In this paper, we propose a fundamentally new approach to Datalog evaluation. Given a linear Datalog program DB written using N constants and binary predicates, we first translate if-and-only-if completions of clauses in DB into a set Eq(DB) of matrix equations with a non-linear operation where relations in M_DB, the least Herbrand model of DB, are encoded as adjacency matrices. We then translate Eq(DB) into another, but purely linear matrix equations tilde_Eq(DB). It is proved that the least solution of tilde_Eq(DB) in the sense of matrix ordering is converted to the least solution of Eq(DB) and the latter gives M_DB as a set of adjacency matrices. Hence computing the least solution of tilde_Eq(DB) is equivalent to computing M_DB specified by DB. For a class of tail recursive programs and for some other types of programs, our approach achieves O(N^3) time complexity irrespective of the number of variables in a clause since only matrix operations costing O(N^3) or less are used. We conducted two experiments that compute the least Herbrand models of linear Datalog programs. The first experiment computes transitive closure of artificial data and real network data taken from the Koblenz Network Collection. The second one compared the proposed approach with the state-of-the-art symbolic systems including two Prolog systems and two ASP systems, in terms of computation time for a transitive closure program and the same generation program. In the experiment, it is observed that our linear algebraic approach runs 10^1 ~ 10^4 times faster than the symbolic systems when data is not sparse. To appear in Theory and Practice of Logic Programming (TPLP).

Increased automation in the workforce has caused many to fear their jobs will soon be taken over by robots – and, experts now warn even the military will be affected by the shift. The use of robots could help the US military to cut costs and would allow them to remove soldiers from potentially dangerous environments, the San Diego Union-Tribune reports. Experts say the move has already begun in many ways, with newer warships like the USS Zumwalt designed to be manned by half the crew of previous destroyers, and Navy researchers experimenting with'ghost fleets' of underwater drones. Experts point to current technologies that already aim to reduce the number of human operators, such as the guided-missile destroyer Zumwalt. A White House report published in December warned millions of American may lose their jobs to AI. Less-skilled and less-educated workers will be affected the most.

From body parts to supercars, the family of 3D printed products just keeps expanding. But in a study published last week in Science Advances, scientists think small: German researchers 3D printed different lenses--each smaller than the width of a human hair--onto a chip. Such micro-cameras could be perfect for tiny drones and other pint-sized robots. "Our system is the only one in the world [where] you can put different optic systems on one imaging sensor that is very small," says study author Alois Herkommer, an applied physicist at the University of Stuttgart in Stuttgart, Germany. "The advantage of doing this by 3D printing is that each of these lenses can be different," says Herkommer.

GM pork pies could be on sale in the UK in just 10 years. Scientists have created a new breed of super pigs resistant to a killer virus that could make the GM meat common in Britain, despite fears over'Frankenstein food'. Scientists at Roslin Institute, which cloned Dolly the Sheep, have used gene-editing to protect pigs from a respiratory condition which costs the UK farming industry millions every year. Scientists at Scotland's Roslin Institute used gene-editing techniques to create GM pigs potentially resilient to the virus Porcine Reproductive and Respiratory Syndrome (PRRS) which costs the industry billions each year Experts trying to combat world hunger are already calling for the British Government to back the use of GM farm animals. The US has paved the way by approving a salmon engineered to grow twice as fast as normal, which less than 18 months ago became the first GM animal ruled safe to eat.

Shortly after walking through the door at Face, a Chinese startup valued at roughly a billion dollars, I see my face, unshaven and looking a bit jet-lagged, flash up on a large screen near the entrance. Having been added to a database, my face now provides automatic access to the building. It can also be used to monitor my movements through each room inside. As I tour the offices of Face (pronounced "face plus plus"), located in a suburb of Beijing, I see it appear on several more screens, automatically captured from countless angles by the company's software. On one screen a video shows the software tracking 83 different points on my face simultaneously.

Alphabet Jigsaw (formerly Google Ideas) officially unveiled this morning their new tool for fighting toxic speech online, appropriately called Perspective. Powered by a deep learning model trained on more than 17 million manually reviewed reader comments provided by the New York Times, the model assigns a score to a given passage of text, rating it on a scale from 0 to 100% similar to statements that human reviewers have previously rated as "toxic." What makes this new approach from Google so different than past approaches is that it largely focuses on language rather than ideas: for the most part you can express your thoughts freely and without fear of censorship as long as you express them clinically and clearly, while if you resort to emotional diatribes and name calling, regardless of what you talk about, you will be flagged. What does this tell us about the future of toxic speech online and the notion of machines guiding humans to a more "perfect" humanity? One of the great challenges in filtering out "toxic" speech online is first defining what precisely counts as "toxic" and then determining how to remove such speech without infringing on people's ability to freely express their ideas.

The goal of this tutorial is to show how to use TensorFlow to train and evaluate a simple feed-forward neural network for handwritten digit classification using the (classic) MNIST data set. The intended audience for this tutorial is experienced machine learning users interested in using TensorFlow. These tutorials are not intended for teaching Machine Learning in general. Please ensure you have followed the instructions to install TensorFlow. Simply run the fully_connected_feed.py file directly to start training: MNIST is a classic problem in machine learning.

MOOC Video Collection 777 views Decision Theory: Utility Functions - Stanford University - Duration: 18:16.

When you spend a lot of time with someone, their characteristics can rub off on you. But what happens when that someone is a robot? As artificial intelligence systems become increasingly human, their abilities to influence people also improve. New Scientist reports that children who spend time with a robotic companion appear to pick up elements of its behavior. New experiments suggest that when kids play with a robot that's a real go-getter, for instance, the child acquires some of its unremitting can-do attitude.

IBM surveyed more than 600 decision-makers about their cognitive initiatives and 62 percent of respondents stated that the results of their cognitive implementations exceed expectations*. Cognitive services, like those offered byIBM Watson, can help you find out how your customers feel and help you predict what they might do. With Watson, IBM is pioneering the development of models that can tell you about different and often hidden, aspects of an individual. These insights can then be used by an organization to deepen relationships, shape initiatives and drive innovation. REST APIs, like Watson Personality Insights and Watson Emotion Analysis, allow organizations to learn about an individual's: Organizations can now train apps to quickly analyze and interpret large volumes of unstructured sensory data.