He also explains where he draws inspiration from in his research, and why robotics has yet to meet science fiction. Vijay Kumar is the Nemirovsky Family Dean of Penn Engineering with appointments in the Departments of Mechanical Engineering and Applied Mechanics, Computer and Information Science, and Electrical and Systems Engineering at the University of Pennsylvania. Kumar's group works on creating autonomous ground and aerial robots, designing bio-inspired algorithms for collective behaviors, and on robot swarms. They have won many best paper awards at conferences, and group alumni are leaders in teaching, research, business and entrepreneurship. Kumar is a fellow of ASME and IEEE and a member of the National Academy of Engineering.

Future research has found that Cybersecurity is directed at human behavioral and emotional factors. Out of this discovery, questions arise. Why do people really commit cybercrime? Is it out of greed, power, negligence, fear, pressure? What are the motives and intentions of these acts?

Everything starts with a good business case that aligns with urban strategy. Business cases should start with the needs of the citizen or the business.

The novel coronavirus that first appeared in mainland China has now spread across the world, with more than 82,000 reported cases and nearly 3,000 deaths, as of Thursday. And right alongside the outbreak is the deployment of myriad types of AI-powered tech that is now being put on full display. New technology like infrared thermometers -- potentially unreliable devices known as "thermometer guns" -- are becoming increasingly commonplace in China, where health workers regularly check people's temperatures. Somewhat behind the scenes, however, more futuristic technology powered by artificial intelligence is helping to identify coronavirus symptoms, find new treatments, and track the spread of the disease. Meanwhile, robots are making interactions with and treatment of sick patients easier. Powerful surveillance tech -- including facial recognition-enabled cameras and drones -- is also helping find people who might be sick or who aren't wearing masks.

The world of artificial intelligence is constantly evolving, and certainly so is the legal and regulatory environment in which it exists. Michael Hayes, Senior Manager of Government Affairs at the Consumer Technology Association (CTA) is focused on these emerging technology challenges that hit up against existing laws and regulations. Michael previously worked on Capitol Hill on patent reform, stopping patent trolls. As part of his current role, Michael makes sure that the emerging policy discussion is framed in a way that makes sure that the technology can thrive and provide competitive advantages for companies implementing them without introducing new risks. There has been a lot of concern about corporation and government's use of data, and the role of privacy.

The Department of Defense has officially adopted principles for the ethical use of artificial intelligence with a focus on ensuring the military can retain full control and understanding over how machines make decisions, it announced Monday. The final principles are largely unchanged from the Defense Innovation Board's recommendations submitted to Secretary of Defense Mark Esper in October. The DOD made some tweaks to meet the department's legal standards and changed a few "shoulds" to "wills," Lt. Gen. Jack Shanahan, the director of the Joint AI Center, said at a press conference Monday. "We believe the nation that successfully implements AI principles will lead in AI for many years," Shanahan said. Adopting AI has been one of Esper's top technological priorities since before his confirmation.

Artificial intelligence has transformed pretty much every industry in which it's been embraced, including healthcare, the stock markets, and, increasingly, cybersecurity, where it's being utilized to both enhance human work and strengthen defenses.

There is a lot of excitement around AI for SecOps. From a market perspective, AI in cybersecurity is projected to grow by a CAGR of 23.3% between 2019 and 2026 to exceed $38B. On the physical security front, AI- powered video analytics market driven primarily by security and safety, is projected to grow by a CAGR of 22.3% between 2018 and 2025 to reach $4.5B2. From a value perspective, securityintelligence.com has an insightful article titled - Artificial Intelligence (AI) and Security: A Match Made in the SOC – where it says " In summary, when security analysts partner with artificial intelligence, the benefits include streamlined threat detection, investigation and response processes, increased productivity, and improved job satisfaction -- analysts spend more time doing what they enjoy most and the cost of security breaches decreases." It is a well-known fact that the talent war is real in security operations.

Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then the field has rapidly progressed congruently with the wide adoption of machine learning (ML) in the environmental sciences. Here, we present a scoping review of ML in wildfire science and management. Our objective is to improve awareness of ML among wildfire scientists and managers, as well as illustrate the challenging range of problems in wildfire science available to data scientists. We first present an overview of popular ML approaches used in wildfire science to date, and then review their use in wildfire science within six problem domains: 1) fuels characterization, fire detection, and mapping; 2) fire weather and climate change; 3) fire occurrence, susceptibility, and risk; 4) fire behavior prediction; 5) fire effects; and 6) fire management. We also discuss the advantages and limitations of various ML approaches and identify opportunities for future advances in wildfire science and management within a data science context. We identified 298 relevant publications, where the most frequently used ML methods included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. There exists opportunities to apply more current ML methods (e.g., deep learning and agent based learning) in wildfire science. However, despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods requires sophisticated knowledge for their application. Finally, we stress that the wildfire research and management community plays an active role in providing relevant, high quality data for use by practitioners of ML methods.

Explainability and interpretability of AI models is an essential factor affecting the safety of AI. While various explainable AI (XAI) approaches aim at mitigating the lack of transparency in deep networks, the evidence of the effectiveness of these approaches in improving usability, trust, and understanding of AI systems are still missing. We evaluate multimodal explanations in the setting of a Visual Question Answering (VQA) task, by asking users to predict the response accuracy of a VQA agent with and without explanations. We use between-subjects and within-subjects experiments to probe explanation effectiveness in terms of improving user prediction accuracy, confidence, and reliance, among other factors. The results indicate that the explanations help improve human prediction accuracy, especially in trials when the VQA system's answer is inaccurate. Furthermore, we introduce active attention, a novel method for evaluating causal attentional effects through intervention by editing attention maps. User explanation ratings are strongly correlated with human prediction accuracy and suggest the efficacy of these explanations in human-machine AI collaboration tasks.