IBM Corp. is becoming more open-minded with a revenue-driving bid to "democratize" access to artificial intelligence. Big Blue will open its previously proprietary Watson AI platform to competing cloud computing services including rivals Amazon Web Services, Microsoft Azure and Google Cloud Platform. The IBM Watson Anywhere initiative will allow a new portable version of IBM's cognitive platform to run on any cloud -- whether it's private, public or a hybrid multi-cloud -- in addition to IBM Cloud, the company announced Tuesday. IBM did not announce a time frame for the Watson Anywhere rollout. "This will be the most open, scalable AI for business in the world," CEO Ginni Rometty said at IBM's Think 2019 conference in San Francisco.

In this paper, we present a novel approach for the task of eXplainable Question Answering (XQA), i.e., generating natural language (NL) explanations for the Visual Question Answering (VQA) problem. We generate NL explanations comprising of the evidence to support the answer to a question asked to an image using two sources of information: (a) annotations of entities in an image (e.g., object labels, region descriptions, relation phrases) generated from the scene graph of the image, and (b) the attention map generated by a VQA model when answering the question. We show how combining the visual attention map with the NL representation of relevant scene graph entities, carefully selected using a language model, can give reasonable textual explanations without the need of any additional collected data (explanation captions, etc). We run our algorithms on the Visual Genome (VG) dataset and conduct internal user-studies to demonstrate the efficacy of our approach over a strong baseline. We have also released a live web demo showcasing our VQA and textual explanation generation using scene graphs and visual attention.

With version 2.1, IBM Machine Learning for z/OS is rebranded to IBM Watson Machine Learning for z/OS. It offers a hybrid cloud approach to model development and model deployment lifecycle management and collaboration that is designed to help organizations innovate and transform on an enterprise scale. It helps data scientists more quickly develop, deploy, and monitor behavioral models that continually learn as new data is introduced. IBM Db2 AI for z/OS, V1.2, a separately licensed product, uses machine learning to improve the operational performance of Db2 for z/OS systems. Watson Machine Learning for z/OS, V2.1 is a key component for operationalizing machine learning models on z/OS. It provides the ability to deploy models on z/OS that were developed and trained in the cloud, on IBM Z or on non IBM Z platforms. This provides greater deployment flexibility through a new architecture where model management, administration, and scoring services install and execute on z/OS. The new version includes capabilities that were previously separately available through IBM Open Data Analytics for z/OS to help simplify the acquisition, installation, and configuration of the product. Watson Machine Learning for z/OS provides an environment that fosters collaboration to enable innovation and transformation on an enterprise scale.

Watson Natural Language Classifier (NLC) is a text classification (aka text categorization) service that enables developers to quickly train and integrate natural language processing (NLP) capabilities into their applications. Once you have the training data, you can set up a classification model (aka a classifier) in 15 minutes or less to label text with your custom labels. In this tutorial, I will show you how to create two classifiers using publicly available Airbnb reviews data. One of the more common text classification patterns I've seen is analyzing and labeling customer reviews. Understanding unstructured customer feedback enables organizations to make informed decisions that'll improve customer experience or resolve issues faster.

In my experience, I have had great results when I have used user intent and question-based search terms, such as "where," near me" or "in" a specific location. In one of my projects, I significantly increased local visibility for one of my branches with geo-targeted content by creating content that contains the location keyword "Queens, NYC." I have created several blogs with local terms and extra pages with content related to the audience -- e.g., "activities for seniors in Queens, NYC." Furthermore, I optimized the Google My Business listing for the Queens branch, collect reviews and post updates frequently.

The moment IBM Watson played Jeopardy on TV almost a decade ago was the time AI burst onto the scene. It was a breakthrough marketing moment. Over the last decade, IBM Watson has remained the go-to player in Artificial Intelligence as the industry grows. Every year IBM holds their Think conference where they pull together thought leaders from companies, governments, think tanks and soon. This has become the AI super-show.

See how businesses can survive and thrive using AI in Marketing and across the Organization.

A question answering system (QA System) was developed that uses graph-pattern association rules on the YAGO knowledge base. The answer as output of the system is provided based on a user question as input. If the answer is missing or unavailable in the database, then graph-pattern association rules are used to get the answer. The architecture of this question answering system is as follows: question classification, graph component generation, query generation, and query processing. The question answering system uses association graph patterns in a waterfall model. In this paper, the architecture of the system is described, specifically discussing its reasoning and performance capabilities. The results of this research is that rules with high confidence and correct logic produce correct answers, and vice versa.

Information Extraction (IE) refers to automatically extracting structured relation tuples from unstructured texts. Common IE solutions, including Relation Extraction (RE) and open IE systems, can hardly handle cross-sentence tuples, and are severely restricted by limited relation types as well as informal relation specifications (e.g., free-text based relation tuples). In order to overcome these weaknesses, we propose a novel IE framework named QA4IE, which leverages the flexible question answering (QA) approaches to produce high quality relation triples across sentences. Based on the framework, we develop a large IE benchmark with high quality human evaluation. This benchmark contains 293K documents, 2M golden relation triples, and 636 relation types. We compare our system with some IE baselines on our benchmark and the results show that our system achieves great improvements.

One of the challenges in large-scale information retrieval (IR) is to develop fine-grained and domain-specific methods to answer natural language questions. Despite the availability of numerous sources and datasets for answer retrieval, Question Answering (QA) remains a challenging problem due to the difficulty of the question understanding and answer extraction tasks. One of the promising tracks investigated in QA is to map new questions to formerly answered questions that are `similar'. In this paper, we propose a novel QA approach based on Recognizing Question Entailment (RQE) and we describe the QA system and resources that we built and evaluated on real medical questions. First, we compare machine learning and deep learning methods for RQE using different kinds of datasets, including textual inference, question similarity and entailment in both the open and clinical domains. Second, we combine IR models with the best RQE method to select entailed questions and rank the retrieved answers. To study the end-to-end QA approach, we built the MedQuAD collection of 47,457 question-answer pairs from trusted medical sources, that we introduce and share in the scope of this paper. Following the evaluation process used in TREC 2017 LiveQA, we find that our approach exceeds the best results of the medical task with a 29.8% increase over the best official score. The evaluation results also support the relevance of question entailment for QA and highlight the effectiveness of combining IR and RQE for future QA efforts. Our findings also show that relying on a restricted set of reliable answer sources can bring a substantial improvement in medical QA.