The UK's weather service, the Met Office, will spend £1.2 billion ($1.6 billion) to acquire the world's most powerful climate supercomputer, the government has announced. That's over ten times more than its current Cray XC40 cost, but it will deliver more timely and accurate forecasts. That could make the difference in knowing whether your flight will be canceled or your street flooded, according to the service. The Met said the computer will create a "digital twin" of our atmosphere with data like wind speeds, air temperatures and pressures, and more. It will create forecasts down to an area just 1,000 meters (.62 miles) across compared to 10 km (6.2 miles) currently.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. An asteroid hitting Earth is one of humanity's greatest existential threats, making it imperative that asteroid detection is a vital task for government space agencies around the world. Using advanced artificial intelligence, researchers in the Netherlands have discovered several "potentially hazardous objects" that were not spotted by humans. The research, published in Astronomy & Astrophysics, looked at space objects more than 100 meters in diameter that were likely to come within 4.7 million miles of Earth.

The rapid development of artificial intelligence technologies around the globe has led to increasing calls for robust AI policy: laws that let innovation flourish while protecting people from privacy violations, exploitive surveillance, biased algorithms, and more. But the drafting and passing of such laws has been anything but easy. "This is a very complex problem," Luis Videgaray PhD '98, director of MIT's AI Policy for the World Project, said in a lecture on Wednesday afternoon. "This is not something that will be solved in a single report. This has got to be a collective conversation, and it will take a while. It will be years in the making."

According to an American military official, the 358 missile in flight is about nine feet long and can run on kerosene or diesel fuel contained in flexible containers that do not require a separate fuel pump. A dozen infrared lenses arranged in a ring around the missile are believed to be able to defeat heat-seeking countermeasures that coalition helicopters typically use. Another United States military official said that the 358 missiles from Iran had been fired against American drones flying in Yemeni airspace, but they had not yet succeeded in hitting any. Three of the 358 missiles were captured in November by the Forrest Sherman, a Navy destroyer, and five more were recovered this month in an operation by the Normandy, a Navy cruiser. Those shipments also included more than 170 antitank guided missiles made in Iran, as well as 13,000 blasting caps, which are critical to making modern roadside bombs.

Using a machine-learning algorithm, MIT researchers have identified a powerful new antibiotic compound. In laboratory tests, the drug killed many of the world's most problematic disease-causing bacteria, including some strains that are resistant to all known antibiotics. It also cleared infections in two different mouse models. The computer model, which can screen more than a hundred million chemical compounds in a matter of days, is designed to pick out potential antibiotics that kill bacteria using different mechanisms than those of existing drugs. "We wanted to develop a platform that would allow us to harness the power of artificial intelligence to usher in a new age of antibiotic drug discovery," says James Collins, the Termeer Professor of Medical Engineering and Science in MIT's Institute for Medical Engineering and Science (IMES) and Department of Biological Engineering.

In (a), the default settings are used (bitruncated order-3 heptagonal tessellation, numbers of adjacent mines are color-coded; some of the mines that the players is sure of are marked red). In (b) we play on an order-3 heptagonal tessellation, and numbers are shown. Minesweeper is a popular game included with many computer systems; it also exists in the puzzle form. In the puzzle form, every cell in a square grid either contains a number or is empty.

Many state-of-the-art technologies developed in recent years have been influenced by machine learning to some extent. Most popular at the time of this writing are artificial intelligence methodologies that fall under the umbrella of deep learning. Deep learning has been shown across many applications to be extremely powerful and capable of handling problems that possess great complexity and difficulty. In this work, we introduce a new layer to deep learning: the fuzzy layer. Traditionally, the network architecture of neural networks is composed of an input layer, some combination of hidden layers, and an output layer. We propose the introduction of fuzzy layers into the deep learning architecture to exploit the powerful aggregation properties expressed through fuzzy methodologies, such as the Choquet and Sugueno fuzzy integrals. To date, fuzzy approaches taken to deep learning have been through the application of various fusion strategies at the decision level to aggregate outputs from state-of-the-art pre-trained models, e.g., AlexNet, VGG16, GoogLeNet, Inception-v3, ResNet-18, etc. While these strategies have been shown to improve accuracy performance for image classification tasks, none have explored the use of fuzzified intermediate, or hidden, layers. Herein, we present a new deep learning strategy that incorporates fuzzy strategies into the deep learning architecture focused on the application of semantic segmentation using per-pixel classification. Experiments are conducted on a benchmark data set as well as a data set collected via an unmanned aerial system at a U.S. Army test site for the task of automatic road segmentation, and preliminary results are promising.

We consider the problem of estimating a ranking on a set of items from noisy pairwise comparisons given item features. We address the fact that pairwise comparison data often reflects irrational choice, e.g. intransitivity. Our key observation is that two items compared in isolation from other items may be compared based on only a salient subset of features. Formalizing this framework, we propose the "salient feature preference model" and prove a sample complexity result for learning the parameters of our model and the underlying ranking with maximum likelihood estimation. We also provide empirical results that support our theoretical bounds and illustrate how our model explains systematic intransitivity. Finally we demonstrate strong performance of maximum likelihood estimation of our model on both synthetic data and two real data sets: the UT Zappos50K data set and comparison data about the compactness of legislative districts in the US.

Event datasets are sequences of events of various types occurring irregularly over the time-line, and they are increasingly prevalent in numerous domains. Existing work for modeling events using conditional intensities rely on either using some underlying parametric form to capture historical dependencies, or on non-parametric models that focus primarily on tasks such as prediction. We propose a non-parametric deep neural network approach in order to estimate the underlying intensity functions. We use a novel multi-channel RNN that optimally reinforces the negative evidence of no observable events with the introduction of fake event epochs within each consecutive inter-event interval. We evaluate our method against state-of-the-art baselines on model fitting tasks as gauged by log-likelihood. Through experiments on both synthetic and real-world datasets, we find that our proposed approach outperforms existing baselines on most of the datasets studied.

Given samples X 1,...,X n from a distribution D, can we estimate the mean of D? This is the problem of mean estimation which is, alongside hypothesis testing, one of the most fundamental questions in statistics. As a result, answers to this problem are known in fairly general settings. For instance, the empirical mean is known to be an optimal estimate of a distribution's true mean under minimal assumptions. That said, statistics like the empirical mean put aside any concerns related to the sensitivity, and might vary significantly based on the addition of a single datapoint in the dataset. While this is not an inherently negative feature, it becomes a problem when the dataset contains personal information, and large shifts based on a single datapoint could potentially violate the corresponding individual's privacy. In order to assuage these concerns, we consider the problem of mean estimation under the constraint of differential privacy (DP) [DMNS06], considered by many to be the gold standard of data privacy. Informally, an algorithm is said to be differentially private if its distribution over outputs is insensitive to the addition or removal of a single datapoint from the dataset. Differential privacy has enjoyed widespread adoption, including deployment in by Apple [Dif17], Google [EPK14], Microsoft [DKY17], and the US Census Bureau for the 2020 Census [DLS 17]. In this vein, a recent line of work [KV18, KLSU19, BKSW19] gives nearly optimal differentially private algorithms for mean estimation of sub-Gaussian random variables.