Solving partial differential equations (PDEs) underlies much of applied mathematics and engineering, ranging from computer graphics and financial pricing, to civil engineering and weather prediction. Conventional approaches to prediction in PDE models rely on numerical solvers and require substantial computing resources in the model-application phase. While in some application domains, such as structural engineering, the longer run-times may be acceptable, in domains with rapid decay of value of the prediction, such as weather forecasting, the run-time of the solver is of paramount importance. In many such applications, the ability to generate large volumes of data facilitates the use of surrogate or reduced-order models [1] obtained using deep artificial neural networks [2]. Although the observation that artificial neural networks could be applied to physical models is not new [3, 4, 5, 6, 7, 8, 9, 5, 10], and indeed, it is seen as one of the key trends [11, 12, 13] on the interface of applied mathematics, data science, and deep learning, their applications did not reach the level of success observed in the field of the image classification, speech recognition, machine translation, and other problems processing unstructured high-dimensional data, yet. A key issue faced by applications of deep-learning techniques to physical models is their scalability. Even very recent research on deep-learning for physical models [14, 15, 16] uses a solver for PDEs to obtain hundreds of thousands of outputs. The deep learning can then be seen as means of nonlinear regression between the inputs and outputs.

Models for predicting aircraft motion are an important component of modern aeronautical systems. These models help aircraft plan collision avoidance maneuvers and help conduct offline performance and safety analyses. In this article, we develop a method for learning a probabilistic generative model of aircraft motion in terminal airspace, the controlled airspace surrounding a given airport. The method fits the model based on a historical dataset of radar-based position measurements of aircraft landings and takeoffs at that airport. We find that the model generates realistic trajectories, provides accurate predictions, and captures the statistical properties of aircraft trajectories. Furthermore, the model trains quickly, is compact, and allows for efficient real-time inference.

This paper introduces the "grasp-ability test" as a "goodness" criteria by which to compare which explanation is more or less meaningful than others for users to understand the automated algorithmic data processing. A growing number of researchers attempt to develop explainable AIs (hereafter, XAI) to meet practical (e.g., explainability is positively correlated to users' learning performance), legal (e.g., explainability is required for S.E.C. to scrutinize AIpowered trading techniques; liability issues) and ethical expectations (e.g., right to explanation; trust; autonomy). Different researchers use different ideas of what an explanation is [1]. For example, as Figure 1 shows, 11 U.S. research groups, funded by DARPA, are currently developing XAI in different manners. Then, a question is raised: how can we know which model of XAI is good enough or better/worse than others? To answer this, we need a "goodness" criteria.

US Military New Secret Technology Super Micro Drone Swarm https://youtu.be/rFrB-3D2p-A The US military has launched 103 miniature swarming drones from a fighter jet during a test in California. Three F/A-18 Super Hornets were used to release the Perdix drones last October. The drones, which have a wingspan of 12in (30cm), operate autonomously and share a distributed brain. A military analyst said the devices, able to dodge air defence systems, were likely to be used for surveillance.

The conversation about unconscious bias in artificial intelligence often focuses on algorithms that unintentionally cause disproportionate harm to entire swaths of society--those that wrongly predict black defendants will commit future crimes, for example, or facial-recognition technologies developed mainly by using photos of white men that do a poor job of identifying women and people with darker skin. But the problem could run much deeper than that. Society should be on guard for another twist: the possibility that nefarious actors could seek to attack artificial intelligence systems by deliberately introducing bias into them, smuggled inside the data that helps those systems learn. This could introduce a worrisome new dimension to cyberattacks, disinformation campaigns or the proliferation of fake news. According to a U.S. government study on big data and privacy, biased algorithms could make it easier to mask discriminatory lending, hiring or other unsavory business practices.

Artificial intelligence (AI) is becoming ever more powerful. Consulting firm PwC estimates that AI could contribute up to $15.7 trillion to the global economy in 2030, more than the combined GDP of China and India today. The technology will soon be omnipresent--from household appliances to our financial, law and justice systems. That is why we should be very worried about the dark side of AI. And this is not about devilishly powerful AIs making humans slaves, as depicted in science fiction.

TEHRAN – Yunes Adiani, an official at the research center of the parliament, has said that Iran will use artificial intelligence in legislation. In an interview with IRNA published on Sunday, he said that Iran is the second country that has taken step in applying artificial intelligence in legislation. "It has been six months that we have started this project. In this project we follow the issues of human intelligence and legislation, artificial intelligence and legislation and artificial intelligence and legislation in the world to see what other countries have used by applying artificial intelligence," Adiani stated. Adiani added, "We are considering the kind of intelligence which receives data and helps us solve problems."

Prime Minister Narendra Modi will attend Monday the fourth edition of the NITI Lecture Series focussed on'leveraging artificial intelligence for inclusive growth', according to an official statement. Modi will attend the lecture series in which the key note address will be delivered by Jensen Huang, president and co-Founder of US-based technology firm NVIDIA Corporation, the Niti Aayog said Sunday. The government think tank said the 2018 theme for the lecture series'AI for All: Leveraging Artificial Intelligence for Inclusive Growth' is part of the National Strategy for Artificial Intelligence aimed at evolving a robust ecosystem in India for AI research and adoption. Union ministers, policy makers, experts from different walks of life along with Niti Aayog vice chairman, CEO, members and other senior officials will also be present on the occasion. The Union Budget 2018 had mandated the Niti Aayog to come up with a national programme on employing artificial intelligence towards national development and since the Aayog has published a National Strategy for artificial intelligence (AI).

In an ever-increasingly digitized world, traditional cyber defense methods are no longer adequate to counter the current cyber threats, according to cybersecurity expert Samer Omar. "The increased likelihood of artificial intelligence being used by adversaries has pushed companies to continue to implement methods of detection and deception in an effort to provide counter-intelligence," he explained. Furthermore, security threat intelligence analyst Martin Giles stated that AI for cybersecurity is a hot new thing -- and a dangerous gamble. Machine learning, and artificial intelligence can help guard against cyberattacks, but hackers can foil security algorithms by targeting the data they train on and the warning flags they look for. The cybersecurity expert added there had been a steady increase in sales of cybersecurity solutions which leverage machine learning and artificial intelligence technologies enabling them to instantly detect any malicious behavior on the network, quickly respond to incidents and reduce impacts of a breach.

A mid-sized company with about 5,000 employees gets approximately 1,000 to 2,000 security incidents per day. This equates to nearly 60,000 threat incidents per month and as many as 720,000 per year. That number has increased dramatically because of automated security attacks using bots. According to The Cybersecurity Intelligence Report from Oracle Dyn "over 50% of internet traffic is bots. With these huge numbers, there are just too many threat incidents for the typical security operations team to manage with any level of precision.