Knowledge bases are useful resources for many natural language processing tasks, however, they are far from complete. In this paper, we define a novel entity representation as a mixture of its neighborhood in the knowledge base and apply this technique on TransE-a well-known embedding model for knowledge base completion. Experimental results show that the neighborhood information significantly helps to improve the results of the TransE model, leading to better performance than obtained by other state-of-the-art embedding models on three benchmark datasets for triple classification, entity prediction and relation prediction tasks.

Knowledge bases of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks. However, because knowledge bases are typically incomplete, it is useful to be able to perform link prediction or knowledge base completion, i.e., predict whether a relationship not in the knowledge base is likely to be true. This paper combines insights from several previous link prediction models into a new embedding model STransE that represents each entity as a low-dimensional vector, and each relation by two matrices and a translation vector. STransE is a simple combination of the SE and TransE models, but it obtains better link prediction performance on two benchmark datasets than previous embedding models. Thus, STransE can serve as a new baseline for the more complex models in the link prediction task.

Artificial intelligence experts point to looming danger amid unpredictable technology and fears that technology could'seduce us into warfare' Experts in artificial intelligence, lawyers and activists organized by the Campaign to Stop Killer Robots gathered at the United Nations on Tuesday to warn against a growing reliance on cheap drones and "stupid AI" that can be unpredictable in the real world. "Terminator always comes up," Toby Walsh, a professor of artificial intelligence at the University of New South Wales, told reporters on Tuesday, referring to the sci-fi cyborg on a mission to wipe out mankind. "But it's not really Terminator that we're worried about at the moment. I think that Terminator is perhaps 50 or so years away." But there are concerning technologies "only a few years, at best, away", Walsh said, and with semi-autonomous systems, such as drones, "it would take very little to remove the human from that loop and replace them with a computer".

Nearly two-thirds of consumers are worried about home IoT devices listening in on their conversations, according to a Gartner survey released Monday. Those jitters aren't too surprising after recent news items about TV announcers inadvertently activating viewers' Amazon Echos, or about data from digital assistants being used as evidence in criminal trials. But privacy concerns are just one hurdle smart homes still have to overcome, according to the survey. In fact, Gartner found that most consumers don't feel they need what smart homes offer. Consumer IoT is still in an early-adopter phase, Gartner concluded from the online survey, which was conducted in the second half of last year in the U.S., U.K., and Australia.

In my last blog, I discussed how digital complacency could put your business at risk and how technological change is fast outpacing businesses' ability to adapt. This trend continues its relentless pursuit to consume those who choose to answer today's questions with yesterday's thinking. Furthermore, yet another transformational technology is now redefining the industry, and it has the potential to change everything. Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data.

Kaplan Professional has announced it acquired an artificial intelligence company in an effort to create new tools for financial advisers amid a changing environment. In a new announcement yesterday, Kaplan said it has now acquired Red Marker. Kaplan Australia and New Zealand managing director Rob Regan said the acquisition would enable Kaplan to broaden its compliance solutions to the financial services sector through artificial intelligence. "We are investing in real-time learning with the aim of further equipping advisers with the tools they need to maximise their success in a rapidly changing landscape," he said. "By enabling advisers to confidently embrace compliance, it will allow them to continually increase their knowledge in a reportable manner. "Adding Red Marker to the Kaplan portfolio will accelerate the development of personalised and adaptive learning engines in the financial services sector." Red Marker principals and staff will continue with the business, the statement said. Red Marker managing director Matt Symons said, "Our clients are really enthusiastic about Artemis.

Google is about to make more Androids even smarter. The tech giant is rolling out its Google Assistant to devices running on the Nougat and Marshmallow versions of its Android software. The firm first revealed the expansion at the Mobile World Congress and users in the US will be the first to receive the update – Australia, Canada, the UK and Germany are set to follow. Google is rolling out its Google Assistant to devices running on Nougat and Marshmallow. The firm first revealed the expansion at the Mobile World Congress and users in the US will be the first to receive the update – Australia, Canada, the UK and Germany are set to follow. Google has yet to reveal a detailed list of Android devices that are eligible to download Google Assistant.

Beginning today, Google Assistant is opening up to new devices around the world. Both Nougat and Marshmallow-powered phones are gaining access to the artificial intelligence platform. Google is making the feature live in select countries to start, so you may not get to use Google Assistant for a couple of days, weeks, and or even months. The first set of people get it are English-speaking users in the United States. After that, English-speaking users in Australia will be next.

On Wednesday, amid neon lights and smoke machines (but no mirrors) Mr Meij, a former pizza delivery driver who is estimated to be worth at least $130 million, unveiled Domino's latest high-tech weapons. After unveiling a stream of disruptive digital initiatives over the past six years – including mobile phone apps, GPS driver tracking, SMS ordering, electronic bikes, robotic pizza delivery vehicles and drones – Domino's is now harnessing the power of artificial intelligence to encourage Australian and New Zealand consumers to eat more pizza. Over the next week Domino's will add new features and devices to its DRU (Domino's Robotic Unit) platform, starting with a beta version of DRU Assist, a virtual, voice-activated digital assistant which takes customers' orders on mobile phones, desktops and laptops.

In this paper we introduce a new classification algorithm called Optimization of Distributions Differences (ODD). The algorithm aims to find a transformation from the feature space to a new space where the instances in the same class are as close as possible to one another while the gravity centers of these classes are as far as possible from one another. This aim is formulated as a multiobjective optimization problem that is solved by a hybrid of an evolutionary strategy and the Quasi-Newton method. The choice of the transformation function is flexible and could be any continuous space function. We experiment with a linear and a non-linear transformation in this paper. We show that the algorithm can outperform 6 other state-of-the-art classification methods, namely naive Bayes, support vector machines, linear discriminant analysis, multi-layer perceptrons, decision trees, and k-nearest neighbors, in 12 standard classification datasets. Our results show that the method is less sensitive to the imbalanced number of instances comparing to these methods. We also show that ODD maintains its performance better than other classification methods in these datasets, hence, offers a better generalization ability.