Abu Dhabi has established a graduate level research-based artificial intelligence (AI) university. The first of its kind university, named the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) will give graduate students, businesses and governments the ability to stimulate the AI space with access to advanced AI systems across the world. HH Sheikh Mohamed bin Zayed Al Nahyan, Crown Prince of Abu Dhabi, and Deputy Supreme Commander of the UAE Armed Forces commented that the establishment "echoes the UAE's pioneering spirit, and paves the way towards a new era of innovation and technological advancement that benefits the UAE and the world". Pizza Hut is leveraging technology to enhance its customers' experience in APAC Experts have predicted that AI's contribution to the UAE's GDP will rise 14% by 2030 and could contribute nearly US$16tn (AED58.7tn) to the global economy. "As such, the Mohamed bin Zayed University of Artificial Intelligence is an open invitation from Abu Dhabi to the world to unleash AI's full potential. The University will bring the discipline of AI into the forefront, moulding and empowering creative pioneers who can lead us to a new AI-empowered era," commented Dr. Sultan Ahmed Al Jaber, chairman of the MBZUAI board of trustees.

The Travelling Salesman Problem (TSP) is one of the most popularCombinatorial Optimization Problem. It is well solicited for the large variety ofapplications that it can solve, but also for its difficulty to find optimal solutions. Oneof the variants of the TSP is the Generalized TSP (GTSP), where the TSP isconsidered as a special case which makes the GTSP harder to solve. We propose inthis paper a new memetic algorithm based on the well-known Breakout Local Search(BLS) metaheuristic to provide good solutions for GTSP instances. Our approach iscompetitive compared to other recent memetic algorithms proposed for the GTSPand gives at the same time some improvements to BLS to reduce its runtime.Keywords: Generalized Travelling Salesman Problem, Breakout Local Search,Memetic Algorithms, Iterated Local Search

Massive volumes of high-dimensional data that evolves over time is continuously collected by contemporary information processing systems, which brings up the problem of organizing this data into clusters, i.e. achieve the purpose of dimensional deduction, and meanwhile learning its temporal evolution patterns. In this paper, a framework for evolutionary subspace clustering, referred to as LSTM-ESCM, is introduced, which aims at clustering a set of evolving high-dimensional data points that lie in a union of low-dimensional evolving subspaces. In order to obtain the parsimonious data representation at each time step, we propose to exploit the so-called self-expressive trait of the data at each time point. At the same time, LSTM networks are implemented to extract the inherited temporal patterns behind data in an overall time frame. An efficient algorithm has been proposed based on MATLAB. Next, experiments are carried out on real-world datasets to demonstrate the effectiveness of our proposed approach. And the results show that the suggested algorithm dramatically outperforms other known similar approaches in terms of both run time and accuracy.

Take advantage of our discount rooms at the conference hotel. We are beginning to announce speakers for 2020. Want to join us as a speaker? Check out our proposals page. Jesse Anderson is a data engineer, creative engineer, and managing director of the Big Data Institute. He works with companies ranging from startups to Fortune 100 companies on Big Data. This includes training on cutting edge technologies like Apache Kafka, Apache Hadoop and Apache Spark. He has taught over 30,000 people the skills to become data engineers.

It's karaoke-rehearsal time at Knollwood Military Retirement Community, a 300-bed facility tucked away in a leafy corner of northwest Washington, D.C. Knollwood resident and retired U.S. Army Colonel Phil Soriano, 86, has hosted the facility's semi-monthly singalongs since their debut during a boozy snowstorm happy hour in 2016. For the late August 2019 show, he'll share emcee duties with a special guest: Stevie, a petite and personable figure who's been living at Knollwood for the last six weeks. Soriano wants to sing the crowd-pleasing hit "YMCA" while Stevie leads the crowd through the song's signature dance moves. But Stevie is a robot, and this is harder than it sounds. "We could try to make him dance," says Niamh Donnelly, the robot's lead AI engineer, though she sounds dubious. She enters commands on a laptop.

The technological singularity, an age when machine intelligence surpasses human intelligence, is now expected to take place in 2035, 10 years earlier than initially predicted. This was the word from Shayne Manne, co-CEO of SingularityU Africa and co-founder of experiential brand agency Mann Made. Almost 2 000 attendees filled the conference centre at the Kyalami Grand Prix Circuit at the SingularityU South Africa Summit 2019 yesterday. Manne, who delivered the welcome note, discussed the current exponential change and possibilities presented by technology. He explained that singularity,a hypothetical point in the future when technological growth and machine intelligence become uncontrollable and irreversible, once predicted to take place in 2045, is now expected to take place a decade earlier, and is anticipated to result in unfathomable changes to human civilisation.

Trees are a low-tech, high-efficiency way to offset much of humankind's negative impact on the climate. What's even better, we have plenty of room for a lot more of them. A new study conducted by researchers at Switzerland's ETH-Zürich, published in Science, details how Earth could support almost an additional billion hectares of trees without the new forests pushing into existing urban or agricultural areas. Once the trees grow to maturity, they could store more than 200 billion metric tons of carbon. Great news indeed, but it still leaves us with some huge unanswered questions. Where and how are we going to plant all the new trees?

Discovering content and stories in movies is one of the most important concepts in multimedia content research studies. Network models have proven to be an efficient choice for this purpose. When an audience watches a movie, they usually compare the characters and the relationships between them. For this reason, most of the models developed so far are based on social networks analysis. They focus essentially on the characters at play. By analyzing characters' interactions, we can obtain a broad picture of the narration's content. Other works have proposed to exploit semantic elements such as scenes, dialogues, etc. However, they are always captured from a single facet. Motivated by these limitations, we introduce in this work a multilayer network model to capture the narration of a movie based on its script, its subtitles, and the movie content. After introducing the model and the extraction process from the raw data, we perform a comparative analysis of the whole 6-movie cycle of the Star Wars saga. Results demonstrate the effectiveness of the proposed framework for video content representation and analysis.

The results of data mining endeavors are majorly driven by data quality. Throughout these deployments, serious show-stopper problems are still unresolved, such as: data collection ambiguities, data imbalance, hidden biases in data, the lack of domain information, and data incompleteness. This paper is based on the premise that context can aid in mitigating these issues. In a traditional data science lifecycle, context is not considered. Context-driven Data Science Lifecycle (C-DSL); the main contribution of this paper, is developed to address these challenges. Two case studies (using data-sets from sports events) are developed to test C-DSL. Results from both case studies are evaluated using common data mining metrics such as: coefficient of determination (R2 value) and confusion matrices. The work presented in this paper aims to re-define the lifecycle and introduce tangible improvements to its outcomes.

Temporal networks representing a stream of timestamped edges are seemingly ubiquitous in the real-world. However, the massive size and continuous nature of these networks make them fundamentally challenging to analyze and leverage for descriptive and predictive modeling tasks. In this work, we propose a general framework for temporal network sampling with unbiased estimation. We develop online, single-pass sampling algorithms and unbiased estimators for temporal network sampling. The proposed algorithms enable fast, accurate, and memory-efficient statistical estimation of temporal network patterns and properties. In addition, we propose a temporally decaying sampling algorithm with unbiased estimators for studying networks that evolve in continuous time, where the strength of links is a function of time, and the motif patterns are temporally-weighted. In contrast to the prior notion of a $\bigtriangleup t$-temporal motif, the proposed formulation and algorithms for counting temporally weighted motifs are useful for forecasting tasks in networks such as predicting future links, or a future time-series variable of nodes and links. Finally, extensive experiments on a variety of temporal networks from different domains demonstrate the effectiveness of the proposed algorithms.