We present discriminative Gaifman models, a novel family of relational machine learning models. Gaifman models learn feature representations bottom up from representations of locally connected and bounded-size regions of knowledge bases (KBs). Considering local and bounded-size neighborhoods of knowledge bases renders logical inference and learning tractable, mitigates the problem of overfitting, and facilitates weight sharing. Gaifman models sample neighborhoods of knowledge bases so as to make the learned relational models more robust to missing objects and relations which is a common situation in open-world KBs. We present the core ideas of Gaifman models and apply them to large-scale relational learning problems. We also discuss the ways in which Gaifman models relate to some existing relational machine learning approaches.

The Smart Data Analytics group is very happy to announce SANSA 0.1 – the initial release of the Scalable Semantic Analytics Stack. SANSA combines distributed computing and semantic technologies in order to allow powerful machine learning, inference and querying capabilities for large knowledge graphs. You can find the FAQ and usage examples at http://sansa-stack.net/faq/. We want to thank everyone who helped to create this release, in particular, the projects Big Data Europe, HOBBIT and SAKE.

The piano ditty below, which ascends jauntily, then finishes with a tuneful flourish, sounds a bit like a jingle composed for the latest toothpaste campaign. The tune was, in fact, dreamed up by a musical AI program developed at Google. And the program's latest compositions show how combining a powerful machine-learning approach with simple musical rules can produce creative works that sound remarkably human. Music composition is an enigmatic form of human creativity. Songwriting programs already exist, but they typically follow a specific set of rules, and they tend to produce tunes that feel rigid and mechanical.

The Obama administration has failed to renegotiate portions of an international arms control arrangement so that it's simpler to export tools related to hacking and surveillance software.

Redescription mining is a field of knowledge discovery that aims at finding different descriptions of similar subsets of instances in the data. These descriptions are represented as rules inferred from one or more disjoint sets of attributes, called views. As such, they support knowledge discovery process and help domain experts in formulating new hypotheses or constructing new knowledge bases and decision support systems. In contrast to previous approaches that typically create one smaller set of redescriptions satisfying a pre-defined set of constraints, we introduce a framework that creates large and heterogeneous redescription set from which user/expert can extract compact sets of differing properties, according to its own preferences. Construction of large and heterogeneous redescription set relies on CLUS-RM algorithm and a novel, conjunctive refinement procedure that facilitates generation of larger and more accurate redescription sets. The work also introduces the variability of redescription accuracy when missing values are present in the data, which significantly extends applicability of the method. Crucial part of the framework is the redescription set extraction based on heuristic multi-objective optimization procedure that allows user to define importance levels towards one or more redescription quality criteria. We provide both theoretical and empirical comparison of the novel framework against current state of the art redescription mining algorithms and show that it represents more efficient and versatile approach for mining redescriptions from data.

If a voice inquiry, Siri invokes a speech-to-text mechanism that transcribes what has been spoken to text. This is very difficult, by the way, particularly given the number of languages in which Siri works.

Job Title: Senior Software Engineer - NLP & Machine Learning Artigen Corporation is seeking a Senior Software Engineer/Team Lead NOTE: This is a Hands on position, Software Development, Design, Framework, Programming etc. NO OPT, NO Sponsorship, No relocation assistance, Local applicants ONLY, Face-to-face interview required. Artigen is a software development company that intends to specialize in enabling Artificial Intelligence software integration with e-commerce site and back end network operations. We are looking for Senior Team Lead/Software Engineer specifically in the development of software encompassing Artificial Intelligence, Machine Learning, Natural Language Processing to develop a virtual agent for Artigen's platform of software and services for Global B2B/B2C clients/customers. The role will develop and mentor a software team dedicated to Ai and cognitive technologies, utilizing existing technologies (such as IBM Watson API, Google's Nuance, Apple's Siri, other open source upcoming platforms such as Viv) and platforms to develop and customize a platform for Artigen's own Ai platform. This role will also require hands-on complex programming in various languages, platforms and must understand and develop machine learning algorithms, data integration and manipulation.

Last month, at the world's leading general management conference--the Drucker Forum in Vienna Austria--Julian Birkinshaw, Professor of Strategy and Entrepreneurship at the London Business School and Director of the Deloitte Institute of Innovation and Entrepreneurship, declared provocatively that we are living in "the Age of Agile." The full text of Julian's important talk is set out below. He also has a new book coming out next year entitled Fast/Forward: Make Your Company Fit For the Future. Earlier this week, I discussed these issues with Julian. Steve Denning: In your talk, you spoke about three possible forms of organization: bureaucracy, meritocracy and adhocracy. Could you tell us more? Julian Birkinshaw: Bureaucracy is about and occupying roles and following rules. Meritocracy is a knowledge-based view of the organization, including big data and analytics. Adhocracy is an action-based view of the organization focused on capturing opportunities, solving problems and getting results. Denning: Where does "the age of Agile" fit into this scheme?

Uber released a new set of rules for passengers on Thursday, banning vandalism, "vomiting due to excessive alcohol consumption" and flirting. It is the first time Uber has published specific guidelines for passengers. The rules set out specific examples of unacceptable behaviour, and people flouting the rules could be permanently banned from the service. "Most riders show drivers the respect they deserve," the company said in a statement. "But some don't – whether it's leaving trash in the car, throwing up in the back seat after too much alcohol or asking a driver to break the speed limit so they can get to their appointment on time. Some of the guidelines relate to sexual misconduct. There have been a number of cases where Uber drivers have been accused of rape and sexual assault since its inception. While setting out rules for passenger-driver interactions, some of the guidelines appear to be aimed at people using UberPool – the money-saving service where separate passengers are collected and dropped off at different locations in the same car. "Don't touch or flirt with other people in the car," the rules state. Drivers are also banned from flirting. "As a reminder, Uber has a no sex rule.

We propose the Gaussian attention model for content-based neural memory access. With the proposed attention model, a neural network has the additional degree of freedom to control the focus of its attention from a laser sharp attention to a broad attention. It is applicable whenever we can assume that the distance in the latent space reflects some notion of semantics. We use the proposed attention model as a scoring function for the embedding of a knowledge base into a continuous vector space and then train a model that performs question answering about the entities in the knowledge base. The proposed attention model can handle both the propagation of uncertainty when following a series of relations and also the conjunction of conditions in a natural way. On a dataset of soccer players who participated in the FIFA World Cup 2014, we demonstrate that our model can handle both path queries and conjunctive queries well.