We analyze state-of-the-art deep learning models for three tasks: question answering on (1) images, (2) tables, and (3) passages of text. Using the notion of \emph{attribution} (word importance), we find that these deep networks often ignore important question terms. Leveraging such behavior, we perturb questions to craft a variety of adversarial examples. Our strongest attacks drop the accuracy of a visual question answering model from $61.1\%$ to $19\%$, and that of a tabular question answering model from $33.5\%$ to $3.3\%$. Additionally, we show how attributions can strengthen attacks proposed by Jia and Liang (2017) on paragraph comprehension models. Our results demonstrate that attributions can augment standard measures of accuracy and empower investigation of model performance. When a model is accurate but for the wrong reasons, attributions can surface erroneous logic in the model that indicates inadequacies in the test data.

I have been writing code for a number of years now, but was finally bitten by the AI bug in 2016. A thrill of excitement ran through me as I ran demos of applications that were powered by AI. Seeing the potential and value of how AI could change our lives, I was convinced that AI was the future, only to find out later that I was wrong. AI had been a part of my life all along. It had worn several clothes like People You May Know on Facebook, autocorrect while I typed on my phone, Siri, and so many others.

Existing attention mechanisms either attend to local image grid or object level features for Visual Question Answering (VQA). Motivated by the observation that questions can relate to both object instances and their parts, we propose a novel attention mechanism that jointly considers reciprocal relationships between the two levels of visual details. The bottom-up attention thus generated is further coalesced with the top-down information to only focus on the scene elements that are most relevant to a given question. Our design hierarchically fuses multi-modal information i.e., language, object- and gird-level features, through an efficient tensor decomposition scheme. The proposed model improves the state-of-the-art single model performances from 67.9% to 68.2% on VQAv1 and from 65.3% to 67.4% on VQAv2, demonstrating a significant boost.

Use streaming IoT data to uncover insights that help you better understand equipment health. Gain real-time visibility into manufacturing and supply chain processes; and monitor overall plant performance, product quality and vehicle safety issues to mitigate or avoid costly product recalls.

The graph represents a network of 3,453 Twitter users whose tweets in the requested range contained "ibm watson", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Sunday, 06 May 2018 at 03:42 UTC. The requested start date was Sunday, 06 May 2018 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 5,000. The tweets in the network were tweeted over the 12-day, 10-hour, 28-minute period from Sunday, 22 April 2018 at 00:01 UTC to Friday, 04 May 2018 at 10:30 UTC.

Technology giants IBM and Salesforce are expanding their strategic partnership by bringing together their artificial intelligence and cloud computing platforms to help companies connect with customers and collaborate more effectively with deeper insights. Salesforce has named IBM as a preferred cloud services provider and IBM has named Salesforce as its preferred customer engagement platform for sales and service, the companies said in a release. "This expanded partnership builds on the combined power of Watson and Einstein to help enterprises make smarter business decisions," said Ginni Rometty, IBM's chairman, president and chief executive officer. Watson and Einstein are the artificial intelligence platforms of IBM and Salesforce respectively. As a part of this extended strategic partnership, IBM will build newIBM WatsonQuip Live Apps, bringing the power of Watson and Quip together.

Qoints has launched an influencer marketing tool that uses the artificial intelligence of IBM's Watson to unearth the best influencers for your brand. The Qoints AI Social Discovery tool is a self-service tool that helps marketers deal with the problem of finding the right influencers for marketing campaigns. Locating the right micro influencers (those with 5,000 to 50,000 followers) is key to making influencer marketing effective and affordable. But it's a tedious process to manually search for the right influencers. Demand for influencers is growing, as they've been shown to generate higher levels of trust, engagement and purchase intent from their followers (in comparison to celebrity influencers), according to Toronto-based Qoints.

BM and MIT today announced that IBM plans to make a 10-year, $240 million investment to create the MIT–IBM Watson AI Lab in partnership with MIT. The lab will carry out fundamental artificial intelligence (AI) research and seek to propel scientific breakthroughs that unlock the potential of AI. The collaboration aims to advance AI hardware, software, and algorithms related to deep learning and other areas; increase AI's impact on industries, such as health care and cybersecurity; and explore the economic and ethical implications of AI on society. IBM's $240 million investment in the lab will support research by IBM and MIT scientists. The new lab will be one of the largest long-term university-industry AI collaborations to date, mobilizing the talent of more than 100 AI scientists, professors, and students to pursue joint research at IBM's Research Lab in Cambridge, Massachusetts -- co-located with the IBM Watson Health and IBM Security headquarters in Kendall Square -- and on the neighboring MIT campus Read more details at http://news.mit.edu/2017/ibm-mit-join...

Image description task has been invariably examined in a static manner with qualitative presumptions held to be universally applicable, regardless of the scope or target of the description. In practice, however, different viewers may pay attention to different aspects of the image, and yield different descriptions or interpretations under various contexts. Such diversity in perspectives is difficult to derive with conventional image description techniques. In this paper, we propose a customized image narrative generation task, in which the users are interactively engaged in the generation process by providing answers to the questions. We further attempt to learn the user's interest via repeating such interactive stages, and to automatically reflect the interest in descriptions for new images. Experimental results demonstrate that our model can generate a variety of descriptions from single image that cover a wider range of topics than conventional models, while being customizable to the target user of interaction.

A popular recent approach to answering open-domain questions is to first search for question-related passages and then apply reading comprehension models to extract answers. Existing methods usually extract answers from single passages independently. But some questions require a combination of evidence from across different sources to answer correctly. In this paper, we propose two models which make use of multiple passages to generate their answers. Both use an answer-reranking approach which reorders the answer candidates generated by an existing state-of-the-art QA model. We propose two methods, namely, strength-based re-ranking and coverage-based re-ranking, to make use of the aggregated evidence from different passages to better determine the answer. Our models have achieved state-of-the-art results on three public open-domain QA datasets: Quasar-T, SearchQA and the open-domain version of TriviaQA, with about 8 percentage points of improvement over the former two datasets.