The ISO/IEC JTC 1/SC 42 Artificial Intelligence community met in #Google in #SiliconValley #Sunnyvale this week 8th - 12th October 2018 under the Chairmanship of Wael William Diab . The Irish National Delegation was well represented 3 Academics from the Science Foundation Ireland funded Adapt Research Centre, SC42 Convenor Dr. David Filip, SC42 Editor Ray Walshe and SC42 Expert Prof Dave Lewis. The other 3 delegates were SC42 Secretariat Barry Smith (NSAI), JTC1 PAS Brian McAuliffe (HP) and NSAI Board Member Terry Landers (Microsoft). The team were actively involved in multiple SC42 areas. After a long week of discussion in National Body mode and AI Expert Mode has progressed the development of the study group reports and established new projects and working groups in Big Data, Trustworthiness and AI Use Cases.

The World Bank has a reassuring message for those fearful of being made obsolete by automation. The robot age is nothing to be worried about. Just like all previous waves of technological advance, the fourth industrial revolution will create rather than destroy jobs, so fears of mass unemployment are largely unfounded. Nor should we be concerned that the arrival of the new machine age is going to widen the gap between rich and poor, because the idea that the world is becoming a less equal place is more perception than reality. Automation, according to the bank's World Development Report, is an opportunity not a threat.

Cambridge has been chosen to host a new global forum designed to change the face of international healthcare. Gamechanging innovation will be centre stage at the Next Health World Innovation Forum – a new international healthcare conference which takes place in Cambridge from June 26-27, 2019. The Innovation Forum is partnering with Cambridge's pioneering Milner Therapeutics Institute to challenge the status quo and identify technologies that will revolutionise patient care across a range of disease areas. The event will incorporate the annual symposium of the Milner Institute, which is dedicated to transforming groundbreaking research into new therapies, by breaking down the barriers between academia and industry. Confirmed speakers include representatives from Servier, Pfizer, Innovative Medicines Initiative and Cancer Research UK.

Globant (NYSE:GLOB) and Verisk Analytics (NASDAQ:VRSK) are both computer and technology companies, but which is the superior investment? We will compare the two businesses based on the strength of their earnings, risk, dividends, analyst recommendations, profitability, institutional ownership and valuation. This table compares Globant and Verisk Analytics' net margins, return on equity and return on assets. This table compares Globant and Verisk Analytics' revenue, earnings per share (EPS) and valuation. Verisk Analytics has higher revenue and earnings than Globant.

Global defence giant Thales is set to create up to 100 jobs at a new £1million base in Plymouth where it will test robotic boats for dealing with sea-borne mines. The French multinational has opened a trials and training centre at Turnchapel Wharf, creating 20 high-skilled jobs, but with an ambition to grow, possibly to as many as 100. The investment is part of a "major commitment" to developing autonomous and unmanned technology for use in the air and on sea – in other words, robotic vessels. Thales, which reported global sales of €14 billion in 2015, is creating two new UK centres, the other being in Wales. The company said that only through experimentation with new and disruptive technologies will the UK military be able to stay ahead and maintain an advantage over other forces.

Change detection involves segmenting sequential data such that observations in the same segment share some desired properties. Multivariate change detection continues to be a challenging problem due to the variety of ways change points can be correlated across channels and the potentially poor signal-to-noise ratio on individual channels. In this paper, we are interested in locating additive outliers (AO) and level shifts (LS) in the unsupervised setting. We propose ABACUS, Automatic BAyesian Changepoints Under Sparsity, a Bayesian source separation technique to recover latent signals while also detecting changes in model parameters. Multi-level sparsity achieves both dimension reduction and modeling of signal changes. We show ABACUS has competitive or superior performance in simulation studies against state-of-the-art change detection methods and established latent variable models. We also illustrate ABACUS on two real application, modeling genomic profiles and analyzing household electricity consumption.

Data preprocessing techniques are devoted to correct or alleviate errors in data. Discretization and feature selection are two of the most extended data preprocessing techniques. Although we can find many proposals for static Big Data preprocessing, there is little research devoted to the continuous Big Data problem. Apache Flink is a recent and novel Big Data framework, following the MapReduce paradigm, focused on distributed stream and batch data processing. In this paper we propose a data stream library for Big Data preprocessing, named DPASF, under Apache Flink. We have implemented six of the most popular data preprocessing algorithms, three for discretization and the rest for feature selection. The algorithms have been tested using two Big Data datasets. Experimental results show that preprocessing can not only reduce the size of the data, but to maintain or even improve the original accuracy in a short time. DPASF contains useful algorithms when dealing with Big Data data streams. The preprocessing algorithms included in the library are able to tackle Big Datasets efficiently and to correct imperfections in the data.

These sources contain images that viewers would have to interpret themselves. Most images do not have a description, but the human can largely understand them without their detailed captions. However, machine needs to interpret some form of image captions if humans need automatic image captions from it. Image captioning is important for many reasons. For example, they can be used for automatic image indexing. Image indexing is important for Content-Based Image Retrieval (CBIR) and therefore, it can be applied to many areas, including biomedicine, commerce, the military, education, digital libraries, and web searching. Social media platforms such as Facebook and Twitter can directly generate descriptions from images. The descriptions can include where we are (e.g., beach, cafe), what we wear and importantly what we are doing there.

After the recent groundbreaking results of AlphaGo and AlphaZero, we have seen strong interests in deep reinforcement learning and artificial general intelligence (AGI) in game playing. However, deep learning is resource-intensive and the theory is not yet well developed. For small games, simple classical table-based Q-learning might still be the algorithm of choice. General Game Playing (GGP) provides a good testbed for reinforcement learning to research AGI. Q-learning is one of the canonical reinforcement learning methods, and has been used by (Banerjee $\&$ Stone, IJCAI 2007) in GGP. In this paper we implement Q-learning in GGP for three small-board games (Tic-Tac-Toe, Connect Four, Hex)\footnote{source code: https://github.com/wh1992v/ggp-rl}, to allow comparison to Banerjee et al.. We find that Q-learning converges to a high win rate in GGP. For the $\epsilon$-greedy strategy, we propose a first enhancement, the dynamic $\epsilon$ algorithm. In addition, inspired by (Gelly $\&$ Silver, ICML 2007) we combine online search (Monte Carlo Search) to enhance offline learning, and propose QM-learning for GGP. Both enhancements improve the performance of classical Q-learning. In this work, GGP allows us to show, if augmented by appropriate enhancements, that classical table-based Q-learning can perform well in small games.

Dropout is a popular regularization technique in neural networks. Yet, the reason for its success is still not fully understood. This paper provides a new interpretation of Dropout from a frame theory perspective. This leads to a novel regularization technique for neural networks that minimizes the cross-correlation between filters in the network. We demonstrate its applicability in convolutional and fully connected layers in both feed-forward and recurrent networks.