IBM is introducing a number of "AI-powered solutions" intended help brands better manage the balance between human intelligence and machine learning. For marketers, IBM's WEATHERfx Footfall with Watson represents a new AI ad solution that's specifically designed to help drive in-store traffic by optimizing marketing campaigns. By improving conversion rates with contextually relevant advertising to reach the right audience at the right time, Subway successfully increased sales and attracted new clients. Subway's WEATHERfx Footfall with Watson work involved designing ads based on shifting weather patterns. "Why advertise hot sandwiches when it's 100 degrees out?" is the thinking behind the creative ad units.

IBM has ushered its customers into the era of enterprise data science for more than a decade, starting with the investment of the ILOG CPLEX and SPSS acquisitions. As the data science market evolved, new macro trends developed, and IBM invested in advanced technologies and platforms to respond to this shift. In 2016, IBM introduced Data Science Experience and several Watson offerings, which blurred the lines between our new and old technologies. We have now made the decision to simplify our portfolio for our customers under one single brand -- IBM Watson Studio. IBM Watson Studio was first announced in the IBM Public Cloud at our Think Conference in March 2018, which included the integration of the capabilities of Data Science Experience Cloud and a new interface for SPSS Modeler.

We propose a video story question-answering (QA) architecture, Multimodal Dual Attention Memory (MDAM). The key idea is to use a dual attention mechanism with late fusion. MDAM uses self-attention to learn the latent concepts in scene frames and captions. Given a question, MDAM uses the second attention over these latent concepts. Multimodal fusion is performed after the dual attention processes (late fusion). Using this processing pipeline, MDAM learns to infer a high-level vision-language joint representation from an abstraction of the full video content. We evaluate MDAM on PororoQA and MovieQA datasets which have large-scale QA annotations on cartoon videos and movies, respectively. For both datasets, MDAM achieves new state-of-the-art results with significant margins compared to the runner-up models. We confirm the best performance of the dual attention mechanism combined with late fusion by ablation studies. We also perform qualitative analysis by visualizing the inference mechanisms of MDAM.

State Farm is moving forward with several digital initiatives as the largest personal lines P&C insurer in the U.S. by market share rides the digitalization wave shaking up the industry. The company has launched a $100 million fund, State Farm Ventures, with the goal of increasing its involvement in and adoption of insurtech. Led by innovation executive Michael Remmes, the unit will focus on "acquiring startups or strategic alliances that support our core products," says spokesperson Angie Harrier. With a major thrust of insurtech being use cases for artificial intelligence, State Farm is beginning to explore that technology as well. The insurer is running an ad campaign along with the Weather Company and IBM Watson through Halloween that uses Watson's cognitive computing technology to deliver relevant storm-preparation content to affected customers.

Reading comprehension QA tasks have seen a recent surge in popularity, yet most works have focused on fact-finding extractive QA. We instead focus on a more challenging multi-hop generative task (NarrativeQA), which requires the model to reason, gather, and synthesize disjoint pieces of information within the context to generate an answer. This type of multi-step reasoning also often requires understanding implicit relations, which humans resolve via external, background commonsense knowledge. We first present a strong generative baseline that uses a multi-attention mechanism to perform multiple hops of reasoning and a pointer-generator decoder to synthesize the answer. This model performs substantially better than previous generative models, and is competitive with current state-of-the-art span prediction models. We next introduce a novel system for selecting grounded multi-hop relational commonsense information from ConceptNet via a pointwise mutual information and term-frequency based scoring function. Finally, we effectively use this extracted commonsense information to fill in gaps of reasoning between context hops, using a selectively-gated attention mechanism. This boosts the model's performance significantly (also verified via human evaluation), establishing a new state-of-the-art for the task. We also show that our background knowledge enhancements are generalizable and improve performance on QAngaroo-WikiHop, another multi-hop reasoning dataset.

Open-domain question answering (QA) is an important problem in AI and NLP that is emerging as a bellwether for progress on the generalizability of AI methods and techniques. Much of the progress in open-domain QA systems has been realized through advances in information retrieval methods and corpus construction. In this paper, we focus on the recently introduced ARC Challenge dataset, which contains 2,590 multiple choice questions authored for grade-school science exams. These questions are selected to be the most challenging for current QA systems, and current state of the art performance is only slightly better than random chance. We present a system that rewrites a given question into queries that are used to retrieve supporting text from a large corpus of science-related text. Our rewriter is able to incorporate background knowledge from ConceptNet and -- in tandem with a generic textual entailment system trained on SciTail that identifies support in the retrieved results -- outperforms several strong baselines on the end-to-end QA task despite only being trained to identify essential terms in the original source question. We use a generalizable decision methodology over the retrieved evidence and answer candidates to select the best answer. By combining query rewriting, background knowledge, and textual entailment our system is able to outperform several strong baselines on the ARC dataset.

Las Vegas, HR Technology Conference & Expo #HRTech -- LEADx, Inc., the world's leading Conversational Learning (CL) platform for leadership enablement, today launched LEADx Coach Amanda, an executive coach virtual assistant powered by IBM Watson Assistant. "We believe every manager deserves a coach," said Kevin Kruse, LEADx founder and CEO. "Traditional leadership development, based on workshops and online tutorials, has long failed enterprises and managers alike. Executive coaches work well, but due to their cost they are ironically reserved for the leaders who have the most experience. But now, we've tapped the power of AI to democratize leadership development."

Datasets that boosted state-of-the-art solutions for Question Answering (QA) systems prove that it is possible to ask questions in natural language manner. However, users are still used to query-like systems where they type in keywords to search for answer. In this study we validate which parts of questions are essential for obtaining valid answer. In order to conclude that, we take advantage of LIME - a framework that explains prediction by local approximation. We find that grammar and natural language is disregarded by QA. State-of-the-art model can answer properly even if 'asked' only with a few words with high coefficients calculated with LIME. According to our knowledge, it is the first time that QA model is being explained by LIME.

We investigate the difficulty levels of questions, and propose a new setting called Difficulty-controllable Question Generation (DQG). Taking as input a reading comprehension paragraph and some text fragments (i.e. answers) in the paragraph that we want to ask questions about, a DQG method needs to generate questions each of which has a given text fragment as its answer, and meanwhile the generation is under the control of specified difficulty labels---the output questions should satisfy the specified difficulty as much as possible. To solve this task, we propose an end-to-end framework to generate questions of designated difficulty levels. Specifically, we explore a few intuitions: (i) In the input sentences, the nearer a word is to the answer fragment, the more likely it is used in the question; (ii) The easier a question is, the nearer its words are to the answer fragment in the sentence; (iii) Performing difficulty control could be regarded as a problem of sentence generation towards a specified attribute or style, namely difficulty level. For evaluation, we prepared the first dataset of reading comprehension questions with difficulty labels. The results show that our framework not only generates questions of better quality under the metrics like BLEU, but also has the capability to generate questions complying with the specified difficulty labels.

There are three modalities in the reading comprehension setting: question, answer and context. The task of question answering or question generation aims to infer an answer or a question when given the counterpart based on context. We present a novel two-way neural sequence transduction model that connects three modalities, allowing it to learn two tasks simultaneously and mutually benefit one another. During training, the model receives question-context-answer triplets as input and captures the cross-modal interaction via a hierarchical attention process. Unlike previous joint learning paradigms that leverage the duality of question generation and question answering at data level, we solve such dual tasks at the architecture level by mirroring the network structure and partially sharing components at different layers. This enables the knowledge to be transferred from one task to another, helping the model to find a general representation for each modality. The evaluation on four public datasets shows that our dual-learning model outperforms the mono-learning counterpart as well as the state-of-the-art joint models on both question answering and question generation tasks.