One of the cornerstones of International Business Machines' (NYSE:IBM) ongoing transformation is cognitive computing, which encompasses artificial intelligence and other related technologies. IBM is a business that serves other businesses, and its approach to artificial intelligence (AI) stays true to its purpose. IBM Watson, the company's well-known AI system, is being used in industries like healthcare and financial services to augment the skills of professionals in those fields. The long-term potential of the technology is immense. This article originally appeared in the Motley Fool. IBM has made a bet that cognitive computing will be a big part of its future.

Attention is typically used to select informative sub-phrases that are used for prediction. This paper investigates the novel use of attention as a form of feature augmentation, i.e, casted attention. We propose Multi-Cast Attention Networks (MCAN), a new attention mechanism and general model architecture for a potpourri of ranking tasks in the conversational modeling and question answering domains. Our approach performs a series of soft attention operations, each time casting a scalar feature upon the inner word embeddings. The key idea is to provide a real-valued hint (feature) to a subsequent encoder layer and is targeted at improving the representation learning process. There are several advantages to this design, e.g., it allows an arbitrary number of attention mechanisms to be casted, allowing for multiple attention types (e.g., co-attention, intra-attention) and attention variants (e.g., alignment-pooling, max-pooling, mean-pooling) to be executed simultaneously. This not only eliminates the costly need to tune the nature of the co-attention layer, but also provides greater extents of explainability to practitioners. Via extensive experiments on four well-known benchmark datasets, we show that MCAN achieves state-of-the-art performance. On the Ubuntu Dialogue Corpus, MCAN outperforms existing state-of-the-art models by $9\%$. MCAN also achieves the best performing score to date on the well-studied TrecQA dataset.

A number of studies have found that today's Visual Question Answering (VQA) models are heavily driven by superficial correlations in the training data and lack sufficient image grounding. To encourage development of models geared towards the latter, we propose a new setting for VQA where for every question type, train and test sets have different prior distributions of answers. Specifically, we present new splits of the VQA v1 and VQA v2 datasets, which we call Visual Question Answering under Changing Priors (VQA-CP v1 and VQA-CP v2 respectively). First, we evaluate several existing VQA models under this new setting and show that their performance degrades significantly compared to the original VQA setting. Second, we propose a novel Grounded Visual Question Answering model (GVQA) that contains inductive biases and restrictions in the architecture specifically designed to prevent the model from 'cheating' by primarily relying on priors in the training data. Specifically, GVQA explicitly disentangles the recognition of visual concepts present in the image from the identification of plausible answer space for a given question, enabling the model to more robustly generalize across different distributions of answers. GVQA is built off an existing VQA model -- Stacked Attention Networks (SAN). Our experiments demonstrate that GVQA significantly outperforms SAN on both VQA-CP v1 and VQA-CP v2 datasets. Interestingly, it also outperforms more powerful VQA models such as Multimodal Compact Bilinear Pooling (MCB) in several cases. GVQA offers strengths complementary to SAN when trained and evaluated on the original VQA v1 and VQA v2 datasets. Finally, GVQA is more transparent and interpretable than existing VQA models.

IBM has laid off a number of employees in its Watson Health unit, but says initial reports that as much as 50 percent to 70 percent of the unit's workforce was furloughed are not accurate and that the reductions will not hurt its core cognitive computing business. A company representative, however, would not provide additional details or give the specific number of employees being let go. The company also refused to say how many people are employed in the Watson Health unit. "IBM is continuing to reposition our team to focus on the high-value segments of the IT market, and we continue to hire aggressively in critical new areas that deliver value for our clients and IBM," said the vendor in a written statement. "This activity affects a small percentage of our Watson Health workforce, as we move to more technology-intensive offerings, simplified processes and automation to drive speed."

IBM has laid off a number of employees in its Watson Health unit, but says initial reports that as much as 50 percent to 70 percent of the unit's workforce was furloughed are not accurate and that the reductions will not hurt its core cognitive computing business. A company representative, however, would not provide additional details or give the specific number of employees being let go. The company also refused to say how many people are employed in the Watson Health unit. "IBM is continuing to reposition our team to focus on the high-value segments of the IT market, and we continue to hire aggressively in critical new areas that deliver value for our clients and IBM," said the vendor in a written statement. "This activity affects a small percentage of our Watson Health workforce, as we move to more technology-intensive offerings, simplified processes and automation to drive speed."

Title: Did the Model Understand the Question? Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

Make sure your content is built the right way. It needs to be factual, accurate, and conversational. Says Forrester, "There is a lot of nuance and subtlety here, but this document from Google explains how their Quality Raters are trained to evaluate voice answers, for example. If you want insights into normal organic results, this Google training document helps." But you'll want to target more conversational terms.

Artificial intelligence may seem futuristic, but smart brands are already focusing on creating practical, measurable consumer-facing applications with the technology. Brand and agency leaders are using this new technology for everything from shifting how media dollars are deployed, to customer service, to using artificial intelligence for content creation. Competitors enter and stakes rise. And consumers expect more from the companies they buy from. It is not about just being the most convenient option, or the cheapest option.

We propose a two-stage neural model to tackle question generation from documents. First, our model estimates the probability that word sequences in a document are ones that a human would pick when selecting candidate answers by training a neural key-phrase extractor on the answers in a question-answering corpus. Predicted key phrases then act as target answers and condition a sequence-to-sequence question-generation model with a copy mechanism. Empirically, our key-phrase extraction model significantly outperforms an entity-tagging baseline and existing rule-based approaches. We further demonstrate that our question generation system formulates fluent, answerable questions from key phrases. This two-stage system could be used to augment or generate reading comprehension datasets, which may be leveraged to improve machine reading systems or in educational settings.

Their conversation focuses on Watson Services for Core ML, which allows developers to build Watson Machine Learning models in the cloud and deploy applications on Apple iOS devices. IBM Watson Services allows developers to build applications and let Watson do the heavy lifting when it comes to AI and Machine Learning. Watson Studio--with its simple steps and drag-and-drop features--allows developers to create models without being Machine Learning experts. Sridhar discusses how this enables users, developers, and businesses to build better applications. The bottom line is that putting Watson in your pocket is a big deal.