What Mr.Trump can do to protect jobs from AI and Robotics VISIT BORINGPORTAL.com

The other day I sat in a coffee shop across from a student who was studying a complex math problem. The equation took up the entire page, and he sat there staring at it for quite awhile. After I thought about how glad I am to not be in school anymore, I started thinking about how jumping into a new investing market can be intimidating. Like the math equation, it can be hard to make sense of all the information on the page. But it doesn't have to be this way, especially if you closely follow what smart companies are doing in new segments and pair that with some good ol' fashioned research.

The US Navy has revealed plans for a radical new smart'mini missile' that can be fired from warships to take out swarms of enemy drones and boats. Known as the Multi Azimuth Defense Fast Intercept Round Engagement System (MAD-FIRES) program, it will develop a'medium-caliber guided projectile'. DARPA says this will'combine the guidance, precision, and accuracy of missiles with the speed, rapid-fire capability, and large ammunition capacity of medium-caliber bullets like 20-to-40-caliber ammunition designed to destroy lightly armored vehicles, aircraft, and personnel.' Navy bosses say they need the new mini missile to deal with the increasing risk of'swarm' attacks, and hope with fit it to warships such at the USS Enterprise (pictured) The MAD FIRES will be enhanced ammunition rounds able to alter their flight path in real time to stay on target. They will be able to continuously target, track and engage multiple fast-approaching targets simultaneously and re-engage any targets that survive initial engagement.

This issue of AI Magazine brings together a collection of articles on challenges, mechanisms, and research progress in turn-taking and coordination between humans and machines. The contributing authors work in interrelated fields of spoken dialog systems, intelligent virtual agents, human-computer interaction, human-robot interaction, and semiautonomous collaborative systems and explore core concepts in coordinating speech and actions with virtual agents, robots, and other autonomous systems. Several of the contributors participated in the AAAI Spring Symposium on Turn-Taking and Coordination in Human-Machine Interaction, held in March 2015, and several articles in this issue are extensions of work presented at that symposium. The articles in the collection address key modeling, methodological, and computational challenges in achieving effective coordination with machines, propose solutions that overcome these challenges under sensory, cognitive, and resource restrictions, and illustrate how such solutions can facilitate coordination across diverse and challenging domains. The contributions highlight turn-taking and coordination in human-machine interaction as an emerging and evolving research area with important implications for future applications of AI.

Although the reception is especially "meet-and-greet" atmosphere.

As NASA explores destinations beyond the Moon, the distance between Earth and spacecraft will increase communication delays between astronauts and Mission Control. Today, astronauts coordinate with Mission Control to request assistance and await approval to perform tasks. Many of these coordination tasks require multiple exchanges of information, (for example, taking turns). In the presence of long communication delays, the length of time between turns may lead to inefficiency, or increased mission risk. Future astronauts will need software-based decision aids to enable them to work autonomously from Mission Control. These tools require the right combination of mission operations functions, for example, automated planning and fault management, troubleshooting recommendations, easy to access information, and just-in-time training. Ensuring these elements are properly designed and integrated requires an integrated human factors approach. This article describes a recent demonstration of autonomous mission operations using a novel software-based decision aid onboard the International Space Station. We describe how this new technology changes the way astronauts coordinate with mission control, and how the lessons learned from these early demonstrations will enable the operational autonomy needed to ensure astronauts can safely journey to Mars, and beyond.

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, presented the 2016 Spring Symposium Series on Monday through Wednesday, March 21-23, 2016 at Stanford University. The titles of the seven symposia were (1) AI and the Mitigation of Human Error: Anomalies, Team Metrics and Thermodynamics; (2) Challenges and Opportunities in Multiagent Learning for the Real World (3) Enabling Computing Research in Socially Intelligent Human-Robot Interaction: A Community-Driven Modular Research Platform; (4) Ethical and Moral Considerations in Non-Human Agents; (5) Intelligent Systems for Supporting Distributed Human Teamwork; (6) Observational Studies through Social Media and Other Human-Generated Content, and (7) Well-Being Computing: AI Meets Health and Happiness Science.

Research systems are providing a vision of what is possible. However much work remains before such abilities are robust, widely useful, and generally available. This article identifies 10 key challenges, relating to modeling, systems architecture, and development methods. Of pressing importance for dialogue systems, these challenges are also relevant for intelligent and interactive systems more generally. Given Siri's broad deployment and popular example in science fiction movies. However, tellingly, salience, one might imagine that it solved the problems such systems are portrayed as idiot savants: knowledgeable, of interacting in dialogue: we often meet people logical, and well-spoken, but unable to who are unaware how cleverly Siri and her sisters interact smoothly with humans. We find it provocative avoid dialogue.

Despite significant progress made over the past twenty five years, unconstrained face verification remains a challenging problem. This paper proposes an approach that couples a deep CNN-based approach with a low-dimensional discriminative embedding learned using triplet probability constraints to solve the unconstrained face verification problem. Aside from yielding performance improvements, this embedding provides significant advantages in terms of memory and for post-processing operations like subject specific clustering. Experiments on the challenging IJB-A dataset show that the proposed algorithm performs comparably or better than the state of the art methods in verification and identification metrics, while requiring much less training data and training time. The superior performance of the proposed method on the CFP dataset shows that the representation learned by our deep CNN is robust to extreme pose variation. Furthermore, we demonstrate the robustness of the deep features to challenges including age, pose, blur and clutter by performing simple clustering experiments on both IJB-A and LFW datasets.

For a social networking service to acquire and retain users, it must find ways to keep them engaged. By accurately gauging their preferences, it is able to serve them with the subset of available content that maximises revenue for the site. Without the constraints of an appropriate regulatory framework, we argue that a sufficiently sophisticated curator algorithm tasked with performing this process may choose to explore curation strategies that are detrimental to users. In particular, we suggest that such an algorithm is capable of learning to manipulate its users, for several qualitative reasons: 1. Access to vast quantities of user data combined with ongoing breakthroughs in the field of machine learning are leading to powerful but uninterpretable strategies for decision making at scale. 2. The availability of an effective feedback mechanism for assessing the short and long term user responses to curation strategies. 3. Techniques from reinforcement learning have allowed machines to learn automated and highly successful strategies at an abstract level, often resulting in non-intuitive yet nonetheless highly appropriate action selection. In this work, we consider the form that these strategies for user manipulation might take and scrutinise the role that regulation should play in the design of such systems.