I recently completed a course on NLP through Deep Learning (CS224N) at Stanford and loved the experience. For my final project I worked on a question answering model built on Stanford Question Answering Dataset (SQuAD). In this blog, I want to cover the main building blocks of a question answering model. You can find the full code on my Github repo. Stanford Question Answering Dataset (SQuAD) is a new reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage.

Watson is IBM's AI platform. This afternoon I tried out IBM Watson's Personality Insights Demo. The service "derives insights about personality characteristics from social media, enterprise data, or other digital communications". You are shrewd, inner-directed and can be perceived as indirect. You are authority-challenging: you prefer to challenge authority and traditional values to help bring about positive changes.

New mobile machine learning capabilities are coming to Apple devices, thanks to IBM Watson and Apple Core ML. Under CEO Tim Cook's leadership, Apple has been angling for an ever-bigger slice of the enterprise pie. Last year we saw the technology giant partner with GE to bring the industrial predictive and analytics capabilities of the Predix IIoT platform to Apple's iOS. But for the past few years Apple has been deepening and extending its ties with IBM too. When the two companies announced their strategic partnership in 2014, Tim Cook and IBM CEO Virginia Rometty claimed that "Apple and IBM are like puzzle pieces that fit perfectly together."

In this paper, we present a framework for Question Difficulty and Expertise Estimation (QDEE) in Community Question Answering sites (CQAs) such as Yahoo! Answers and Stack Overflow, which tackles a fundamental challenge in crowdsourcing: how to appropriately route and assign questions to users with the suitable expertise. This problem domain has been the subject of much research and includes both language-agnostic as well as language conscious solutions. We bring to bear a key language-agnostic insight: that users gain expertise and therefore tend to ask as well as answer more difficult questions over time. We use this insight within the popular competition (directed) graph model to estimate question difficulty and user expertise by identifying key hierarchical structure within said model. An important and novel contribution here is the application of "social agony" to this problem domain. Difficulty levels of newly posted questions (the cold-start problem) are estimated by using our QDEE framework and additional textual features. We also propose a model to route newly posted questions to appropriate users based on the difficulty level of the question and the expertise of the user. Extensive experiments on real world CQAs such as Yahoo! Answers and Stack Overflow data demonstrate the improved efficacy of our approach over contemporary state-of-the-art models. The QDEE framework also allows us to characterize user expertise in novel ways by identifying interesting patterns and roles played by different users in such CQAs.

Artificial intelligence is revolutionizing businesses across industries. More than half of the executives surveyed in a 2017 PwC report said that AI solutions were already increasing their companies' productivity. As usual, marketers are at the forefront, embracing AI at a particularly rapid pace. But while any new resource can create excitement in some, it can make others feel uncertain--sometimes even worried about their futures. Many marketers fear that onboarding AI will fundamentally change the way they do business, and not completely for the better.

In ontology-based data access (OBDA), the classical database is enhanced with an ontology in the form of logical assertions generating new intensional knowledge. A powerful form of such logical assertions is the tuple-generating dependencies (TGDs), also called existential rules, where Horn rules are extended by allowing existential quantifiers to appear in the rule heads. In this paper we introduce a new language called loop restricted (LR) TGDs (existential rules), which are TGDs with certain restrictions on the loops embedded in the underlying rule set. We study the complexity of this new language. We show that the conjunctive query answering (CQA) under the LR TGDs is decid- able. In particular, we prove that this language satisfies the so-called bounded derivation-depth prop- erty (BDDP), which implies that the CQA is first-order rewritable, and its data complexity is in AC0 . We also prove that the combined complexity of the CQA is EXPTIME complete, while the language membership is PSPACE complete. Then we extend the LR TGDs language to the generalised loop restricted (GLR) TGDs language, and prove that this class of TGDs still remains to be first-order rewritable and properly contains most of other first-order rewritable TGDs classes discovered in the literature so far.

On Thursday, IBM announced new capabilities for its Watson Data Platform that make it easier for developers and data scientists to analyze and prepare enterprise data for artificial intelligence (AI) applications. By 2018, nearly 75% of developers will build AI functionality into their apps, according to an IDC report. However, this requires wading through increasingly complex data that lives in different places, and must be continually and securely ingested, according to an IBM press release. In response to this challenge, Watson will now include data cataloging and data refining, to improve data visibility and better enforce data security policies so that users can more easily share information across public and private cloud environments. "We are always looking for new ways to gain a more holistic view of our clients' campaign data, and design tailored approaches for each ad and marketing tactic," Michael Kaushansky, chief data officer at global advertising and marketing consultancy Havas, said in the release.

We are pleased to announce that in "The Forrester New Wave: Conversational Computing Platforms, Q2 2018,"[1] IBM Watson Assistant is named as a Leader in conversational computing. It has become increasingly important for businesses to build engaging interactions that deliver value to their customers, and IBM is proud to offer technologies that help developers and enterprises enhance those experiences. The report evaluated the most significant conversational computing platforms, diving into each vendor's current offering and strategy and including customer feedback. IBM was cited for its developer-friendly tools and enterprise expertise requirements, which give developers access to the tools and technologies they need while providing industry and enterprise support for their businesses. Our customers also appreciated IBM's thorough understanding of enterprise requirements and Watson Assistant's readiness for integration into a broader architecture.

Today, we are enhancing our product to accelerate the value of AI in your companies and announcing Watson Studio. IBM Watson Studio is an integrated environment designed to make it easy to develop, train, manage models and deploy AI-powered applications and is a SaaS solution delivered on the IBM Cloud. IBM Services has already been using Watson Studio to train business patterns. In specific client situations, we are able to train business patterns and encode into end user applications in a few hours. Our consultants now embed Watson-powered AI across all processes in our client business with simplicity and speed, so our consultants spend more time creating incremental value, rather then coding applications.

Christina Cardoza is the News Editor of SD Times. She is responsible for the oversight of the daily news published to the website as well as the company's weekly newsletter, News on Monday. She covers agile, DevOps, AI, machine learning, mixed reality and software security. She is an undeniable nerd who loves Marvel comics and Star Wars. On Follow her on Twitter at @chriscatdoza!