A Priest

The paper addresses the question of Informed Virtual Environments from the point of view of some philosophical and ethical key-issues. By browsing some eclectic experiences like dancing, serving Asturian cider, trying to escape Dante’s Hell or manufacturing a tyre, important questions are raised, and some answers are sketched about some possible ways of designing our future environments: but gestures have then to be considered as cultural objects too, being not reducible to their physical traces

AI and IP: Theory to Policy and Back Again – Policy and Research Recommendations at the Intersection of Artificial Intelligence and Intellectual Property

The interaction between artificial intelligence and intellectual property rights (IPRs) is one of the key areas of development in intellectual property law. After much, albeit selective, debate, it seems to be gaining increasing practical relevance through intense AI-related market activity, an initial set of case law on the matter, and policy initiatives by international organizations and lawmakers. Against this background, Zurich University’s Center for Intellectual Property and Competition Law is conducting, together with the Swiss Intellectual Property Institute, a research and policy project that explores the future of intellectual property law in an AI context. This paper briefly describes the AI/IP Research Project and presents an initial set of policy recommendations for the development of IP law with a view to AI. The recommendations address topics such as AI inventorship in patent law; AI authorship in copyright law; the need for sui generis rights to protect innovative AI output; rules for the allocation of AI-related IPRs; IP protection carve-outs in order to facilitate AI system development, training, and testing; the use of AI tools by IP offices; and suitable software protection and data usage regimes

A Touching Agent : Integrating Touch in Social Interactions between Human and Embodied Conversational Agent in an Immersive Environment

International audienceEmbodied conversational agents (ECAs) are endowed with more and more social and emotional capabilities. They can build rapport with humans by expressing thoughts and emotions through verbal and non-verbal behaviors. However, ECAs often lack the sense of touch, which is an important communicative and emotional capability, in particular in the context of an immersive environment. The sense of touch has been shown to be essential to the social development and general well-being of human beings, from their infancy to adulthood. It is thought to be a facilitator in the establishment of relationships and it is overall considered a very important medium of communication, especially for the communication of emotions. In many languages being touched designates not only physical contact but also being moved by something, feeling something on an emotional level. While in adulthood touch is usually kept for the closest relationships, it is nonetheless present in our daily lives (in greetings for example). We therefore believe that adding touch to the social capabilities of artificial agents would contribute to their ability to build rapport and emotional connections with humans. But to which extent would granting an ECA the ability to touch and be touched enhance its ability to communicate emotions and to build and maintain a social and emotional relationship with a human? To investigate this interrogation, our work focuses on the development of a touching agent: touching in the physical sense as well as the emotional sense. In the context of an immersive environment, our autonomous virtual ECA should be able to perceive and interpret touch, as well as decide how and when to perform it based on the overall interaction and emotional state of its human partner. Our first results are so far promising as to the credibility of such a touching agent

A Distributed Block-Split Gibbs Sampler with Hypergraph Structure for High-Dimensional Inverse Problems

Sampling-based algorithms are classical approaches to perform Bayesian inference in inverse problems. They provide estimators with the associated credibility intervals to quantify the uncertainty on the estimators. Although these methods hardly scale to high dimensional problems, they have recently been paired with optimization techniques, such as proximal and splitting approaches, to address this issue. Such approaches pave the way to distributed samplers, splitting computations to make inference more scalable and faster. We introduce a distributed Split Gibbs sampler (SGS) to efficiently solve such problems involving distributions with multiple smooth and non-smooth functions composed with linear operators. The proposed approach leverages a recent approximate augmentation technique reminiscent of primal-dual optimization methods. It is further combined with a block-coordinate approach to split the primal and dual variables into blocks, leading to a distributed block-coordinate SGS. The resulting algorithm exploits the hypergraph structure of the involved linear operators to efficiently distribute the variables over multiple workers under controlled communication costs. It accommodates several distributed architectures, such as the Single Program Multiple Data and client-server architectures. Experiments on a large image deblurring problem show the performance of the proposed approach to produce high quality estimates with credibility intervals in a small amount of time. Codes to reproduce the experiments are available online

Estimation de variabilité pour le démélange non-supervisé d'images hyperspectrales

International audienceLe démélange d’images hyperspectrales vise à identifier les signatures spectrales d’un milieu imagé ainsi que leurs proportions dans chacun des pixels. Toutefois, les signatures extraites présentent en pratique une variabilité qui peut compromettre la fiabilité de cette identification. En supposant ces signatures potentiellement affectées par le phénomène de variabilité, nous proposons d’estimer les paramètres d’un modèle de mélange linéaire à l’aide d’un algorithme de minimisation alternée (Proximal alternating linearized minimization, PALM) dont la convergence a été démontrée pour une classe de problèmes non-convexes qui contient précisément le problème du démélange d’images hyperspectrales. La méthode proposée est évaluée sur des données synthétiques et réelles

Combining Precision Boosting with LP Iterative Refinement for Exact Linear Optimization

This article studies a combination of the two state-of-the-art algorithms for the exact solution of linear programs (LPs) over the rational numbers, i.e., without any roundoff errors or numerical tolerances. By integrating the method of precision boosting inside an LP iterative refinement loop, the combined algorithm is able to leverage the strengths of both methods: the speed of LP iterative refinement, in particular in the majority of cases when a double-precision floating-point solver is able to compute approximate solutions with small errors, and the robustness of precision boosting whenever extended levels of precision become necessary. We compare the practical performance of the resulting algorithm with both puremethods on a large set of LPs and mixed-integer programs (MIPs). The results show that the combined algorithm solves more instances than a pure LP iterative refinement approach, while being faster than pure precision boosting. When embedded in an exact branch-and-cut framework for MIPs, the combined algorithm is able to reduce the number of failed calls to the exact LP solver to zero, while maintaining the speed of the pure LP iterative refinement approach

Social Touch in Human-agent Interactions in an Immersive Virtual Environment

International audienceWorks on artificial social agents, and especially embodied conversational agents, have endowed them with social-emotional capabilities. They are being given the abilities to take into account more and more modalities to express their thoughts, such as speech, gestures, facial expressions, etc. However, the sense of touch, although particularly interesting for social and emotional communication, is still a modality widely missing from interactions between humans and agents. We believe that integrating touch into those modalities of interaction between humans and agents would help enhancing their channels of empathic communication. In order to verify this idea, we present in this paper a system allowing tactile communication through haptic feedback on the hand and the arm of a human user. We then present a preliminary evaluation of the credibility of social touch in human-agent interaction in an immersive environment. The first results are promising and bring new leads to improve the way humans can interact through touch with virtual social agents

Hyperspectral unmixing with spectral variability using a perturbed linear mixing model

International audienceGiven a mixed hyperspectral data set, linear unmixing aims at estimating the reference spectral signatures composing the data-referred to as endmembers-their abundance fractions and their number. In practice, the identified endmembers can vary spectrally within a given image and can thus be construed as variable instances of reference endmembers. Ignoring this variability induces estimation errors that are propagated into the unmixing procedure. To address this issue, endmember variability estimation consists of estimating the reference spectral signatures from which the estimated endmembers have been derived as well as their variability with respect to these references. This paper introduces a new linear mixing model that explicitly accounts for spatial and spectral endmember variabilities. The parameters of this model can be estimated using an optimization algorithm based on the alternating direction method of multipliers. The performance of the proposed unmixing method is evaluated on synthetic and real data. A comparison with state-of-the-art algorithms designed to model and estimate endmember variability allows the interest of the proposed unmixing solution to be appreciated