In my keynote speech, I emphasised the fact that Africa will miss the point if the continent listened more to sceptics and failed to prepare for the emerging new world order – the fourth industrial revolution – that in my view was ushered unto the world at the World Economic Forum (WEF) in Davos at the beginning of this year.

Along with the advent of blockchain, artificial intelligence is an innovation that could change the world. As cryptocurrencies have grown more popular in use and application, so too are the number of projects focused on fusing these two technologies together. This blockchain project has become known worldwide for creating the robot called Sophia, the first robot ever to have citizenship. Thanks to this, it's one of the most recognized and prominent projects when it comes to blockchain and AI. Singularity is a decentralized AI marketplace created for the development and funding of AI projects.

The high seas are probably the most lawless place left on Earth. They're a portal back in time to the way the world looked for most of our history: fierce and open competition for resources and contested territories. Pirating continues to be a way to make a living. It's not a complete free-for-all--most countries require registration of fishing vessels and enforce environmental protocols. Cooperative agreements between countries oversee fisheries in international waters.

Their technical forebears are, obviously, helicopters. But helicopters are "too noisy, inefficient, polluting and expensive for mass-scale use," says the white paper for UberAir, the company's aeromobile arm. "VTOL aircraft will make use of electric propulsion so they have zero operational emissions and will likely be quiet enough to operate in cities without disturbing the neighbors." Your weekly look at how innovation and technology are transforming the way we live, work and play. Tap here to get it delivered to your inbox.

Tasks involving the analysis of geometric (graph- and manifold-structured) data have recently gained prominence in the machine learning community, giving birth to a rapidly developing field of geometric deep learning. In this work, we leverage graph neural networks to improve signal detection in the IceCube neutrino observatory. The IceCube detector array is modeled as a graph, where vertices are sensors and edges are a learned function of the sensors' spatial coordinates. As only a subset of IceCube's sensors is active during a given observation, we note the adaptive nature of our GNN, wherein computation is restricted to the input signal support. We demonstrate the effectiveness of our GNN architecture on a task classifying IceCube events, where it outperforms both a traditional physics-based method as well as classical 3D convolution neural networks.

In this paper, we show how unsupervised sense representations can be used to improve hypernymy extraction. We present a method for extracting disambiguated hypernymy relationships that propagates hypernyms to sets of synonyms (synsets), constructs embeddings for these sets, and establishes sense-aware relationships between matching synsets. Evaluation on two gold standard datasets for English and Russian shows that the method successfully recognizes hypernymy relationships that cannot be found with standard Hearst patterns and Wiktionary datasets for the respective languages.

WASHINGTON (WASHINGTON POST) - A new poll shows that in several countries around the world, large majorities of people believe it is most likely that robots will be doing much of the work done by humans within 50 years. The effects of this technological leap are not viewed optimistically by most, however. Instead, people largely say they think humans will struggle to find meaningful work and inequality will rise, the research found. The poll was conducted earlier this year by the Pew Research Centre in Greece, Japan, Canada, Argentina, Poland, Brazil, South Africa, Italy and Hungary. Pew also compared the responses in those countries to a poll done in the United States in 2015 that asked about automation. In general, the poll found that majorities in most countries were in agreement that robots would soon do humans' work, with only limited differences in their views of how this would affect society despite some countries being advanced economically and others still developing.

Recently, product images have gained increasing attention in clothing recommendation since the visual appearance of clothing products has a significant impact on consumers' decision. Most existing methods rely on conventional features to represent an image, such as the visual features extracted by convolutional neural networks (CNN features) and the scale-invariant feature transform algorithm (SIFT features), color histograms, and so on. Nevertheless, one important type of features, the \emph{aesthetic features}, is seldom considered. It plays a vital role in clothing recommendation since a users' decision depends largely on whether the clothing is in line with her aesthetics, however the conventional image features cannot portray this directly. To bridge this gap, we propose to introduce the aesthetic information, which is highly relevant with user preference, into clothing recommender systems. To achieve this, we first present the aesthetic features extracted by a pre-trained neural network, which is a brain-inspired deep structure trained for the aesthetic assessment task. Considering that the aesthetic preference varies significantly from user to user and by time, we then propose a new tensor factorization model to incorporate the aesthetic features in a personalized manner. We conduct extensive experiments on real-world datasets, which demonstrate that our approach can capture the aesthetic preference of users and significantly outperform several state-of-the-art recommendation methods.

Data heterogeneity hampers the effort to integrate and infer knowledge from vast heterogeneous data sources. An application case study is described, in which the objective was to semantically represent and integrate structured data from sensor devices with unstructured data in the form of local indigenous knowledge. However, the semantic representation of these heterogeneous data sources for environmental monitoring systems is not well supported yet. To combat the incompatibility issues, a dedicated semantic middleware solution is required. In this paper, we describe and evaluate a cross-domain middleware architecture that semantically integrates and generate inference from heterogeneous data sources. These use of semantic technology for predicting and forecasting complex environmental phenomenon will increase the degree of accuracy of environmental monitoring systems.

The Chart of the Week is a weekly Visual Capitalist feature on Fridays. Artificial Intelligence is transforming the way we live, and the tech giants are racing to stay ahead of the curve. AI-related funding totaled an estimated $15.2 billion in 2017, a 144% increase over the previous year. The U.S. tech industry leads with a 50% share of those investments, even with China swiftly closing the gap in terms of patents and AI research. AI itself isn't new, but boosted computing power, increased connectivity, and the sheer volume of data has paved the way for the fourth industrial revolution of AI. "The coming era will be looked back upon as the'AI era,' when AI became the defining competitive advantage for corporations, government agencies, and investment professionals," predicts David Nadler, founder of Kensho Technologies.