Another significant proposal, which got McIlroy's attention, was how to drop. The goal was to get the ball back in play quickly. Modern rules would more easily identify where to drop, and players would only have to hold the ball above the ground without it touching anything. The recommendation is at least 1 inch above the ground or grass. Currently, players have to stand upright and hold the ball at shoulder height and arm's length from their bodies.

OSAKA – Osaka Gov. Ichiro Matsui said Tuesday the prefecture relaxed regulations regarding the approval system for opening schools after nationalist private kindergarten operator Moritomo Gakuen requested it, but denied the company influenced the local government's decision. "Compared to other Kansai area prefectures, the hurdles (to run private schools) in Osaka were quite high," Matsui said, adding the reason for the decision was to attract more schools. In April 2012, a few months after Matsui became Osaka's governor, the prefecture relaxed regulations. Nearly six months earlier in September 2011, Moritomo Gakuen head Yasunori Kagoike, who wanted to build an elementary school despite financial difficulties that might have disqualified it from getting prefectural approval, asked the Osaka to ease the rules. Moritomo Gakuen has been under fire recently following revelations of a questionable land deal and for distributing anti-Chinese and anti-Korean literature at its kindergarten.

This post is the second in a series of three posts, each of which discuss the fundamental concepts of Artificial Intelligence. In our first post we discussed AI definitions, helping our readers to understand the basic concepts behind AI, giving them the tools required to sift through the many AI articles out there and form their own opinion. In this second post, we will discuss several notions which are important in understanding the limits of AI. Figure 1: How intelligent can Artificial Intelligence get? When we speak about how far AI can go, there are two "philosophies": strong AI and weak AI. The most commonly followed philosophy is that of weak AI, which means that machines can manifest certain intelligent behavior to solve specific (hard) tasks, but that they will never equal the human mind.

Commonsense reasoning at scale is a critical problem for modern cognitive systems. Large theories have millions of axioms, but only a handful are relevant for answering a given goal query. Irrelevant axioms increase the search space, overwhelming unoptimized inference engines in large theories. Therefore, methods that help in identifying useful inference paths are an essential part of large cognitive systems. In this paper, we use retrograde analysis to build a database of proof paths that lead to at least one successful proof. This database helps the inference engine identify more productive parts of the search space. A heuristic based on this approach is used to order nodes during a search. We study the efficacy of this approach on hundreds of queries from the Cyc KB. Empirical results show that this approach leads to significant reduction in inference time.

Keyphrases that efficiently summarize a document’s content are used in various document processing and retrieval tasks. Current state-of-the-art techniques for keyphrase extraction operate at a phrase-level and involve scoring candidate phrases based on features of their component words.In this paper, we learn keyphrase taggers for research papers using token-based features incorporating linguistic, surface-form, and document-structure information through sequence labeling. We experimentally illustrate that using within document features alone, our tagger trained with ConditionalRandom Fields performs on-par with existing state-of-the-art systems that rely on information from Wikipedia and citation networks. In addition, we are also able to harness recent work on feature labeling to seamlessly incorporate expert knowledge and predictions from existing systems to enhance the extraction performance further. We highlight the modeling advantages of our keyphrase taggers and show significant performance improvements on two recently-compiled datasets of keyphrases from Computer Science research papers.

Evolution of Knowledge Bases (KBs) consists of incorporating new information in an existing KB. Previous studies assume that the new information should be fully trusted and thus completely incorporated in the old knowledge. We suggest a setting where the new knowledge can be partially trusted and develop model-based approaches (MBAs) to KB evolution that rely on this assumption. Under MBAs the result of evolution is a set of interpretations and thus two core problems for MBAs are closure, i.e., whether evolution result can be axiomatised with a KB, and approximation, i.e., whether it can be (maximally) approximated with a KB. We show that DL-Lite is not closed under a wide range of trust-sensitive MBAs. We introduce a notion of s-approximation that improves the previously proposed approximations and show how to compute it for various trust-sensitive MBAs.

Knowledge base(KB) plays an important role in artificial intelligence. Much effort has been taken to both manually and automatically construct web-scale knowledge bases. Comparing with manually constructed KBs, automatically constructed KB is broader but with more noises. In this paper, we study the problem of improving the quality for automatically constructed web-scale knowledge bases, in particular, lexical taxonomies of isA relationships. We find that these taxonomies usually contain cycles, which are often introduced by incorrect isA relations. Inspired by this observation, we introduce two kinds of models to detect incorrect isA relations from cycles. The first one eliminates cycles by extracting directed acyclic graphs, and the other one eliminates cycles by grouping nodes into different levels. We implement our models on Probase, a state-of-the-art, automatically constructed, web-scale taxonomy. After processing tens of millions of relations, our models eliminate 74 thousand wrong relations with 91% accuracy.

Artificial Intelligence (AI) has captured the attention of C-suite executives across all industries and is poised to transform businesses in ways we've never seen since the impact of computer technology in the late 20th century. We are already seeing venture capitalists funding AI start-ups at a rapid pace. Technology companies are also moving swiftly to create and capture value in this emerging area. High-profile acquisitions by Google, Apple and Facebook are piquing interest in Artificial Intelligence technologies such as robotics, expert systems, computer vision, speech, gesture and facial recognition. Companies are creating new research labs devoted to innovating with these technologies. In Africa, AI has a strong role to play.

Using analogical model formulation with sketches to solve Bennett Mechanical Comprehension Test problems.