NASA has acquired more than 22 million images from the planet Mars. To help users find images of interest, we developed a content-based search capability for Mars rover surface images and Mars orbital images. We started with the AlexNet convolutional neural network, which was trained on Earth images, and used transfer learning to adapt the network for use with Mars images. We report on our deployment of these classifiers within the PDS Imaging Atlas, a publicly accessible web interface, to enable the first content-based image search for NASA’s Mars images.

The Association for the Advancement of Artificial Intelligence was pleased to present the 2013 Fall Symposium Series, held Friday through Sunday, November 15–17, at the Westin Arlington Gateway in Arlington, Virginia near Washington DC USA. The titles of the five symposia were as follows: Discovery Informatics: AI Takes a Science-Centered View on Big Data (FS-13-01); How Should Intelligence be Abstracted in AI Research: MDPs, Symbolic Representations, Artificial Neural Networks, or --? The highlights of each symposium are presented in this report.

Rinke Hoekstra (VU University from transferring and adapting semantic web Amsterdam) presented linked open data tools technologies to the big data quest. Finally, in the Social to discover connections within established scientific Networks and Social Contagion symposium, a data sets. Louiqa Rashid (University of Maryland) community of researchers explored topics such as social presented work on similarity metrics linking together contagion, game theory, network modeling, network-based drugs, genes, and diseases. Kyle Ambert (Intel) presented inference, human data elicitation, and Finna, a text-mining system to identify passages web analytics. Highlights of the symposia are contained of interest containing descriptions of neuronal in this report.

To assist scientists in data analysis tasks, we have developed semantic workflow representations that support automatic constraint propagation and reasoning algorithms to manage constraints among the individual workflow steps. Semantic constraints can be used to represent requirements of input datasets as well as best practices for the method represented in a workflow. We demonstrate how the Wings workflow system uses semantic workflows to assist users in creating workflows while validating that the workflows comply with the requirements of the software components and datasets. Wings reasons over semantic workflow representations that consist of both a traditional dataflow graph as well as a network of constraints on the data and components of the workflow.

We present a formal approach for combining programming by demonstration (PbD) with programming by instruction (PbI) — a largely unsolved problem. Our solution is based on the integration of two successful formalisms: version space algebras and the logic programming language Golog. Version space algebras have been successfully applied to programming by demonstration. Intuitively, a version space describes a set of candidate procedures and a learner filters this space as necessary to be consistent with all given demonstrations of the target procedure. Golog, on the other hand, is a logical programming language defined in the situation calculus that allows for the specification of non-deterministic programs. While Golog was originally proposed as a means for integrating programming and automated planning, we show that it serves equally well as a formal framework for integrating PbD and PbI. Our approach is the result of two key insights: (a) Golog programs can be used to define version spaces, and (b) with only a minor augmentation, the existing Golog semantics readily provides the update-function for such version spaces, given demonstrations. Moreover, as we will show, two or more programs can be symbolically synchronized, resulting in the intersection of two, possibly infinite, version spaces. The framework thus allows for a rather flexible integration of PbD and PbI, and in addition establishes a new connection between two active research areas, enabling cross-fertilization.

While there is a plethora of mechanisms to ensure lawful access to privacy-protected data, additional research is required in order to reassure individuals that their personal data is being used for the purpose that they consented to. This is particularly important in the context of new data mining approaches, as used, for instance, in biomedical research and commercial data mining. We argue for the use of computational workflows to ensure and enforce appropriate use of sensitive personal data. Computational workflows describe in a declarative manner the data processing steps and the expected results of complex data analysis processes such as data mining (Gil et al. 2007b; Taylor et al. 2006). We see workflows as an artifact that captures, among other things, how data is being used and for what purpose. Existing frameworks for computational workflows need to be extended to incorporate privacy policies that can govern the use of data.

The evolution in health care to greater support for self-managed care is escalating the demand for e-health systems in which patients can access their personal health information in order to ultimately partner with providers in the management of their health and wellness care. At present, unfortunately, patients are seldom able to easily access their own health information so, as a result, it is often difficult for patients to enter into a dialogue with their healthcare providers about treatment and other options. One truism seems to be constantly ignored: it is not possible for patients to actively manage their health without the requisite information. Health information should be made available through "any time, anywhere" delivery: outside the physician's office or hospital, in the home or other personal setting, on a variety of multimedia information devices. We believe that personalization of health information will be a key element in effective self-managed healthcare.