Relation Extraction (RE), the task of detecting and characterizing semantic relations between entities in text, has gained much importance in the last two decades, mainly in the biomedical domain. Many papers have been published on Relation Extraction using supervised machine learning techniques. Most of these techniques rely on statistical methods, such as feature-based and tree-kernels-based methods. Such statistical learning techniques are usually based on a propositional hypothesis space for representing examples, i.e., they employ an attribute-value representation of features. This kind of representation has some drawbacks, particularly in the extraction of complex relations which demand more contextual information about the involving instances, i.e., it is not able to effectively capture structural information from parse trees without loss of information. In this work, we present OntoILPER, a logic-based relational learning approach to Relation Extraction that uses Inductive Logic Programming for generating extraction models in the form of symbolic extraction rules. OntoILPER takes profit of a rich relational representation of examples, which can alleviate the aforementioned drawbacks. The proposed relational approach seems to be more suitable for Relation Extraction than statistical ones for several reasons that we argue. Moreover, OntoILPER uses a domain ontology that guides the background knowledge generation process and is used for storing the extracted relation instances. The induced extraction rules were evaluated on three protein-protein interaction datasets from the biomedical domain. The performance of OntoILPER extraction models was compared with other state-of-the-art RE systems. The encouraging results seem to demonstrate the effectiveness of the proposed solution.

Many people may be familiar with the frustration of calling up their smartphone's speech-to-text function to dictate an email, only to find that it won't work because the phone isn't connected to the internet. Now, a new generation of edge artificial intelligence (AI) chips is set to reduce those frustrations by bringing the AI to the device.1 We predict that in 2020, more than 750 million edge AI chips--chips or parts of chips that perform or accelerate machine learning tasks on-device, rather than in a remote data center--will be sold. This number, representing a cool US$2.6 billion in revenue, is more than twice the 300 million edge AI chips Deloitte predicted would sell in 20172--a three-year compound annual growth rate (CAGR) of 36 percent. Further, we predict that the edge AI chip market will continue to grow much more quickly than the overall chip market. By 2024, we expect sales of edge AI chips to exceed 1.5 billion, possibly by a great deal.3 This represents annual unit sales growth of at least 20 percent, more than double the longer-term forecast of 9 percent CAGR for the overall semiconductor industry.4 These edge AI chips will likely find their way into an increasing number of consumer devices, such as high-end smartphones, tablets, smart speakers, and wearables.

WIRE)--Cambridge Mobile Telematics (CMT), the world's leading mobile telematics and analytics provider, has launched its latest product line, Claims Studio. Through a lightweight smartphone solution, Claims Studio gives claims adjusters access to robust, unbiased telematics and contextual crash data after an impact occurs. CMT's ability to detect crashes has been in the market since 2015, but now has expanded to support the end-to-end claims process. Claims Studio uses telematics and artificial intelligence to reproduce the true story of a crash, creating a data-driven narrative to accelerate the claims process. By accessing key details like speed, severity, and vehicle impact location early in the process, insurers can spend less time collecting information from drivers and third parties, and more time confirming facts and accurately assessing loss.

Do you remember what Steve Jobs said about'making a dent in the universe?' Well, the way Artificial Intelligence and Big Data are improving lives it seems it would be much easier to do so with these technologies. Be it fraud prevention, automation, security, banking, and now forecasting climate change, AI and data-driven technologies are making rapid progress. Take the finance sector, for instance, AI has been serving it for years by automating and streamlining the customer experience. Additionally, AI-driven identity verification systems are detecting fraud, eliminating fraudsters, and helping banks through automation.

Dimensionality reduction is an important operation in information visualization, feature extraction, clustering, regression, and classification, especially for processing noisy high dimensional data. However, most existing approaches preserve either the global or the local structure of the data, but not both. Approaches that preserve only the global data structure, such as principal component analysis (PCA), are usually sensitive to outliers. Approaches that preserve only the local data structure, such as locality preserving projections, are usually unsupervised (and hence cannot use label information) and uses a fixed similarity graph. We propose a novel linear dimensionality reduction approach, supervised discriminative sparse PCA with adaptive neighbors (SDSPCAAN), to integrate neighborhood-free supervised discriminative sparse PCA and projected clustering with adaptive neighbors. As a result, both global and local data structures, as well as the label information, are used for better dimensionality reduction. Classification experiments on nine high-dimensional datasets validated the effectiveness and robustness of our proposed SDSPCAAN.

For months, Australia has been ravaged by bushfires. These massive fires, fueled by lengthy droughts and high temperatures, have caught the world's attention as lives have been lost and entire towns have been evacuated. But even as the firefighters push back against the flames, another kind of flame war has emerged: the debate over whether or not changes in the world's climate are responsible for the devastating scale of the bushfires. As it turns out, deep learning techniques may have already provided some crucial answers. A few years ago, the seemingly increasing frequency of Australian bushfires had already caught the attention of a group of Tasmanian researchers.

This research study is anticipated to help the new and existing key players in the market that will help in making current business decisions as well as to sustain in the severe competition of the global artificial intelligence for edge devicesmarket. The artificial intelligence for edge devices market report provides a database which pertains to the current and contemporary discovery and the new technology which has been induced in the artificial intelligence for edge devices market, thereby helping the investors to understand the impact of these on the market future development.

The Artificial Intelligence For Healthcare Applications Market recently Published Global Market look into study with in excess of 100 industry enlightening work area and Figures spread through Pages and straightforward itemized TOC on "Artificial Intelligence For Healthcare Applications Market". The report provides information and the advancing business series information in the sector to the exchange. The report gives an idea associated with the advancement of this market development of significant players of this industry. An examination of this Artificial Intelligence For Healthcare Applications relies upon aims, which are of coordinated into market analysis, is incorporated into the reports. The global Artificial Intelligence For Healthcare Applications market is expected to grow at a CAGR of 43.5% from 2018 to reach USD 27.60 billion by 2025. Artificial intelligence (AI) in healthcare is the use of complex algorithms and software to emulate human cognition in the analysis of complicated medical data.

A mathematical study claims to have proven the long-held belief that the Battle of Britain could have easily been won by the Germans if not for tactical ineptitude. University of York researchers have created a computer model that uses a statistical technique called'weighted bootstrapping' to re-imagine the 1940 battle under different circumstances. It identifies two enormous blunders by notorious Nazi commander Hermann Goering - a trained fighter pilot - who led the assault that crippled the Nazi effort and helped Britain win. The researchers say it provides statistical backing to many historians' belief that if Germany had launched an attack immediately after Winston Churchill's famous'Battle of Britain' speech on June 18, rather than three weeks later on July 10, and targeted airfields rather than cities and populated areas, the Nazis would probably have been victorious. This would have crippled the British response by decimating the number of fighter pilots and destroying vital radar systems used to track German planes, paving the way for a naval and land invasion.

With each passing year, the fintech sector is providing faster, flexible and secured consumer experience, and is protecting against the risks and vulnerabilities of traditional insurance and loans. In fact, the global fintech market size is expected to grow to $124.3 billion by the end of 2025 at a CAGR of 23.8%. With a vision of providing small and medium-sized growth companies with debt finance, along with aiding them in competing against the large corporations, UK-based OakNorth is utilising artificial intelligence and machine learning to fulfil the dream. Since its inception, OakNorth has secured over $1 billion from leading investors which has been used to launch lending operations and others such. After meeting each other at college in the year 2002, Khosla and Perlman decided to launch their own business that could solve the challenges they had to face in securing debt finance from high street banks during their previous business -- Copal Amba which scaled to 3,000 employees and was later acquired by Moody's Corporation in 2014.