We present a system that enables real-time interaction between human users and agents trained to control fighter jets in simulated 3D air combat scenarios. The agents are trained in a dedicated environment using Multi-Agent Reinforcement Learning. A communication link is developed to allow seamless deployment of trained agents into VR-Forces, a widely used defense simulation tool for realistic tactical scenarios. This integration allows mixed simulations where human-controlled entities engage with intelligent agents exhibiting distinct combat behaviors. Our interaction model creates new opportunities for human-agent teaming, immersive training, and the exploration of innovative tactics in defense contexts.

Despite advances in AI's performance and interpretability, AI advisors can undermine experts' decisions and increase the time and effort experts must invest to make decisions. Consequently, AI systems deployed in high-stakes settings often fail to consistently add value across contexts and can even diminish the value that experts alone provide. Beyond harm in specific domains, such outcomes impede progress in research and practice, underscoring the need to understand when and why different AI advisors add or diminish value. To bridge this gap, we stress the importance of assessing the value AI advice brings to real-world contexts when designing and evaluating AI advisors. Building on this perspective, we characterize key pillars -- pathways through which AI advice impacts value -- and develop a framework that incorporates these pillars to create reliable, personalized, and value-adding advisors. Our results highlight the need for system-level, value-driven development of AI advisors that advise selectively, are tailored to experts' unique behaviors, and are optimized for context-specific trade-offs between decision improvements and advising costs. They also reveal how the lack of inclusion of these pillars in the design of AI advising systems may be contributing to the failures observed in practical applications.

Engagement is a key factor in every social interaction, be it between humans or humans and robots. Many studies were aimed at designing robot behavior in order to sustain human engagement. Infants and children, however, learn how to engage their caregivers to receive more attention.We used a social robot platform, DragonBot, that learned which of its social behaviors retained human engagement. This was achieved by implementing a reinforcement learning algorithm, wherein the reward is the proximity and number of people near the robot. The experiment was run in the World Science Festival in New York, where hundreds of people interacted with the robot. After more than two continuous hours of interaction, the robot learned by itself that making a sad face was the most rewarding expression. Further analysis showed that after a sad face, people's engagement rose for thirty seconds. In other words, the robot learned by itself in two hours that almost no-one leaves a sad DragonBot.

For creators, brand marketers, and business owners, producing content is only half the journey. To ensure that the videos serve their intended purpose, video-makers must also consider several other factors--testing, targeting, scheduling, and distribution, among other things. One particularly important step that's usually foregone by smaller creators and businesses is testing. This is usually due to the limited, expensive, and time-consuming nature of traditional market research. In this article, we'll demonstrate how Aifilia's technology can provide anyone working with video content an efficient and proven means to test their creations.

Auto accidents kill more than 33,000 Americans each year, more than homicide or prescription drug overdoses. Companies working on self-driving cars, such as Alphabet and Ford, say their technology can slash that number by removing human liabilities such as texting, drunkenness, and fatigue. But Christopher Hart, chairman of the National Transportation Safety Board, says his agency's experience investigating accidents involving autopilot systems used in trains and planes suggests that humans can't be fully removed from control. He told MIT Technology Review that future autos will be much safer, but that they will still need humans as copilots. What follows is a condensed transcript.

It represents a particular pure quantum state of a specific isolated system of one or more particles. By choosing a specific system of coordinates, e.g.

Less than a day after Microsoft launched its new artificial intelligence bot Tay, she has already learned the most important lesson of the internet: Never tweet. Microsoft reportedly had to suspend Tay from tweeting after she tweeted a series of racist statements, including "Hitler was right I hate the jews." The company had launched the AI on Wednesday, which was designed to communicate with "18 to 24 year olds in the U.S" and "experiment with and conduct research on conversational understanding." It appears some of her racist replies were simply regurgitating the statements trolls tweeted at her. Tay also apparently went from "i love feminism now" to "i fucking hate feminists they should all die and burn in hell" within hours. Zoe Quinn, a target of online harassment campaign Gamergate, shared a screengrab from the bot calling her a "Stupid Whore," saying, "this is the problem with content-neutral algorithms."

Engagement is a key factor in every social interaction, be it between humans or humans and robots. Many studies were aimed at designing robot behavior in order to sustain human engagement. Infants and children, however, learn how to engage their caregivers to receive more attention.We used a social robot platform, DragonBot, that learned which of its social behaviors retained human engagement. This was achieved by implementing a reinforcement learning algorithm, wherein the reward is the proximity and number of people near the robot. The experiment was run in the World Science Festival in New York, where hundreds of people interacted with the robot. After more than two continuous hours of interaction, the robot learned by itself that making a sad face was the most rewarding expression. Further analysis showed that after a sad face, people's engagement rose for thirty seconds. In other words, the robot learned by itself in two hours that almost no-one leaves a sad DragonBot.