SAN FRANCISCO (Reuters) - Microsoft Corp said on Monday it was looking into whether Russians bought U.S. election ads on its Bing search engine or on other Microsoft-owned products and platforms, after rival Google said it had discovered such ads on its products.

Content marketing lies at the intersection of art and technology, so it's only logical that artificial intelligence comes into play. AI plays a big role in search and it can be useful in creating content, too. While it's not known exactly how the Google algorithm works, researchers and marketers have conducted experiments that make a strong argument for topic modeling as a key component of the Hummingbird algorithm. Also, Wired reported last year about how Google's search engine now uses artificial intelligence to rank content. A basic understanding of how search engines interpret text and its quality can help you develop a strategy that produces consistently high-ranking content because topic modeling – done manually or with software – allows you to create blueprints for the best possible content.

When is milk not milk? This is no trick question -- it's a distinction that artificial intelligence (AI) is going to have to learn to make in order for eMerchants to fully leverage the potential of machine learning. To one customer, "buy milk" means buy a gallon of whole milk; to another, a 1.4-liter jug of unsweetened vanilla almond milk. Digital shopping lists, apps and virtual assistants must understand this and not force the customer to spell it out each time before these platforms can successfully become the new normal. "We think about things in shorthand, not in terms of specifics," Dave Barrowman, Skava VP of Innovation, told PYMNTS' Karen Webster in a recent webinar.

The current applications of AI in legal work includes drafting and reviewing contracts, mining documents in discovery and due diligence, answering routine questions or sifting data to predict outcomes. AI is a human-like legal issues spotter providing relevant information on contract terms, therefore allowing lawyers to focus their review on the relevant segments of each contract, saving countless lawyer-hours. The tools are simple to use, making litigation document management easier and more efficient, allowing companies to manage more of this work in-house without resorting to outside counsel. Predictive technology analyzes past legal reference data to provide insights into future outcomes, powered by advances in machine learning.

NEW YORK (Reuters) - Microsoft Corp Chief Executive Officer Satya Nadella said on Wednesday the company's search engine, Bing, will focus on expanding in the PC search market after losing its deal with Apple Inc's Siri.

Today, Oath, the Verizon-owned company born of the merger between AOL and Yahoo, released the source code of a data-crunching tool called Vespa, which has long-powered search and other features across the Yahoo empire. Yahoo also uses Vespa to power related-article recommendations and ad-targeting on many Yahoo-branded sites, including Yahoo News, Yahoo Sports, Yahoo Finance, and its advertising network. Don't Laugh: Yahoo's Open Source AI Has a Secret Weapon Vespa's history traces back to the Norwegian search engine AlltheWeb, which Yahoo acquired in 2004. By making Vespa open source, Oath VP of engineering for big data Peter Cnudde says the company hopes to replicate the benefits it has reaped from supporting Hadoop, an open-source software framework for managing big data.

Ask a software engineer: "How would you add search functionality to your product?" or "How do I build a search engine?" You'll probably immediately hear back something like: "Oh, we'd just launch an ElasticSearch cluster. Search is easy these days." Numerous current products still have suboptimal search experiences. Any true search expert will tell you that few engineers have a very deep understanding of how search engines work, knowledge that's often needed to improve search quality.

We propose a generalization of the classical database query optimization problem: multi-objective parametric query (MPQ) optimization. MPQ compares alternative processing plans according to multiple execution cost metrics. It also models missing pieces of information on which plan costs depend upon as parameters. Both features are crucial to model query processing on modern data processing platforms. MPQ generalizes previously proposed query optimization variants, such as multi-objective query optimization, parametric query optimization, and traditional query optimization.

Query optimization is a fundamental problem in data management. Simply put, most database query languages are declarative rather than imperative--that is, they specify properties the answer should satisfy, rather than give an algorithm to compute the answer. The best known and most widely used database query language--SQL--is a prime example of a language for which optimization is essential. By "essential," I mean that database optimization is not a matter of shaving 10% or even a factor of 2x from a query's execution time. In database query evaluation, the difference between a good plan and a bad or even average plan can be multiple orders of magnitude--so successful query optimization makes the difference between a plan that runs quickly and one that never finishes at all.

In many information systems, rankings are widely used to represent the preferences over a set of items or candidates, ranging from information retrieval, recommender to decision making systems [1], [2], [3], [4], [5], [6], in order to improve quality of the services provided by the systems. For example, in search engine, the list of the terms suggested by a search engine after a user's few keystrokes is a typical ranking and such ranking service, widely adopted nowadays, has great impact on user's search experience; it is also recognized that the list of search results is a ranking after a query is issued. A ranking is an ordered sequence of items, in which an item with higher ranking score is more preferred than the items with lower ranking scores. The consensus of rankings is the degree to which the rankings agree according to certain common patterns. The consensus measure, can be used in many information systems, in order to uncover how close or related the rankings are. For example, in the group decision making, a group of experts express their preferences over a set of candidates by using rankings and the measure of the degree of consensus is very useful for reaching consensus [2]. In many information system with large volume of items, such as search engines, it is hard to clearly define what ground truth is, which make it more difficult to evaluate and compare the rankings returned from the systems. The consensus measure of rankings, as a tool for understanding how related or close the rankings are, will help engineers and researchers to discern what aspects of a ranking system need to be improved and to detect outliers [7], [8].