AI research on photo quality could work wonders for medical imaging

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Researchers have shown that they can use artificial intelligence (AI) to restore low-quality photos by exposing a neural network to only other low-quality photos, according to work presented at the International Conference on Machine Learning in Stockholm. The research was developed by representatives from Nvidia, a Santa Clara, California-based technology company, the Massachusetts Institute of Technology in Cambridge, Massachusetts, and Aalto University in Greater Helsinki, Finland. The team was able to clean up "grainy" photos by using deep learning to train the neural network with more than 50,000 example images, as explained in a news release from Nvidia. As the authors explained, their work has potential to be used in numerous industries, including radiology. "There are several real-world situations where obtaining clean training data is difficult: low-light photography (e.g., astronomical imaging), physically-based rendering, and magnetic resonance imaging," wrote author Jaakko Lehtinen, an associate professor at Aalto University, and colleagues.


ServiceNow To Acquire Cognitive Search Capabilities of Attivio FinSMEs

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ServiceNow (NYSE: NOW), a Santa Clara, California-based provider of a cloud‑based platform and solutions that deliver digital workflows that improve experiences and productivity for employees and the enterprise, is to acquire the cognitive search capabilities of Attivio, an AI-powered answers and insights platform company based in Boston, MA. As part of the transaction, whose amount was not disclosed, select Attivio R&D employees will join ServiceNow's engineering team. With the addition of Attivio's search engine, ServiceNow will move beyond keyword-based search to deliver conversational AI and search experiences at scale to customers. Attivio's AI-powered search capabilities will help ServiceNow better understand the meaning behind natural language searches on the Now Platform to deliver relevant, personalized results that users can act on right from the search results window. By integrating Attivio into the Now Platform, the company plans to enhance search natively across its IT, Customer and Employee workflows through the ServiceNow Service Portal, Now Mobile app and Virtual Agent chatbot solution.


Efficient Graph-based Word Sense Induction

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The paper was first presented at TextGraphs-2018, a workshop series at The 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) on June 6, 2018 in New Orleans. This new approach to word-sense induction comes from the work of the Lexalytics Magic Machines AI Labs, launched in 2017 in partnership with the University of Massachusetts Amherst's Center for Data Science and Northwestern University's Medill School of Journalism, Media and Integrated Marketing Communications to drive innovation in AI. Word sense induction (WSI) is a challenging task of natural language processing whose goal is to categorize and identify multiple senses of polysemous words from raw text without the help of predefined sense inventory like WordNet (Miller, 1995). The problem is sometimes also called unsupervised word sense disambiguation (Agirre et al., 2006; Pelevina et al., 2016). An effective WSI has wide applications.


Qlik acquires Podium Data as BI and Big Data coalesce

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QlikTech International AB (Qlik), a foundational self-service business intelligence (BI) player based in Radnor, Pennsylvania, today announced its acquisition of Lowell, Massachusetts-based data management startup Podium Data. The deal closed on Friday and was announced to employees yesterday. The two companies made the deal public today. The tie-up gives Qlik serious data preparation, data quality and data catalog capabilities to add to its hallmark visual data discovery and analytics offering. Once tighter integration is achieved, the deal will effectively transform Qlik into an end-to-end data platform.


Is GOLAP the Next Wave for Big Data Warehousing?

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The 1990s and 2000s saw the rise of the relational databases for transaction processing (OLTP) as well as analytical processing (OLAP). As the volume and variety of data explodes in the 2010s, database experts are looking to parallel graph analytic database– what some are calling GOLAP data warehouses -- to enable the timely extraction of insights. The biggest proponent of the GOLAP data warehouse concept at this point is Cambridge Semantics, the company behind the Anzo data lake offering. The Boston, Massachusetts company's GOLAP offering includes a parallel graph database, called Anzo Graph, along with a collection of tools that help to automate core functions, like data modeling, transformation, and query-building (the rest of the Anzo suite). Barry Zane, the vice president of engineering for Cambridge Semantics (via its SPARQL City acquisition), knows a thing or two about developing parallel databases.