ING, KLM, NS, Phillips and food retailer Ahold Delhaize have announced the launch of Kickstart AI, a series of initiatives to promote and grow the Dutch artificial intelligence sector. By accelerating the development of new technology and supporting talent, it aims to improve education in AI, nurture the local AI community and raise the Netherlands' profile as an international hub in this growing field. The Kickstart AI programme aims to bridge the gap between the Netherlands and other countries with strong artificial intelligence industries, including China, the UK and the US. To accomplish this goal, it will bring together the Dutch government, private organisations, businesses and universities to develop investment and talent. The companies behind the programme believe it can help solve societal challenges, provide new opportunities for young people and advance the rate that different industries (including food, healthcare and finance) can use AI to improve the lives of consumers. In a statement about the launch of Kickstart AI, Maarten de Rijke, professor at the University of Amsterdam and director of the Innovation Center for Artificial Intelligence, said: "The Netherlands has always been a technological pioneer, a forerunner in the development and innovation of AI.

I work in a research department where one of my seniors are authoring a position paper on trustworthy AI, and we came into a discussion regarding the phrase "...understanding the theoretical basis for (human) intelligence has gone hand in hand with improvements in the capabilities of real systems." Even though it was referenced to Russel and Norvigs book, I think the statement is misleading and a bit sensationalist, as more or less none of the recent advances in deep learning I can think of are inspired by neuroscience. In fact, the two seems more detached now than ever. I've tried searching for good papers to back my claim on this, but I have not been able to find any. Are there any good material on the subject of the connection between applied AI and the theory of human intelligence?

There's a new "Terminator" movie coming out -- and artificial intelligence researchers really hope you don't see it. The issue lies in the films' misrepresentation of AI, several experts told BBC News in a fascinating new story, which they worry could stir up irrational fears about the tech the same way "Jaws" once made moviegoers afraid to go in the water. "[The films] paint a picture which is really not coherent with the current understanding of how AI systems are built today and in the foreseeable future," AI pioneer Yoshua Bengio told the outlet. The reality is that today's AI systems are hyper-specialized, with programmers often designing an AI to excel at just one task, such as playing a board game. Researchers haven't come close to creating a Terminator-level artificial general intelligence capable of acting beyond the control of its creator.

Augmented analytics, Gartner continued, will increasingly leverage machine learning (ML) and drive new use cases in the market.

If you're still emotionally wiped out by Avengers Endgame, The Void and ILMxLAB's latest VR entry might soothe your geeky soul. Avengers Damage Control is more than just a mere virtual reality game, like the upcoming Iron Man title for the PlayStation VR. Instead, it's a prime example of what The Void does best: Building large-scale multi-player VR experiences mapped to physical sets. It's a dream come true for anyone who's ever wanted to fight alongside their favorite Marvel superheroes -- just be prepared to shell out $40 to experience it. Suiting up for Damage Control involves strapping on one of The Void's backpack computers, as well as a huge VR headset.

Newswire) GBT Technologies Inc. (OTCPINK: GTCH) ("GBT", or the "Company"), a company specializing in the development of Internet of Things (IoT) and Artificial Intelligence (AI) enabled networking and tracking technologies, including its GopherInsight wireless mesh network technology platform and its Avant! AI, for both mobile and fixed solutions, announced that it is now adding the first elements of cognitive features within its AI expert agent. The agent now includes feedback features, i.e. "thumbs up" and "thumbs down", that work with the artificial neural network mechanism to learn and improve answers' accuracy and their relationship to the topic. The user feedback is fed into the Avant! RNN (Recurrent Neural Network), which synthesizes data from various information sources, weighing and comparing the feedback to the answer context to provide the best, most accurate answers.

Knowledge Graphs (KGs) are composed of structured information about a particular domain in the form of entities and relations. In addition to the structured information KGs help in facilitating interconnectivity and interoperability between different resources represented in the Linked Data Cloud. KGs have been used in a variety of applications such as entity linking, question answering, recommender systems, etc. However, KG applications suffer from high computational and storage costs. Hence, there arises the necessity for a representation able to map the high dimensional KGs into low dimensional spaces, i.e., embedding space, preserving structural as well as relational information. This paper conducts a survey of KG embedding models which not only consider the structured information contained in the form of entities and relations in a KG but also the unstructured information represented as literals such as text, numerical values, images, etc. Along with a theoretical analysis and comparison of the methods proposed so far for generating KG embeddings with literals, an empirical evaluation of the different methods under identical settings has been performed for the general task of link prediction.

The traditional offline approaches are no longer sufficient for building modern recommender systems in domains such as online news services, mainly due to the high dynamics of environment changes and necessity to operate on a large scale with high data sparsity. The ability to balance exploration with exploitation makes the multi-armed bandits an efficient alternative to the conventional methods, and a robust user segmentation plays a crucial role in providing the context for such online recommendation algorithms. In this work, we present an unsupervised and trend-responsive method for segmenting users according to their semantic interests, which has been integrated with a real-world system for large-scale news recommendations. The results of an online A/B test show significant improvements compared to a global-optimization algorithm on several services with different characteristics. Based on the experimental results as well as the exploration of segments descriptions and trend dynamics, we propose extensions to this approach that address particular real-world challenges for different use-cases. Moreover, we describe a method of generating traceable publishing insights facilitating the creation of content that serves the diversity of all users needs.