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.

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).

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.

China estimates that by 2030, AI-related activities will generate 26% of its GDP. The UAE has set up a ministry of AI. The US has created a strong ecosystem comprising advanced research institutions, universities, labs, start-up hubs and institutional capital. The French government will spend €1.5 billion over five years to support research in the field, encourage start-ups, and collect data that can be used and shared by engineers. The UK plans to nurture 1000 government-supported PhD researchers by 2025.

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.

Future Tense is a partnership of Slate, New America, and Arizona State University that examines emerging technologies, public policy, and society. It's a common curse: The flash goes off, and you blink, resulting in an image that makes you look ridiculous. But Facebook thinks it doesn't have to be that way. Researchers there have created an artificial intelligence system that can retouch images of people blinking to replace closed eyes with convincing computer-generated open eyes. The tool uses a generative adversarial network, or GAN, which is a two-part machine learning system whose dual components compete against each other to try to fool the system into thinking its computer-generated images are real.

PolygamousRanchKid shares a report: The human brain may be the ultimate super computer, but artificial intelligence is catching up so fast, it can now hold a substantive debate with a human, according to audience feedback. IBM's Project Debater made its public debut in San Francisco Monday afternoon, where it squared off against Noa Ovadia, the 2016 Israeli debate champion and in a second debate, Dan Zafrir, a nationally renowned debater in Israel. The AI is the latest grand challenge from IBM, which previously created Deep Blue, technology that beat chess champion Garry Kasparov and Watson, which bested humans on the game show Jeopardy. In its first public outing, Project Debater turned out to be a formidable opponent, scanning the hundreds of millions of newspaper and journal articles in its memory to quickly synthesize an argument on a topic and position it was assigned on the spot. "Project Debater could be the ultimate fact-based sounding board without the bias that often comes from humans," said Arvind Krishna, director of IBM Research.

In what may be the biggest rollout of conversational AI from IBM since Watson, IBM Research today debuted Project Debater, an experimental conversational AI with a sense of humor, little tact, and occasionally powerful arguments. Training of Project Debater began six years ago, but it only gained an ability to participate in debates with people two years ago, said Noam Slonim, IBM Research principal investigator and creator of Project Debater. Debater's smarts come from hundreds of millions of interactions with millions of journal and newspaper articles. The AI system's ability to deliver persuasive arguments was demonstrated for an audience of tech journalists gathered at IBM offices in San Francisco, where the AI system participated in debates about whether governments should subsidize space exploration and whether telemedicine should play a bigger role in health care. When Project Debater gets a new topic, it searches its corpus of articles for sentences and clauses that are relevant, argumentative, and support its side of the debate.