Inspired by findings of sensorimotor coupling in humans and animals, there has recently been a growing interest in the interaction between action and perception in robotic systems [Bogh et al., 2016]. Here we consider perception and action as two serial information channels with limited information-processing capacity. We follow [Genewein et al., 2015] and formulate a constrained optimization problem that maximizes utility under limited information-processing capacity in the two channels. As a solution we obtain an optimal perceptual channel and an optimal action channel that are coupled such that perceptual information is optimized with respect to downstream processing in the action module. The main novelty of this study is that we propose an online optimization procedure to find bounded-optimal perception and action channels in parameterized serial perception-action systems. In particular, we implement the perceptual channel as a multi-layer neural network and the action channel as a multinomial distribution. We illustrate our method in a NAO robot simulator with a simplified cup lifting task.

Efficient network management is one of the key challenges of the constantly growing and increasingly complex wide area networks (WAN). The paradigm shift towards virtualized (NFV) and software defined networks (SDN) in the next generation of mobile networks (5G), as well as the latest scientific insights in the field of Artificial Intelligence (AI) enable the transition from manually managed networks nowadays to fully autonomic and dynamic self-organized networks (SON). This helps to meet the KPIs and reduce at the same time operational costs (OPEX). In this paper, an AI driven concept is presented for the malfunction detection in NFV applications with the help of semi-supervised learning. For this purpose, a profile of the application under test is created. This profile then is used as a reference to detect abnormal behaviour. For example, if there is a bug in the updated version of the app, it is now possible to react autonomously and roll-back the NFV app to a previous version in order to avoid network outages.

Demand is growing for more accountability in the technological systems that increasingly occupy our world. However, the complexity of many of these systems - often systems of systems - poses accountability challenges. This is because the details and nature of the data flows that interconnect and drive systems, which often occur across technical and organisational boundaries, tend to be opaque. This paper argues that data provenance methods show much promise as a technical means for increasing the transparency of these interconnected systems. Given concerns with the ever-increasing levels of automated and algorithmic decision-making, we make the case for decision provenance. This involves exposing the 'decision pipeline' by tracking the chain of inputs to, and flow-on effects from, the decisions and actions taken within these systems. This paper proposes decision provenance as a means to assist in raising levels of accountability, discusses relevant legal conceptions, and indicates some practical considerations for moving forward.

A key aspect of a sustainable urban transportation system is the effectiveness of transportation policies. To be effective, a policy has to consider a broad range of elements, such as pollution emission, traffic flow, and human mobility. Due to the complexity and variability of these elements in the urban area, to produce effective policies remains a very challenging task. With the introduction of the smart city paradigm, a widely available amount of data can be generated in the urban spaces. Such data can be a fundamental source of knowledge to improve policies because they can reflect the sustainability issues underlying the city. In this context, we propose an approach to exploit urban positioning data based on stigmergy, a bio-inspired mechanism providing scalar and temporal aggregation of samples. By employing stigmergy, samples in proximity with each other are aggregated into a functional structure called trail. The trail summarizes relevant dynamics in data and allows matching them, providing a measure of their similarity. Moreover, this mechanism can be specialized to unfold specific dynamics. Specifically, we identify high-density urban areas (i.e hotspots), analyze their activity over time, and unfold anomalies. Moreover, by matching activity patterns, a continuous measure of the dissimilarity with respect to the typical activity pattern is provided. This measure can be used by policy makers to evaluate the effect of policies and change them dynamically. As a case study, we analyze taxi trip data gathered in Manhattan from 2013 to 2015.

This new Chair, funded by Google France, aims to support the training of a new generation of talents in artificial intelligence in terms of training, research, and international outreach. The new Artificial Intelligence and Advanced Visual Computing Master's program of École Polytechnique, offered in partnership with Inria, ENSTA ParisTech, and Télécom ParisTech, will benefit from support as it welcomes its first class in September 2018. Google France will offer students opportunities in its research internship programs. AI awareness seminars in ethics and law, as well as roundtables and workshops, will also be conducted to allow students to succeed in the professional world and in scientific research in the field of AI. An invitation program for world-renowned professors will also be funded by the Chair to support these research activities and to enrich the academic offer of the Artificial Intelligence and the Advanced Visual Computing Master's program.

Every 30 minutes a stroke patient who could have been saved dies or is permanently disabled, in many cases because they were treated in the wrong hospital. In Europe, only about a third of stroke patients have access to the organised stroke care they need to survive a stroke with minimal or no disability. Stroke treatment is particularly time-sensitive. The so-called door-to-therapy (D2T) time describes the time interval between a patient arriving at the hospital and the initiation of their therapy. D2T time is critical for patient outcomes: on average, each single minute saved adds two days of healthy life and every 15 minutes saved adds one extra month of disability-free life.

STOCKHOLM, SWEDEN – During the last century, the world shifted from manual labour to a situation where manufacturing is almost completely automated. Quality control by visual inspection is a task people are quite good at – at least, for a short period of time. It is difficult for people to stay focused for very long, and suddenly they drift off thinking of what to do in the weekend. Computers are quite the opposite. They never take a break, and can focus on the exact same task 24/7 without ever drifting away.

Is the world ready for cows armed with artificial intelligence? No time to ruminate on that because the moment has arrived, thanks to a Dutch company that has married two technologies -- motion sensors and AI -- with the aim of bringing the barnyard into the 21st century. The company, Connecterra, has brought its IDA system, or "The Intelligent Dairy Farmer's Assistant," to the United States after having piloted it in Europe for several years. IDA uses a motion-sensing device attached to a cow's neck to transmit its movements to a program driven by AI. The sensor data, when aligned repeatedly with real-world behavior, eventually allows IDA to tell from data alone when a cow is chewing cud, lying down, walking, drinking or eating.

The University of Agder invites applications for a full-time fixed-term position for a period of three years as PhD Research Fellow. The position is linked to the Department of Information and Communication Technology (ICT), and is located in Grimstad, Norway. The starting date is as soon as possible or by agreement. The Department of ICT hosts three research groups including nine Professors, 20 Associate Professors/Assistant professors and about 20 Research Fellows, pursuing a variety of research areas such as artificial intelligence (AI), wireless communications, network administration, and embedded systems. This open position is associated with Centre for Artificial Intelligence Research (CAIR) and targets development of a Chatbot based Personal Teaching Assistant (educator).

We are living in the age of Big Data, but data is useless if we do not have algorithms that help us to interpret it. Algorithms are increasingly used in both the public and the private sectors; across industrial sectors for financial trading, recruiting decisions (hiring, firing, and promotions), and for setting insurance premiums. Algorithms help decide whether individuals are desirable candidates for insurance, eligible for a loan or a mortgage, or should be admitted to university. The criminal justice system uses algorithms for sentencing or to decide if someone should be granted parole and to calculate the probability whether someone will commit a crime. Algorithms can – if well-designed and fed unbiased data – make more accurate, efficient, and fairer decisions than humans.