Learning suitable and well-performing dialogue behaviour in statistical spoken dialogue systems has been in the focus of research for many years. While most work which is based on reinforcement learning employs an objective measure like task success for modelling the reward signal, we use a reward based on user satisfaction estimation. We propose a novel estimator and show that it outperforms all previous estimators while learning temporal dependencies implicitly. Furthermore, we apply this novel user satisfaction estimation model live in simulated experiments where the satisfaction estimation model is trained on one domain and applied in many other domains which cover a similar task. We show that applying this model results in higher estimated satisfaction, similar task success rates and a higher robustness to noise.

Clinical decision support systems combine knowledge and data from a variety of sources, represented by quantitative models based on stochastic methods, or qualitative based rather on expert heuristics and deductive reasoning. At the same time, case-based reasoning (CBR) memorizes and returns the experience of solving similar problems. The cooperation of heterogeneous clinical knowledge bases (knowledge objects, semantic distances, evaluation functions, logical rules, databases...) is based on medical ontologies. A multi-agent decision support system (MADSS) enables the integration and cooperation of agents specialized in different fields of knowledge (semiology, pharmacology, clinical cases, etc.). Each specialist agent operates a knowledge base defining the conduct to be maintained in conformity with the state of the art associated with an ontological basis that expresses the semantic relationships between the terms of the domain in question. Our approach is based on the specialization of agents adapted to the knowledge models used during the clinical steps and ontologies. This modular approach is suitable for the realization of MADSS in many areas.

What happens when the sensor-imbued city acquires the ability to see – almost as if it had eyes? Ahead of the 2019 Shenzhen Biennale of Urbanism\Architecture (UABB), titled "Urban Interactions," ArchDaily is working with the curators of the "Eyes of the City" section at the Biennial to stimulate a discussion on how new technologies – and Artificial Intelligence in particular – might impact architecture and urban life. Here you can read the "Eyes of the City" curatorial statement by Carlo Ratti, the Politecnico di Torino and SCUT. Technologies of the virtual realm present an opportunity to rethink the experience of space, society, and culture. They give us the possibility to engage with the city of the future, shaping the built environment of the 21st century.

The UAE continues to places great importance to protecting the environment and promoting a green economy, placing sustainability at the forefront of its strategic priorities. This is in line with the UAE Vision 2021, which aims to build a sustainable environment, and a diversified and sustainable competitive economy that ensures a secure future for generations to come. Under the guidance of its wise leadership, the UAE has made great progress towards sustainability, driven by significant achievements in the adoption of advanced technologies to create a new reality and to build a leading global model for sustainable development. The UAE has recognised the importance of Artificial Intelligence (AI) as the cornerstone for achieving sustainability goals, at a time when this advanced technology is expected to contribute to the growth of the country's GDP by 35% until 2031, while also reducing government expenditures by 50% annually, cutting down the number of paper transactions and saving millions of work hours annually. The aim of the UAE Strategy for Artificial Intelligence 2031 is to improve government performance, accelerate the pace of achievements, and to create innovative and productive work environments that ensure high levels of productivity.

The lack of diversity of the Artificial Intelligence (AI) field is nowadays a concern, and several initiatives such as funding schemes and mentoring programs have been designed to fight against it. However, there is no indication on how these initiatives actually impact AI diversity in the short and long term. This work studies the concept of diversity in this particular context and proposes a small set of diversity indicators (i.e. indexes) of AI scientific events. These indicators are designed to quantify the lack of diversity of the AI field and monitor its evolution. We consider diversity in terms of gender, geographical location and business (understood as the presence of academia versus industry). We compute these indicators for the different communities of a conference: authors, keynote speakers and organizing committee. From these components we compute a summarized diversity indicator for each AI event. We evaluate the proposed indexes for a set of recent major AI conferences and we discuss their values and limitations.

Are you sure you want to view these Tweets? Is it really that different than the West? BREAKING: Military to jam GPS signals across East Coast through Jan. 24th; FBI asserting imminent domain to seize… http://disq.us/t/3ldp0z3 What Will It Take to Get the Public to Embrace Sound Money? Human Rights Watch says China is trying to censor critics abroad http://cnb.cx/35UVs43

As urban populations grow, more people are exposed to the benefits and hazards of city life. One challenge for cities is managing the risk of disasters in a constantly changing built environment. Buildings, roads, and critical infrastructure need to be mapped frequently, accurately, and in enough detail to represent assets important to every community. Knowing where and how assets are vulnerable to damage or disruption by natural hazards is key to disaster risk management (DRM). The Global Facility for Disaster Reduction and Recovery (GFDRR) is a global partnership that provides knowledge, funding, and technical assistance towards achieving the vision of a world where resilient societies manage and adapt to ever-changing disaster and climate risk, and where the human and economic impact of disasters is reduced.

Video surveillance systems are evolving and are using artificial intelligence (AI) to inspect and analyse video footage, interpret patterns and flag unusual activity. Lenovo DCG and Pivot3 provide a state-of-the-art upgraded infrastructure solutions that aim to enhance current technology required to support these systems rather than entrusting the preservation of crucial data to outdated NVR technology. Commenting on the partnership, Dr. Chris Cooper, General Manager for Lenovo DCG, Middle East, Turkey and Africa, said, "We are delighted to showcase our partnership with Pivot3 at one the world's leading technology trade shows. The Middle East is exhibiting tremendous growth in terms of adopting smart solutions. The UAE in particular is investing heavily in implementing the latest innovations in their technological infrastructure; therefore, we see great potential from our partnership with Pivot3 as we work together to supply the appetite for next generation computing products and services."

Regardless of whether it's language, music, speech, or video, sequential data isn't simple for AI and machine learning models to understand, especially when it relies upon the extensive surrounding context. For example, if an individual or an item vanishes from view in a video just to return a lot later, numerous algorithms will overlook what it looked like. Researchers at Google set out to solve this with Transformer, a design that reached out to thousands of words, drastically improving performance in tasks like song composition, image synthesis, sentence-by-sentence text translation, and document summarization. In any case, Transformer isn't flawless by any stretch, extending it to bigger contexts makes clear its restrictions. Applications that utilize enormous windows have memory necessities going from gigabytes to terabytes in size, which means models can just ingest a few paragraphs of text or create short bits of music.

Researchers in Japan and The Netherlands have, for the first time, used machine learning techniques, in particular artificial neural networks (ANNs), to predict the Indian Ocean Dipole (IOD), a positive phase of which has affected weather and climate in India and Australia in a spectacular fashion so far in 2019-20. The IOD has both positive and negative phases, and signals large socio-economic impacts on many countries and hence predicting the IOD well in advance will benefit the affected societies, note authors JV Ratnam and Swadhin K Behera (Application Laboratory, Japan Agency for Marine-Earth Science and Technology, Yokohama) and HA Dijkstra (Institute for Marine and Atmospheric Research Utrecht, Utrecht University in The Netherlands) in a paper published by Nature. The IOD is a mode of climate variability observed in the Indian Ocean sea surface temperature anomalies with one pole in Sumatra (Indonesia) and the other near East Africa. Therefore, the IOD is represented by an index derived from the gradient between the western equatorial Indian Ocean and the south-eastern equatorial Indian Ocean. It starts sometime in May-June, peaks in September-October and ends in November (2019's rather strong positive phase of the IOD lasted into early January of 2020).