Throughout the Air Force, teams referred to as Spark Cells serve as a hub for innovation. The 59th Training Group's Alamo Spark Cell is a collaborative team that focuses on improving training at the Medical Education and Training Campus. "Our Spark Cell team works with the whole campus here and also works with the Air Force Medical Modeling and Simulation Training at Randolph," said Tech. "We have every person we can get involved within the campus, and we brainstorm ideas. We ask ourselves, how can we innovate and accelerate training?" Even during the pandemic, these innovators have implemented new ideas to help improve their students' education.

This work investigates the use of a Deep Neural Network (DNN) to perform an estimation of the Weapon Engagement Zone (WEZ) maximum launch range. The WEZ allows the pilot to identify an airspace in which the available missile has a more significant probability of successfully engaging a particular target, i.e., a hypothetical area surrounding an aircraft in which an adversary is vulnerable to a shot. We propose an approach to determine the WEZ of a given missile using 50,000 simulated launches in variate conditions. These simulations are used to train a DNN that can predict the WEZ when the aircraft finds itself on different firing conditions, with a coefficient of determination of 0.99. It provides another procedure concerning preceding research since it employs a non-discretized model, i.e., it considers all directions of the WEZ at once, which has not been done previously. Additionally, the proposed method uses an experimental design that allows for fewer simulation runs, providing faster model training.

Taxi-out time is an indicator of departure efficiency and is often the early signal of large holding and diversion events for airports that have constrained surface space. This is one of the real-time performance metrics that is of great interest to air traffic managers and flight dispatchers. For a busy airport that has limited tarmac space like LaGuardia Airport (LGA), an increasing average taxi-out time under a deteriorating visibility condition could soon lead to surface gridlock that would cause significant delays to both arrivals and departures. Thus, research is needed to develop an early alert or prediction of long taxi-out times to enable early delay mitigation actions. The problem of predicting taxi-out times has received considerable treatment in the aviation literature. Most research exploring the domain of taxi time prediction has focused on predicting taxi-out times for individual aircraft. Inaccurate taxi-out times can lead to a variety of National Airspace System (NAS) inefficiencies, such as a reduction in predictability for downstream Traffic Flow Management (TFM) applications and excess fuel consumption after push back from the gate. By better predicting aircraft-specific taxi-out times, informed updates can be made to the flight schedule to improve predictability and more efficiently use available NAS resources (e.g., capacity). Although our focus is on predicting average taxi-out time, it's worth reviewing the literature on aircraft-specific taxi-out time predictions for historical context.

Although artificial intelligence (AI) is solving real-world challenges and transforming industries, there are serious concerns about its ability to behave and make decisions in a responsible way. Many AI ethics principles and guidelines for responsible AI have been recently issued by governments, organisations, and enterprises. However, these AI ethics principles and guidelines are typically high-level and do not provide concrete guidance on how to design and develop responsible AI systems. To address this shortcoming, we first present an empirical study where we interviewed 21 scientists and engineers to understand the practitioners' perceptions on AI ethics principles and their implementation. We then propose a template that enables AI ethics principles to be operationalised in the form of concrete patterns and suggest a list of patterns using the newly created template. These patterns provide concrete, operationalised guidance that facilitate the development of responsible AI systems.

This work aims to provide an engagement decision support tool for Beyond Visual Range (BVR) air combat in the context of Defensive Counter Air (DCA) missions. In BVR air combat, engagement decision refers to the choice of the moment the pilot engages a target by assuming an offensive stance and executing corresponding maneuvers. To model this decision, we use the Brazilian Air Force's Aerospace Simulation Environment (Ambiente de Simula\c{c}\~ao Aeroespacial - ASA in Portuguese), which generated 3,729 constructive simulations lasting 12 minutes each and a total of 10,316 engagements. We analyzed all samples by an operational metric called the DCA index, which represents, based on the experience of subject matter experts, the degree of success in this type of mission. This metric considers the distances of the aircraft of the same team and the opposite team, the point of Combat Air Patrol, and the number of missiles used. By defining the engagement status right before it starts and the average of the DCA index throughout the engagement, we create a supervised learning model to determine the quality of a new engagement. An algorithm based on decision trees, working with the XGBoost library, provides a regression model to predict the DCA index with a coefficient of determination close to 0.8 and a Root Mean Square Error of 0.05 that can furnish parameters to the BVR pilot to decide whether or not to engage. Thus, using data obtained through simulations, this work contributes by building a decision support system based on machine learning for BVR air combat.

Symbolic regression is the process of identifying mathematical expressions that fit observed output from a black-box process. It is a discrete optimization problem generally believed to be NP-hard. Prior approaches to solving the problem include neural-guided search (e.g. using reinforcement learning) and genetic programming. In this work, we introduce a hybrid neural-guided/genetic programming approach to symbolic regression and other combinatorial optimization problems. We propose a neural-guided component used to seed the starting population of a random restart genetic programming component, gradually learning better starting populations. On a number of common benchmark tasks to recover underlying expressions from a dataset, our method recovers 65% more expressions than a recently published top-performing model using the same experimental setup. We demonstrate that running many genetic programming generations without interdependence on the neural-guided component performs better for symbolic regression than alternative formulations where the two are more strongly coupled. Finally, we introduce a new set of 22 symbolic regression benchmark problems with increased difficulty over existing benchmarks.

An internal report on Artificial Intelligence recently approved by a special committee of the European Parliament embodies a push from EU lawmakers and member states to make regulation on artificial intelligence less burdensome and more innovation-friendly. Christian Democrat MEP Axel Voss has been leading the charge against "overburdening" companies with excessive regulation, arguing that the EU regulatory environment should leave more room for innovation. That was the underlying motive of an own-initiative report on Artificial Intelligence in a Digital Age, recently approved in the AIDA committee, a parliamentary body set up in 2020, under Voss' leadership. "We need a better regulatory framework that learns also from the mistakes of the GDPR," Voss said while presenting the report. Instead of overburdening companies, the AI Act should give clear guidance and should leave space for innovation, he added.

Service robots hit double digit growth worldwide. The market for professional service robots reached a turnover of 6.7 billion U.S. dollars worldwide – up 12% in 2020. At the same time, turnover of new consumer service robots grew 16% to 4.4 billion U.S. dollars.

Computers and science are intertwined – and not just as tools that help humans connect and collaborate. With computers, scientists model the earth's climate, design alternative energy strategies and simulate exploding stars. From laptops to the world's fastest supercomputers, software innovations and artificial intelligence are reshaping how we interact with mounds of data from healthcare to high-energy physics and how we solve critical problems. Computational science brings together mathematics, computer science and hardware and science expertise to take on these challenges. In this podcast, you'll meet the scientists doing this work, learn more about their research and gain insights into the workings of this dynamic field.

Sometime in the midst of the last century, Chester Barnard, a retired telephone executive and author of The Functions of the Executive, imported the term "decision making" from the lexicon of public administration into the business world. There it began to replace narrower descriptors such as "resource allocation" and "policy making." The introduction of that phrase changed how managers thought about what they did and spurred a new crispness of action and desire for conclusiveness, argues William Starbuck, professor in residence at the University of Oregon's Charles H. Lundquist College of Business. "Policy making could go on and on endlessly, and there are always resources to be allocated," he explains. "'Decision' implies the end of deliberation and the beginning of action." So Barnard--and such later theorists as James March, Herbert Simon, and Henry Mintzberg--laid the foundation for the study of managerial decision making. But decision making within organizations is only one ripple in a stream of thought flowing back to a time when man, facing uncertainty, sought guidance from the stars. The questions of who makes decisions, and how, have shaped the world's systems of government, justice, and social order. "Life is the sum of all your choices," Albert Camus reminds us.