How should government and society come together to address the challenge of regulating artificial intelligence? What approaches and tools will promote innovation, protect society from harm and build public trust in AI? Artificial intelligence (AI) is a key driver of the Fourth Industrial Revolution. Algorithms are already being applied to improve predictions, optimize systems and drive productivity in many other sectors. However, early experience shows that AI can create serious challenges. Without proper oversight, AI may replicate or even exacerbate human bias and discrimination, cause potential job displacement, and lead to other unintended and harmful consequences.

WELLINGTON – Mike Moore, who served as New Zealand's prime minister before leading the World Trade Organization during a tumultuous time when thousands protested in Seattle riots, died early Sunday. He died at his home in Auckland, his wife Yvonne Moore said. He had suffered a number of health complications since having a stroke five years ago. Moore was an advocate for both advancing the rights of blue-collar workers and for expanding international trade, a combination which, to some, seemed at odds with itself. Although he had a long political career in New Zealand, Moore's tenure as prime minister was brief: just two months in 1990 before he was defeated in an election.

Human knowledge provides a formal understanding of the world. Knowledge graphs that represent structural relations between entities have become an increasingly popular research direction towards cognition and human-level intelligence. In this survey, we provide a comprehensive review on knowledge graph covering overall research topics about 1) knowledge graph representation learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph, and 4) knowledge-aware applications, and summarize recent breakthroughs and perspective directions to facilitate future research. We propose a full-view categorization and new taxonomies on these topics. Knowledge graph embedding is organized from four aspects of representation space, scoring function, encoding models and auxiliary information. For knowledge acquisition, especially knowledge graph completion, embedding methods, path inference and logical rule reasoning are reviewed. We further explore several emerging topics including meta relational learning, commonsense reasoning, and temporal knowledge graphs. To facilitate future research on knowledge graphs, we also provide a curated collection of datasets and open-source libraries on different tasks. In the end, we have a thorough outlook on several promising research directions.

The evaluation of machine learning (ML) pipelines is essential during automatic ML pipeline composition and optimisation. The previous methods such as Bayesian-based and genetic-based optimisation, which are implemented in Auto-Weka, Auto-sklearn and TPOT, evaluate pipelines by executing them. Therefore, the pipeline composition and optimisation of these methods requires a tremendous amount of time that prevents them from exploring complex pipelines to find better predictive models. To further explore this research challenge, we have conducted experiments showing that many of the generated pipelines are invalid, and it is unnecessary to execute them to find out whether they are good pipelines. To address this issue, we propose a novel method to evaluate the validity of ML pipelines using a surrogate model (AVATAR). The AVATAR enables to accelerate automatic ML pipeline composition and optimisation by quickly ignoring invalid pipelines. Our experiments show that the AVATAR is more efficient in evaluating complex pipelines in comparison with the traditional evaluation approaches requiring their execution.

Perth City Council has reportedly been filming and tracking people moving around parts of the city without their knowledge. In what the council calls a trial, a network of 30 cameras with facial recognition technology have been deployed across East Perth. This has quietly gone on for six months. The cameras use deep-learning artificial intelligence (AI) to recognise faces and vehicles, and to count passing people – a form of population control which China widely employs, and is criticised for by human rights groups. But in Perth – the third Australian city to invest in the technology – many residents were unaware of the trial before it started.

Several countries are currently investigating issues of neglect, poor quality care and abuse in the aged care sector. In most cases it is the State who license and monitor aged care providers, which frequently introduces a serious conflict of interest because the State also operate many of the facilities where our most vulnerable peoples are cared for. Where issues are raised with the standard of care being provided, the State are seen by many as a deep-pockets defendant and become the target of high-value lawsuits. This paper draws on cases and circumstances from one jurisdiction based on the English legal tradition, Australia, and proposes a Bayesian solution capable of determining probability for success for citizen plaintiffs who bring negligence claims against a public authority defendant. Use of a Bayesian network trained on case audit data shows that even when the plaintiff case meets all requirements for a successful negligence litigation, success is not often assured. Only in around one-fifth of these cases does the plaintiff succeed against a public authority as defendant.

Martel, Jozef Adamcik, Matthew Cook, Richard H. R. Hahnloser Abstract --Serial section electron microscopy (ssEM) is a widely used technique for obtaining volumetric information of biological tissues at nanometer scale. However, accurate 3D reconstructions of identified cellular structures and volumetric quantifications require precise estimates of section thickness and anisotropy (or stretching) along the XY imaging plane. In fact, many image processing algorithms simply assume isotropy within the imaging plane. T o ameliorate this problem, we present a method for estimating thickness and stretching of electron microscopy sections using nonparametric Bayesian regression of image statistics. We verify our thickness and stretching estimates using direct measurements obtained by atomic force microscopy (AFM) and show that our method has a lower estimation error compared to a recent indirect thickness estimation method as well as a relative Z coordinate estimation method. Furthermore, we have made the first dataset of ssSEM images with directly measured section thickness values publicly available for the evaluation of indirect thickness estimation methods. I NTRODUCTION Electron microscopy (EM) has enabled imaging of nano-scale neuroanatomical structures such as synapses. Serial section Scanning Electron Microscopy (ssSEM) and serial section Transmission Electron Microscopy (ssTEM) are used to inspect tissue volumes on the scale of tens to hundreds of micrometers in each dimension. Tissue sections suitable for ssEM typically have a thickness that ranges from 30 nm to 70 nm .

The global sports tech ecosystem is awash with early-stage companies and entrepreneurs who have brought to market all manner of novel solutions and innovations. In recent years, the number of startups specialising in areas such as athletic performance and analytics, artificial intelligence (AI), big data, fantasy sports, gaming, content production and in-venue technology has proliferated, contributing to rapid growth across the sports tech sector. Already estimated to be worth US$8.9 billion, the global sports tech market is expected to triple in value in the next five years. But, as any investor worth their salt will know, not all new technologies make for attractive investment propositions. Here, with the help of Sports Loft founder Charlie Greenwood, SportsPro profiles the innovators whose products and services should be on every sports tech investor's radar.

In the space of a single year, South African agritech enterprise Aerobotics has won numerous awards and made strategic inroads into the massively competitive US agriculture industry. Propelled by world-leading technology, the South African success story is poised to mushroom into a truly global data and analytics software company serving the entire agriculture value chain. Aerobotics, which as little as a year ago was nominated as one of South Africa's most exciting startups, turns imagery into actionable data so that any issues on the farm, or elsewhere in the value chain, can be identified and resolved before they become problems. In essence, Aerobotics exposes what the naked eye cannot see in order to solve problems and make accurate projections, translating into improved yields and profitability. The company's CEO, James Paterson, says the business is ready to build on its highly successful launch in the US and strategically drop further roots and extend services in numerous regions around the world.

What exactly is biotechnology, and how could it change our approach to human health? As the age of big data transforms the potential of this emerging field, members of the World Economic Forum's Global Future Council on Biotechnology tell you everything you need to know. What if your doctor could predict your heart attack before you had it – and prevent it? Or what if we could cure a child's cancer by exploiting the bacteria in their gut? These types of biotechnology solutions aimed at improving human health are already being explored. As more and more data (so called "big data") is available across disparate domains such as electronic health records, genomics, metabolomics, and even life-style information, further insights and opportunities for biotechnology will become apparent. However, to achieve the maximal potential both technical and ethical issues will need to be addressed. As we look to the future, let's first revisit previous examples of where combining data with scientific understanding has led to new health solutions. Biotechnology is a rapidly changing field that continues to transform both in scope and impact. Karl Ereky first coined the term biotechnology in 1919.