Agile satellites with advanced attitude maneuvering capability are the new generation of Earth observation satellites (EOSs). The continuous improvement in satellite technology and decrease in launch cost have boosted the development of agile EOSs (AEOSs). To efficiently employ the increasing orbiting AEOSs, the AEOS scheduling problem (AEOSSP) aiming to maximize the entire observation profit while satisfying all complex operational constraints, has received much attention over the past 20 years. The objectives of this paper are thus to summarize current research on AEOSSP, identify main accomplishments and highlight potential future research directions. To this end, general definitions of AEOSSP with operational constraints are described initially, followed by its three typical variations including different definitions of observation profit, multi-objective function and autonomous model. A detailed literature review from 1997 up to 2019 is then presented in line with four different solution methods, i.e., exact method, heuristic, metaheuristic and machine learning. Finally, we discuss a number of topics worth pursuing in the future.

AI is adding power to these chatbots and helping bridge the gap between humans and machines by employing natural language capabilities. These more intelligent chatbots are more capable and are being used in a variety of different contexts such as online customer support, phone interactions, information retrieval, assisting with online commerce or tech support, and the increasing popularity of voice assistants. Because chatbots are easy to deploy, companies find them a great first use case for AI within their organization. Because bots can provide consistent results without the need to sleep or take breaks, companies are able to keep them deployed to engage and interact with customers. Organizations including banking, finance, retail, and others have AI-enabled chatbots to help enhance customer engagement, collect basic customer information and answer general company questions.

Work and Winnefeld discuss the Pentagon's "Third Offset" Strategy, and delve into the military applications and ethical dimensions of technologies like artificial intelligence and quantum science. They also review the Defense Department's transition from focusing on counterterrorism and counterinsurgency to great power competition. Work, now the Distinguished Senior Fellow for Defense and National Security at the Center for a New American Security, explains how Russia and China are developing a range of technologies in an effort to leapfrog the U.S. in the military realm. Military applications of new technologies: "We don't know how AI and 5G and quantum and synthetic biology, we don't know how they are all going to go to work. But they all have the capabilities to provide a step function in the way we fight wars. And the competitor who gets there first is going to have an enormous advantage. This is a time of enormous foment inside the department. The stakes associated with AI: "[T]he competition in AI is a central one in great power competition between China and Russia. AI will reflect the values of the competitors. Whereas we want to protect human privacy, we want to protect human dignity, we want to make sure that our use of AI is ethical and moral and consistent with our laws, an authoritarian regime might not do it that way." On competition with Russia and China: "This is not a time where we can really afford to waste the time we have. We believe that the Chinese and the Russians are really pressing us in the military sphere. They've had 18 years of kind of coming after us while we've been focused on counterterrorism. And so they've closed the gap to an uncomfortable degree.

The American retaliation led to a siege of the United States Embassy in Baghdad and then an American drone attack that killed the leader of Iran's elite Quds force, Maj. The cycle of attacks and counterattacks ended more than two weeks later after Iran launched 16 cruise missiles at bases in Iraq that house American forces. No one was killed by the Iranian missile attacks and tensions had appeared to subside. An Iraqi military official said that hours after the attack on Wednesday, the American-led coalition responded with airstrikes on camps used by Kataib Hezbollah near Abu Kamal in Syria, just across the border from Qaim, Iraq. However American officials said the United States had not carried out those strikes.

As an accounting professional, your clients expect you to help them navigate a fast-changing business environment so they can continue to grow and prosper. The advice many accounting practices provide today include compliance, current tax laws, and how to keep cost structures in line. However, it is also critical to keep up with the current technologies that can transform both your client's business and your practice. There are three core technologies – artificial intelligence, blockchain, and the cloud– that have an impact in virtually every market. The challenge is, even though your clients are aware of these technologies, most are not yet prepared to invest in them.

Military research agency DARPA wants video game developers to help it develop AI fighter jets. The Pentagon's so-called "mad science" wing is seeking their support for its Air Combat Evolution (ACE) program. ACE aims to build the trust of fighter pilots in automated combat by testing algorithms in close-range aerial battles -- better known as dogfights. But before they're dodging enemy jets for real, the algorithms need to be trialed in simulations. Lieutenant Colonel Dan Javorsek, who also goes by the cuddly nom de guerre of "Animal," envisions the algorithms guiding an aircraft's maneuvers so human pilots can focus on the "higher-level context intent and sentiment challenges" of future battles.

BENGALURU: The Department of Science & Technology (DST) - Government of India, Intel India, and Society for Innovation & Entrepreneurship (SINE)-IIT Bombay has announced the third edition of Plugin. This is a one-year collaborative accelerator program for hardware and systems software startups. Eleven startups have been selected from across the country in the areas of artificial intelligence (AI), machine learning (ML), security and platform to receive mentoring, access to labs for tools and platforms, technical and financial support, local and international ecosystem connect and visibility. Nivruti Rai, Country Head, Intel India and VP - Data Platforms Group, Intel Corp, in a statement, said, "Our Intel India Maker Lab incubation program has so far supported over 70 startups in accelerating their innovation journey and scaling their businesses. In its third year, Plugin is supporting startups that are using data-centric technologies to drive innovation in the healthcare, manufacturing, industrial, retail, automotive and banking domains."

In the course of the two years, we have seen a consistent increment in the percentage of adoption of AI in India. Given the Indian government's ongoing focus on building up a plan for artificial intelligence, it is recommended to apply strengths (deep analysis of AI applications and implications) to determine (a) the state of AI innovation in India, and (b) strategic insights to help India survive and thrive in a global market with the help of AI initiatives. Advances in artificial intelligence and data analytics are pushing development in numerous parts of the world. China, for instance, has committed $150 billion towards its objective of turning into a world chief by 2030. And while the United States government is putting just $1.1 billion in non-classified AI research, its private sector is burning through billions in fields from finance and healthcare to retail and defense.

Spatial prediction is commonly achieved under the assumption of a Gaussian random field (GRF) by obtaining maximum likelihood estimates of parameters, and then using the kriging equations to arrive at predicted values. For massive datasets, fixed rank kriging using the Expectation-Maximization (EM) algorithm for estimation has been proposed as an alternative to the usual but computationally prohibitive kriging method. The method reduces computation cost of estimation by redefining the spatial process as a linear combination of basis functions and spatial random effects. A disadvantage of this method is that it imposes constraints on the relationship between the observed locations and the knots. We develop an alternative method that utilizes the Spatial Mixed Effects (SME) model, but allows for additional flexibility by estimating the range of the spatial dependence between the observations and the knots via an Alternating Expectation Conditional Maximization (AECM) algorithm. Experiments show that our methodology improves estimation without sacrificing prediction accuracy while also minimizing the additional computational burden of extra parameter estimation. The methodology is applied to a temperature data set archived by the United States National Climate Data Center, with improved results over previous methodology.

Vertex classification is vulnerable to perturbations of both graph topology and vertex attributes, as shown in recent research. As in other machine learning domains, concerns about robustness to adversarial manipulation can prevent potential users from adopting proposed methods when the consequence of action is very high. This paper considers two topological characteristics of graphs and explores the way these features affect the amount the adversary must perturb the graph in order to be successful. We show that, if certain vertices are included in the training set, it is possible to substantially an adversary's required perturbation budget. On four citation datasets, we demonstrate that if the training set includes high degree vertices or vertices that ensure all unlabeled nodes have neighbors in the training set, we show that the adversary's budget often increases by a substantial factor---often a factor of 2 or more---over random training for the Nettack poisoning attack. Even for especially easy targets (those that are misclassified after just one or two perturbations), the degradation of performance is much slower, assigning much lower probabilities to the incorrect classes. In addition, we demonstrate that this robustness either persists when recently proposed defenses are applied, or is competitive with the resulting performance improvement for the defender.