Knowledge bases are employed in a variety of applications from natural language processing to semantic web search; alas, in practice their usefulness is hurt by their incompleteness. Embedding models attain state-of-the-art accuracy in knowledge base completion, but their predictions are notoriously hard to interpret. In this paper, we adapt "pedagogical approaches" (from the literature on neural networks) so as to interpret embedding models by extracting weighted Horn rules from them. We show how pedagogical approaches have to be adapted to take upon the large-scale relational aspects of knowledge bases and show experimentally their strengths and weaknesses.

Binary neural networks (BNN) have been studied extensively since they run dramatically faster at lower memory and power consumption than floating-point networks, thanks to the efficiency of bit operations. However, contemporary BNNs whose weights and activations are both single bits suffer from severe accuracy degradation. To understand why, we investigate the representation ability, speed and bias/variance of BNNs through extensive experiments. We conclude that the error of BNNs are predominantly caused by the intrinsic instability (training time) and non-robustness (train & test time). Inspired by this investigation, we propose the Binary Ensemble Neural Network (BENN) which leverages ensemble methods to improve the performance of BNNs with limited efficiency cost. While ensemble techniques have been broadly believed to be only marginally helpful for strong classifiers such as deep neural networks, our analyses and experiments show that they are naturally a perfect fit to boost BNNs. We find that our BENN, which is faster and much more robust than state-of-the-art binary networks, can even surpass the accuracy of the full-precision floating number network with the same architecture.

Trajectory prediction (TP) is of great importance for a wide range of location-based applications in intelligent transport systems such as location-based advertising, route planning, traffic management, and early warning systems. In the last few years, the widespread use of GPS navigation systems and wireless communication technology enabled vehicles has resulted in huge volumes of trajectory data. The task of utilizing this data employing spatio-temporal techniques for trajectory prediction in an efficient and accurate manner is an ongoing research problem. Existing TP approaches are limited to short-term predictions. Moreover, they cannot handle a large volume of trajectory data for long-term prediction. To address these limitations, we propose a scalable clustering and Markov chain based hybrid framework, called Traj-clusiVAT-based TP, for both short-term and long-term trajectory prediction, which can handle a large number of overlapping trajectories in a dense road network. In addition, Traj-clusiVAT can also determine the number of clusters, which represent different movement behaviours in input trajectory data. In our experiments, we compare our proposed approach with a mixed Markov model (MMM)-based scheme, and a trajectory clustering, NETSCAN-based TP method for both short- and long-term trajectory predictions. We performed our experiments on two real, vehicle trajectory datasets, including a large-scale trajectory dataset consisting of 3.28 million trajectories obtained from 15,061 taxis in Singapore over a period of one month. Experimental results on two real trajectory datasets show that our proposed approach outperforms the existing approaches in terms of both short- and long-term prediction performances, based on prediction accuracy and distance error (in km).

Through the success of deep learning in various domains, artificial neural networks are currently among the most used artificial intelligence methods. Taking inspiration from the network properties of biological neural networks (e.g. sparsity, scale-freeness), we argue that (contrary to general practice) artificial neural networks, too, should not have fully-connected layers. Here we propose sparse evolutionary training of artificial neural networks, an algorithm which evolves an initial sparse topology (Erd\H{o}s-R\'enyi random graph) of two consecutive layers of neurons into a scale-free topology, during learning. Our method replaces artificial neural networks fully-connected layers with sparse ones before training, reducing quadratically the number of parameters, with no decrease in accuracy. We demonstrate our claims on restricted Boltzmann machines, multi-layer perceptrons, and convolutional neural networks for unsupervised and supervised learning on 15 datasets. Our approach has the potential to enable artificial neural networks to scale up beyond what is currently possible.

We prove the existence of a semilinear representation of Cellular Automata (CA) with the introduction of multiple convolution kernels. Examples of the technique are presented for rules akin to the "edge-of-chaos" including the Turing universal rule 110 for further utilization in the area of reservoir computing. We also examine the significance of their dual representation on a frequency or wavelength domain as a superposition of plane waves for distributed computing applications including a new proposal for a "Hologrid" that could be realized with present Wi-Fi/Li-Fi technologies. Keywords: Cellular Automata, Distributed Computing, Holographic Representations Introduction Distributed computing has a long history running the full half of the previous century and its development went hand in hand with the rise of connectionist paradigm out of the study of both natural and artificial neural networks [1]. Perhaps one of the first application of holographic principles in general computation are to be found in the entirely original method of tearing by Kron [2], later termed "Diakoptics", which was invented for the efficient solution of large electrical networks by decomposition.

The United Arab Emirates (UAE) is the first country in the world to appoint a State Minister for Artificial Intelligence (AI). The UAE is embracing AI in society at the governmental level, which is leading to a new generations of digital government (which we are labeling Gov. 3.0). This paper argues that the decision to embrace AI will lead to positive impacts on society, including businesses, organizations and individuals, as well as on the AI industry itself. This paper discusses the societal impacts of AI at a macro (country-wide) level. This article is part of the special track on AI and Society.

New York, NY--June 19, 2018--A major challenge in current climate prediction models is how to accurately represent clouds and their atmospheric heating and moistening. This challenge is behind the wide spread in climate prediction. Yet accurate predictions of global warming in response to increased greenhouse gas concentrations are essential for policy-makers (e.g. the Paris climate agreement). In a paper recently published online in Geophysical Research Letters (May 23), researchers led by Pierre Gentine, associate professor of earth and environmental engineering at Columbia Engineering, demonstrate that machine learning techniques can be used to tackle this issue and better represent clouds in coarse resolution ( 100km) climate models, with the potential to narrow the range of prediction. "This could be a real game-changer for climate prediction," says Gentine, lead author of the paper, and a member of the Earth Institute and the Data Science Institute.

The year of 2018 should be affectionately referred to as the'Year of Sci-Fi Dreams'. This is primarily due to the fact that almost every unimaginable concept from the depths of science fiction cinema has started becoming a reality. Even though the advent of Deep Neural Networks and Artificial General Intelligence (AGI) has been making waves across the technology ecosystem (like the defeat of World Champion Lee Sedol in the game of'Go' by AGI AlphaGo-Zero), there have been a few innovations that have baffled even the brightest minds in the industry. So ignoring the various trends and innovations in the world of AI, here are three technological advancements in the field of AI and Machine Learning (ML) that will blow your mind! One of the biggest achievements in the field of Medicine was the creation of a fully functional human lung, just from stem cells.

This year, we have seen an acceleration of Silicon Valley tech giants opening AI research labs around the world as they seek to gain traction among researchers and fulfill their global ambitions. In the past six months or so, Google brought labs to China and France, Facebook opened labs in Pittsburgh and Seattle, and Microsoft announced plans to open labs near universities in Berkeley, California and Melbourne, Australia. This trend shows no signs of slowing down. Last month, Samsung announced labs in Cambridge, Moscow, and Toronto. This week, Nvidia announced plans to open a new lab in Toronto, while Google shared plans to open a lab in Accra, Ghana, Google's first in Africa and perhaps the first of any tech giant in Africa.

Amazon's plan to put Alexa everywhere is extending from homes to hotels and, soon, the Mediterranean. It will bring Alexa and the various Echo devices to Spain and Italy later this year. Sonos and Bose will also start selling their Alexa-enabled devices in those countries before the year's out. In the meantime, Amazon is encouraging developers to start creating skills in Italian and Spanish. There's no use having a smart voice assistant on hand if it can't do a whole lot, after all.