's largest underground lakes, the Dragon's Breath Cave, with an AI-powered drone SUNFISH looks like a small enclosed canoe and is powered by a set of small propellers. It uses a sonar mapping system to create a 3D image of its surroundings, which an onboard AI system then uses to make decisions about where to go next.

Machine learning algorithms are known to be susceptible to data poisoning attacks, where an adversary manipulates the training data to degrade performance of the resulting classifier. While many heuristic defenses have been proposed, few defenses exist which are certified against worst-case corruption of the training data. In this work, we propose a strategy to build linear classifiers that are certifiably robust against a strong variant of label-flipping, where each test example is targeted independently. In other words, for each test point, our classifier makes a prediction and includes a certification that its prediction would be the same had some number of training labels been changed adversarially. Our approach leverages randomized smoothing, a technique that has previously been used to guarantee---with high probability---test-time robustness to adversarial manipulation of the input to a classifier. We derive a variant which provides a deterministic, analytical bound, sidestepping the probabilistic certificates that traditionally result from the sampling subprocedure. Further, we obtain these certified bounds with no additional runtime cost over standard classification. We generalize our results to the multi-class case, providing what we believe to be the first multi-class classification algorithm that is certifiably robust to label-flipping attacks.

Can meta-learning discover generic ways of processing time-series (TS) from a diverse dataset so as to greatly improve generalization on new TS coming from different datasets? This work provides positive evidence to demonstrate this using a broad meta-learning framework which we show subsumes many existing meta-learning algorithms as specific cases. We further identify via theoretical analysis the meta-learning adaptation mechanisms within N-BEATS, a recent neural TS forecasting model. Our meta-learning theory predicts that N-BEATS iteratively generates a subset of its task-specific parameters based on a given TS input, thus gradually expanding the expressive power of the architecture on-the-fly. Our empirical results emphasize the importance of meta-learning for successful zero-shot forecasting to new sources of TS, supporting the claim that it is viable to train a neural network on a source TS dataset and deploy it on a different target TS dataset without retraining, resulting in performance that is at least as good as that of state-of-practice univariate forecasting models.

A theory explaining how deep learning works is yet to be developed. Previous work suggests that deep learning performs a coarse graining, similar in spirit to the renormalization group (RG). This idea has been explored in the setting of a local (nearest neighbor interactions) Ising spin lattice. We extend the discussion to the setting of a long range spin lattice. Markov Chain Monte Carlo (MCMC) simulations determine both the critical temperature and scaling dimensions of the system. The model is used to train both a single RBM (restricted Boltzmann machine) network, as well as a stacked RBM network. Following earlier Ising model studies, the trained weights of a single layer RBM network define a flow of lattice models. In contrast to results for nearest neighbor Ising, the RBM flow for the long ranged model does not converge to the correct values for the spin and energy scaling dimension. Further, correlation functions between visible and hidden nodes exhibit key differences between the stacked RBM and RG flows. The stacked RBM flow appears to move towards low temperatures whereas the RG flow moves towards high temperature. This again differs from results obtained for nearest neighbor Ising.

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