Two American B-52 bombers flew another show-of-force mission in the Persian Gulf on Wednesday, a week after President Trump warned Iran that he would hold it accountable "if one American is killed" in rocket attacks in Iraq that the administration and military officials blamed on Tehran. The warplanes' 36-hour round-trip mission from Minot Air Force Base in North Dakota was the third time in six weeks that Air Force bombers had conducted long-range flights about 60 miles off the Iranian coast, moves that military officials said were intended to deter Iran from attacking American troops in the region. The United States periodically conducts such quick demonstration missions to the Middle East and Asia to showcase American air power to allies and adversaries. But tensions have been rising in advance of the Jan. 3 anniversary of the American drone strike that killed Maj. Gen. Qassim Suleimani, the commander of Iran's elite Quds Force of the Islamic Revolutionary Guards Corps, and the Iraqi leader of an Iranian-backed militia -- deaths that Iranian leaders repeatedly insist they have not yet avenged.

Dubai/Washington – An American nuclear-powered guided-missile submarine traversed the strategically vital waterway between Iran and the Arabian Peninsula on Monday, the U.S. Navy said, in a rare announcement that comes amid rising tensions with Iran. The Navy's 5th Fleet, based in Bahrain, said the Ohio-class guided-missile submarine USS Georgia, accompanied by two other warships, passed through the Strait of Hormuz, a narrow passageway through which a fifth of the world's oil supplies travel. The unusual transit in the Persian Gulf's shallow waters, aimed at underscoring American military might in the region, follows the killing last month of Mohsen Fakhrizadeh, an Iranian scientist named by the West as the leader of the Islamic Republic's disbanded military nuclear program. It also comes some two weeks before the anniversary of the American drone strike near Baghdad airport in Iraq that killed top Iranian military commander Gen. Qassem Soleimani on Jan. 3. Iran has promised to seek revenge for both killings. The Ohio-class ballistic-missile submarine's presence in Mideast waterways signals the U.S. Navy's "commitment to regional partners and maritime security with a full spectrum of capabilities," the Navy said, demonstrating its readiness "to defend against any threat at any time."

For developing statistical and machine learning models, it is common to split the dataset into two parts: training and testing (Stone, 1974; Hastie et al., 2009). The training part is used for fitting the model, that is, to estimate the unknown parameters in the model. The model is then evaluated for its accuracy using the testing dataset. The reason for doing this is because if we were to use the entire dataset for fitting, the model would overfit the data and can lead to poor predictions in future scenarios. Therefore, holding out a portion of the dataset and testing the model for its performance before deploying it in the field can protect against unexpected issues that can arise due to overfitting. In this article we consider only datasets where each row is independent, that is, we will exclude cases such as time series data. The simplest and probably the most common strategy to split such a dataset is to randomly sample a fraction of the dataset.

Most children beginning around 2-years old can walk up to a digital assistant in their home these days, say "hey goo-goo", and get a weather forecast dictated back to them. In a quickly-growing trend, AI (Artificial Intelligence) is more and more becoming a part of everyday life. While some enjoy the digital help, performing simple tasks around the home, NOAA and Google signed an agreement to use AI in ways that could transform the weather enterprise. AI in weather is certainly nothing new. Before the fancy name (including machine learning and neural networks), scientists relied on handwritten algorithms for weather detection.

After his mother got dementia, Bari became forgetful. It was little things, like hanging up the wet laundry on time so it wouldn't stink; spraying pesticide on their patch of sea wall against the adventures of crabs and mutant fish; checking the AQI meter before leading his mother out for her evening walk along New Karachi's polluted shoreline. Did something break in your brain, too, when you took care of people who once held you on their lap, helped you count the last straggling trees in the mohalla courtyard? Overwhelmed by their needs and your grief, perhaps you were split into two halves, each perpetually being run into the ground. It wasn't like he had a sibling or a spouse to lean on.

Former CIA director, author of the book'Undaunted,' John Brennan provides insight on'Fox News Sunday.' The U.S. military flew a pair of B-52 bombers to the Middle East Thursday from Barksdale AFB in Louisiana the second deterrence mission against Iran in recent weeks and comes on the same day U.S. drones attacked al-Qaeda-linked'explosives experts' in Somalia. "We have seen some indications of increased attack planning by Iranian-linked forces inside Iraq" said one U.S. military official who declined to be identified to discuss the sensitive nature of the information. "Presidential transitions are normally a time when our adversaries try to test us," the official added. U.S. military forces are drawing down to 2,500 in Iraq and Afghanistan before January 20th.

In this work, we study emergent communication through the lens of cooperative multi-agent behavior in nature. Using insights from animal communication, we propose a spectrum from low-bandwidth (e.g. pheromone trails) to high-bandwidth (e.g. compositional language) communication that is based on the cognitive, perceptual, and behavioral capabilities of social agents. Through a series of experiments with pursuit-evasion games, we identify multi-agent reinforcement learning algorithms as a computational model for the low-bandwidth end of the communication spectrum.

Downscaling is necessary to generate high-resolution observation data to validate the climate model forecast or monitor rainfall at the micro-regional level operationally. Dynamical and statistical downscaling models are often used to get information at high-resolution gridded data over larger domains. As rainfall variability is dependent on the complex Spatio-temporal process leading to non-linear or chaotic Spatio-temporal variations, no single downscaling method can be considered efficient enough. In data with complex topographies, quasi-periodicities, and non-linearities, deep Learning (DL) based methods provide an efficient solution in downscaling rainfall data for regional climate forecasting and real-time rainfall observation data at high spatial resolutions. In this work, we employed three deep learning-based algorithms derived from the super-resolution convolutional neural network (SRCNN) methods, to precipitation data, in particular, IMD and TRMM data to produce 4x-times high-resolution downscaled rainfall data during the summer monsoon season. Among the three algorithms, namely SRCNN, stacked SRCNN, and DeepSD, employed here, the best spatial distribution of rainfall amplitude and minimum root-mean-square error is produced by DeepSD based downscaling. Hence, the use of the DeepSD algorithm is advocated for future use. We found that spatial discontinuity in amplitude and intensity rainfall patterns is the main obstacle in the downscaling of precipitation. Furthermore, we applied these methods for model data postprocessing, in particular, ERA5 data. Downscaled ERA5 rainfall data show a much better distribution of spatial covariance and temporal variance when compared with observation.

In this paper, we propose two nonparametric methods used in the forecasting of functional time-dependent data, namely functional singular spectrum analysis recurrent forecasting and vector forecasting. Both algorithms utilize the results of functional singular spectrum analysis and past observations in order to predict future data points where recurrent forecasting predicts one function at a time and the vector forecasting makes predictions using functional vectors. We compare our forecasting methods to a gold standard algorithm used in the prediction of functional, time-dependent data by way of simulation and real data and we find our techniques do better for periodic stochastic processes.

This excerpt from my upcoming book is an extension of the work I have published online on Artificial General Intelligence, Artificial General Cognition, Cognitive Artificial General Intelligence and AI Development. All of the previous content is included in The Artificial Superintelligence Handbook Series (vol. 1 & 2) available on Amazon and vol 3, including this article in full, scheduled for release early next year. Thank you to all who follow and are inspired to reach for the singularity. There is one truth in Artificial Intelligence design and development. There are lots of good coders and researchers but a huge gap between the development talent pool and financially viable commercial products.