I introduce an algorithm for estimating parameters from multidimensional data based on forward modelling. In contrast to many machine learning approaches it avoids fitting an inverse model and the problems associated with this. The algorithm makes explicit use of the sensitivities of the data to the parameters, with the goal of better treating parameters which only have a weak impact on the data. The forward modelling approach provides uncertainty (full covariance) estimates in the predicted parameters as well as a goodness-of-fit for observations. I demonstrate the algorithm, ILIUM, with the estimation of stellar astrophysical parameters (APs) from simulations of the low resolution spectrophotometry to be obtained by Gaia. The AP accuracy is competitive with that obtained by a support vector machine. For example, for zero extinction stars covering a wide range of metallicity, surface gravity and temperature, ILIUM can estimate Teff to an accuracy of 0.3% at G=15 and to 4% for (lower signal-to-noise ratio) spectra at G=20. [Fe/H] and logg can be estimated to accuracies of 0.1-0.4dex for stars with G<=18.5. If extinction varies a priori over a wide range (Av=0-10mag), then Teff and Av can be estimated quite accurately (3-4% and 0.1-0.2mag respectively at G=15), but there is a strong and ubiquitous degeneracy in these parameters which limits our ability to estimate either accurately at faint magnitudes. Using the forward model we can map these degeneracies (in advance), and thus provide a complete probability distribution over solutions. (Abridged)

Although AI and HCI explore computing and intelligent behavior and the fields have seen some cross-over, until recently there was not very much. This article outlines a history of the fields that identifies some of the forces that kept the fields at arm’s length. AI was generally marked by a very ambitious, long-term vision requiring expensive systems, although the term was rarely envisioned as being as long as it proved to be, whereas HCI focused more on innovation and improvement of widely-used hardware within a short time-scale. These differences led to different priorities, methods, and assessment approaches.  A consequence was competition for resources, with HCI flourishing in AI winters and moving more slowly when AI was in favor. The situation today is much more promising, in part because of platform convergence: AI can be exploited on widely-used systems.

These unintended negative consequences of the introduction of intelligence often have no direct relationship with the intended benefits, just as the adverse effects of a medication may bear no obvious relationship to the intended benefits of taking that medicine. Therefore, these negative consequences can be seen as side effects. The purpose of this article is to give designers, developers, and users of interactive intelligent systems a detailed awareness of the potential side effects of AI. As with medications, awareness of the side effects can have different implications: We may be relieved to see that a given side effect is unlikely to occur in our particular case. We may become convinced that it will inevitably occur and therefore decide not to "take the medicine" (that is, decide to stick with mainstream systems). Or most likely and most constructively, by looking carefully at the causes of the side effects and the conditions under which they can occur, we can figure out how to exploit the benefits of AI in interactive systems while avoiding the side effects.

This is an opinion piece about the relationship between the fields of human-computer interaction (HCI), and artificial intelligence (AI). The ultimate goal of both fields is to make user interfaces more effective and easier to use for people. But historically, they have disagreed about whether "intelligence" or "direct manipulation" is the better route to achieving this. There is an unjustified perception in HCI that AI is unreliable. There is an unjustified perception in AI that interfaces are merely cosmetic. This disagreement is counterproductive.This article argues that AI's goals of intelligent interfaces would benefit enormously by the user-centered design and testing principles of HCI. It argues that HCI's stated goals of meeting the needs of users and interacting in natural ways, would be best served by application of AI. Peace.

We report part of the design experience gained in X-Media, a system for knowledge management and sharing. Consolidated techniques of interaction design (scenario-based design) had to be revisited to capture the richness and complexity of intelligent interactive systems. We show that the design of intelligent systems requires methodologies (faceted scenarios) that support the investigation of intelligent features and usability factors simultaneously. Interaction designers become mediators between intelligent technology and users, and have to facilitate reciprocal understanding.

When creating algorithms or systems that are supposed to be used by people, we should be able to adopt a “binocular” view of users’ interaction with intelligent systems: a view that regards the design of interaction and the design of intelligent algorithms as interrelated parts of a single design problem. This special issue offers a coherent set of articles on two levels of generality that illustrate the binocular view and help readers to adopt it.

This case study article describes the iterative design process of an adaptive, mixed-initiative calendaring tool with embedded artificial intelligence.  We establish the specific types of assistance in which the target user population expressed interest, and we highlight our findings regarding the scheduling practices and the reminding preferences of these users.  These findings motivated the redesign and enhancement of our intelligent system.  Lessons learned from the study—namely, highlighting the merits of usability toward widespread adoption and retention, and that simple problems that perhaps do not necessitate complex AI-based solutions should not go unattended merely due to their inherent simplicity—conclude the article, along with a discussion of the importance of the iterative design process for any user adaptive system.

Interface personalization aims to streamline the process of working in a feature-rich application by providing the user with an adapted interface tailored specifically to his/her needs. The MICA (Mixed-Initiative Customization Assistance) system explores a middle ground between two opposing approaches to personalization: (1) an adaptable approach, where personalization is fully user controlled and (2) and adaptive approach, where personalization is fully system controlled. We overview MICA’s strategy for providing user-adaptive recommendations to help users decide how to personalize their interfaces. In doing so, we focus primarily on how MICA handles threats to usability that are often found in adaptive interfaces including obtrusiveness and lack of understandability and control. We also describe how we evaluated MICA and highlight results from these evaluations.

Funding for the conference was provided by the National Science Foundation, the Naval Research Laboratory, ABB, Microsoft Research, Microsoft Robotics, Evolution Robotics, Willow Garage, and Intel. Springer sponsored the best student paper award. The meeting brought together more than 280 researchers from Europe, Asia, North America, and Australia. He showed how molecular motors exploit for the technical program. Twenty of the accepted thermal noise to achieve energy efficiency and papers were presented orally; the remaining 20 talked about the implications for building artificial were presented as posters.

ICAART 2010 will be held January 22-24, 2010, in Valencia, Spain. This page includes forthcoming AAAI sponsored conferences, conferences presented International Conference on Intelligent by AAAI Affiliates, and conferences held in cooperation with AAAI. IUI 2010 will be Magazine also maintains a calendar listing that includes nonaffiliated conferences held February 7-10, 2010, in Hong at www.aaai.org/Magazine/calendar.php. The Twelfth International Conference The Third Conference on Artificial AAAI Spring Symposium Series will be on Principles of Knowledge Representation General Intelligence. AGI-08 will be held March 22-24, 2010 at Stanford and Reasoning.