India could emerge as the third-largest market in the Asia-Pacific (APAC) region for IBM's artificial intelligence (AI)-powered workforce automation solution, launched in November last year. The Armonk-based software services giant expects large-sized and mid-sized enterprises from sectors such as banking, insurance and manufacturing to be among the first adopters of the solution. The solution, dubbed the Talent and Transformation suite of services, is one among several that have come out of IBM's global AI platform, Watson. "India is one of the largest markets for the solution in terms of opportunity after Australia and Singapore (in the APAC region)," Lula Mohanty, general manager for APAC at IBM Global Business Services, told TechCircle. "Only five per cent of chief executive officers (CEOs) think that they have embarked on a transformation journey, especially when it comes to human resources core functions and only 24% of CHROs (chief human resources officers) think that they have a lot of work to do in terms of improving their core functions. This is a positive change in terms of rising awareness in the country," she added.

Folks that don't do much of it are often astounded about how quickly costs escalate and how much the process can cost. While the trial itself can cost upwards of $50K, just getting to trial with all the back and forth between the attorneys can cost several times that. A general rule of thumb is that unless the judgment is reasonably likely to be over $100K and include attorney's fees, you'll probably end up in the hole even if you win. Litigation was one of the initial target industries for IBM's advanced artificial intelligence (AI) platform Watson because litigation was so well defined and well documented. The promise was a significant reduction in costs for those bringing or defending against lawsuits and a far better way of determining if it was economically viable to bring or defend against the action to begin with.

We propose a new class of probabilistic neural-symbolic models, that have symbolic functional programs as a latent, stochastic variable. Instantiated in the context of visual question answering, our probabilistic formulation offers two key conceptual advantages over prior neural-symbolic models for VQA. Firstly, the programs generated by our model are more understandable while requiring lesser number of teaching examples. Secondly, we show that one can pose counterfactual scenarios to the model, to probe its beliefs on the programs that could lead to a specified answer given an image. Our results on the CLEVR and SHAPES datasets verify our hypotheses, showing that the model gets better program (and answer) prediction accuracy even in the low data regime, and allows one to probe the coherence and consistency of reasoning performed.

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.

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