Entity Alignment (EA) aims to find equivalent entities between two Knowledge Graphs (KGs). While numerous neural EA models have been devised, they are mainly learned using labelled data only. In this work, we argue that different entities within one KG should have compatible counterparts in the other KG due to the potential dependencies among the entities. Making compatible predictions thus should be one of the goals of training an EA model along with fitting the labelled data: this aspect however is neglected in current methods. To power neural EA models with compatibility, we devise a training framework by addressing three problems: (1) how to measure the compatibility of an EA model; (2) how to inject the property of being compatible into an EA model; (3) how to optimise parameters of the compatibility model. Extensive experiments on widely-used datasets demonstrate the advantages of integrating compatibility within EA models. In fact, state-of-the-art neural EA models trained within our framework using just 5\% of the labelled data can achieve comparable effectiveness with supervised training using 20\% of the labelled data.

Is it finally the year for the rise of the machines? Until not too long ago, AI was just an overused marketing term. Many software vendors who sold solutions based on algorithms and fancy regular expressions branded their stuff as artificial intelligence, even though it wasn't. Times have changed, and the market is--in a helicopter view--divided into two camps: vendors who use a predefined AI framework and vendors who create their own. I'm not looking into the pros and cons of each, but what does this mean for the users?

PNY Technologies, a global provider of components and solutions for the artificial intelligence and high-performance computing market, has sealed a European distribution agreement with data storage firm NetApp. NetApp and PNY are teaming up to provide customers with systems featuring NVIDIA accelerated AI computing. PNY works with partners across all distribution channels, including wholesalers, workstation system integrators, NVIDIA Partner Network members and value-added resellers. The collaboration with NetApp allows PNY to strengthen its global AI offerings with complete clusters that meet the requirements of high-performance AI. "Companies and organisations of all sizes and across many industries are turning to artificial intelligence, machine learning and deep learning to solve real-world problems, deliver innovative products and services, and to get an edge in an increasingly competitive marketplace," said Kristian Kerr, channel vice president for EMEA at NetApp. "As organisations increase their use of AI, ML and DL, they face many challenges, including workload scalability and data availability. With PNY, NVIDIA and NetApp, our partners have the support of the very best in the market and can comfortably position themselves in this long-term growth sector of the industry."

Adapters are light-weight modules that allow parameter-efficient fine-tuning of pretrained models. Specialized language and task adapters have recently been proposed to facilitate cross-lingual transfer of multilingual pretrained models (Pfeiffer et al., 2020b). However, this approach requires training a separate language adapter for every language one wishes to support, which can be impractical for languages with limited data. An intuitive solution is to use a related language adapter for the new language variety, but we observe that this solution can lead to sub-optimal performance. In this paper, we aim to improve the robustness of language adapters to uncovered languages without training new adapters. We find that ensembling multiple existing language adapters makes the fine-tuned model significantly more robust to other language varieties not included in these adapters. Building upon this observation, we propose Entropy Minimized Ensemble of Adapters (EMEA), a method that optimizes the ensemble weights of the pretrained language adapters for each test sentence by minimizing the entropy of its predictions. Experiments on three diverse groups of language varieties show that our method leads to significant improvements on both named entity recognition and part-of-speech tagging across all languages.

LONDON--(BUSINESS WIRE)--Deep Instinct, the first and only cybersecurity company to apply end-to-end deep learning to predict, identify, and prevent cyberattacks, is continuing its strategic expansion into EMEA, contracting with T-Systems (Poland), one of the region's largest IT services providers, to utilize and distribute Deep Instinct's protection to its customers. Deep Instinct also signed strategic partnership agreements with Cyber Monks and Spinnakar to distribute Deep Instinct's deep learning-based solution across the region. Leading Deep Instincts' EMEA expansion is Brooks Wallace, VP Sales EMEA, a veteran cybersecurity sales leader with over 20 years of experience in building sales teams. Wallace will oversee the newly opened sales and support office in the UK and forge additional strategic partnerships with MSSPs across the region. "Our expansion into EMEA comes at a critical time for the region, and contracting with T-Systems Poland attests to the unique value of our deep learning-based cyber-attack prevention solution," said Guy Caspi, CEO and Co-founder of Deep Instinct.

AI and advanced analytics can have a transformational impact on every aspect of a business, from the contact centre or supply chain to the overall business strategy. With the new challenges caused by coronavirus, companies are in a growing need of more advice, more data and visibility to minimise the business impact of the virus. However, long before the disruption caused by Covid-19, data was recognised as an essential asset in delivering improved customer service. And yet, businesses of all sizes have continued to struggle with gaining more tangible value from their vast hoards of data to improve the employee and customer experience. Data silos, creaking legacy systems and fast-paced, agile competitors have made the need to harness an organisations data to drive value of paramount importance.

Daniel heads up Product Management for RingCentral in EMEA. He has spent his career at RingCentral driving the expansion of cloud Business Communication solutions in the UK and EMEA via the adoption of exciting, innovative features and services as well as their integration into customers' workflows. He has spent the last 8 years in the cloud software and communication industries and his background is in software development and telecoms infrastructure.

Imagine a constantly evolving and evasive cyberthreat that could target individuals and organisations remorselessly. This is the reality of cybersecurity in an era of artificial intelligence (AI). AI has shaken up the cybersecurity industry, with automated threat prevention, detection and response revolutionising one of the fastest growing sectors in the digital economy. Hackers are using AI to speed up polymorphic malware, causing it to constantly change its code so it can't be identified However, as is so often the case, there's a dark side. What if cybercriminals get their hands on AI, and use it against public and private sector organisations? "The edge in cyberdefence is speed.

The software is designed to meet these needs by offering businesses of all sizes access a wide range of anonymised Experian data in combination with industry-leading analytical tools and expert consultancy support, allowing users to identify the key insights that will best serve their customers and drive growth. Francesco Nazzarri, MD of Commercial Strategy for Experian EMEA, says: "To effectively compete in today's economy, businesses must be able to quickly use and understand a vast range of data assets, to move at speed with their market and consistently deliver outcomes that best serve their customers. It quickly converts vast quantities of data into smart, actionable insights by leveraging the latest analytical innovation, machine learning and artificial intelligence tools. Ascend is an important part of our suite of market-leading products, all of which will accelerate the ability of business to harness big data's full potential. Better still, Ascend can seamlessly integrate with PowerCurve, our leading decisioning and workflow software solution.

John started his career in the Financial Services sector with Sun Microsystems, managing global relationships with JP Morgan & Bank of America. He went on to spend 10 years at NetApp, leading global FSI teams and then two years at Juniper Networks, as EMEA Sales Director for FSI. Today, he is responsible for NVIDIA's FSI business in EMEA, along with the newly announced RAPIDS machine learning business in EMEA.