We consider the problem of estimating the parameters of a linear univariate autoregressive model with sub-Gaussian innovations from a limited sequence of consecutive observations. Assuming that the parameters are compressible, we analyze the performance of the $\ell_1$-regularized least squares as well as a greedy estimator of the parameters and characterize the sampling trade-offs required for stable recovery in the non-asymptotic regime. In particular, we show that for a fixed sparsity level, stable recovery of AR parameters is possible when the number of samples scale sub-linearly with the AR order. Our results improve over existing sampling complexity requirements in AR estimation using the LASSO, when the sparsity level scales faster than the square root of the model order. We further derive sufficient conditions on the sparsity level that guarantee the minimax optimality of the $\ell_1$-regularized least squares estimate. Applying these techniques to simulated data as well as real-world datasets from crude oil prices and traffic speed data confirm our predicted theoretical performance gains in terms of estimation accuracy and model selection.

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.

(Turing 1950). In a written an expected way. Also, processing these signals and chat, the end of a turn is typically marked with the making use of them in a spoken dialogue system in return key, and voice assistants typically use a button real time is a nontrivial task. In this article, I will summarize or a key word (like Amazon's "Alexa") to initiate a some of the results from several studies done turn, and then a long pause to mark the end. Before discussing the challenges of real-time coordination Spoken interaction is typically coordinated on a in human-robot interaction, I will present the much finer level, and humans are very good at research platforms that we have developed at KTH: switching turns with very short gaps (around 200 ms) the robot head Furhat and the interaction framework and little overlap. Humans also give precisely timed IrisTK. I will also present two different application feedback in the middle of the interlocutor's speech in scenarios that we have developed, which pose different the form of very short utterances (so-called types of challenges when it comes to modeling backchannels, such as "mhm") or head nods. Another turn-taking, feedback, and joint attention in humanrobot notable property of everyday human interaction is interaction.

Radio signal recognition in dense and complex multi-user spectrum environments is an important tool for optimizing spectrum utilization, identifying and minimizing interference, enforcing spectrum policy, and implementing effective radio sensing and coordination systems. Classical approaches to the problem focus on energy detection and the use of expert features and decision criteria to identify and categorize specific modulation types [2] [1]. These approaches rely on prior knowledge of signal properties, features, and decision statistics to separate known modulations and are typically derived under simplified analytic hardware, propagation, radio environment models. We recently demonstrated the viability of naive feature learning for supervised radio classification systems [14] which allows for joint feature and classifier learning given labeled datasets and examples. In this case we were able to outperform traditional expert decision statistic based classification in sensitivity and accuracy by a significant margin. This was a powerful result, providing significant performance improvements against current day solutions, but it still relied entirely on supervised learning and well curated training data. In the real world, and especially in the radio domain, we are faced with vast amounts of unlabeled example data available to our sensor and incomplete knowledge of class labels comprising ground truth. To address this problem we investigate alternative strategies for radio identification learning which rely less heavily on labeled training data and are capable of making sense of radio signals with either no or less labeled examples, potentially drastically reducing the burden of data curation on such a machine learning system for developers and maintainers, and allowing systems to recognize new signals and scale to to understand new environments over time.

This page includes forthcoming AAAI sponsored conferences, conferences presented by AAAI Affiliates, and conferences held in cooperation with AAAI. AI Magazine also maintains a calendar listing that includes nonaffiliated conferences at www.aaai.org/Magazine/calendar.php. ICAIL 2017 will be held 12-16 June, Twenty-Ninth Innovative Applications Thirtieth International Florida AI 2017 in London, UK of Artificial Intelligence Conference. IAAI-17 will be held February FLAIRS-2017 will be held May 22-24, 4-9 in San Francisco, California USA. IEA/AIE-2017 will be AAAI 2017 Spring Symposium Series on Automated Planning and Scheduling.

Embedding making, political analysis, and intelligence analysis; morality when handling preferences and dealing models of biomedical argumentation in research journals with the potential and risks of big data were identified and popular media; annotation of rhetorical figures; as challenging endeavors for the future.

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.

To enable situated human-robot dialogue, techniques to support grounded language communication are essential. One particular challenge is to ground human language to robot internal representation of the physical world. Although copresent in a shared environment, humans and robots have mismatched capabilities in reasoning, perception, and action. Their representations of the shared environment and joint tasks are significantly misaligned. Humans and robots will need to make extra effort to bridge the gap and strive for a common ground of the shared world. Only then, is the robot able to engage in language communication and joint tasks. Thus computational models for language grounding will need to take collaboration into consideration. A robot not only needs to incorporate collaborative effort from human partners to better connect human language to its own representation, but also needs to make extra collaborative effort to communicate its representation in language that humans can understand. To address these issues, the Language and Interaction Research group (LAIR) at Michigan State University has investigated multiple aspects of collaborative language grounding. This article gives a brief introduction to this research effort and discusses several collaborative approaches to grounding language to perception and action.

During the last 36 years of its illustrious history, ince its inception in 1980, AI Magazine has played an the magazine has gone through several transformations. Now the magazine is going through another transition: David Leake, the longtime editor-in-chief is moving on after 17 years of distinguished service, though fortunately he will continue to advise us as editor emeritus. I am honored and delighted to follow David. I have been a member of the Editorial Board of AI Magazine for several years, associate editor since August 2015, and editor elect since February 2016; my tenure as editor-in-chief starts with this winter 2016 issue. I thank David, Managing Editor Mike Hamilton, former AAAI President Tom Dietterich, and AAAI for recruiting me for this challenge....

Gaussian processes (GPs) have been proven to be powerful tools in various areas of machine learning. However, there are very few applications of GPs in the scenario of multi-view learning. In this paper, we present a new GP model for multi-view learning. Unlike existing methods, it combines multiple views by regularizing marginal likelihood with the consistency among the posterior distributions of latent functions from different views. Moreover, we give a general point selection scheme for multi-view learning and improve the proposed model by this criterion. Experimental results on multiple real world data sets have verified the effectiveness of the proposed model and witnessed the performance improvement through employing this novel point selection scheme.