To access this members-only content, please log in. If you're not a subscriber why not join today? If you believe you should be able to view this area but cannot log in, then please contact us and we will try to rectify this issue as soon as possible.

Watson can now help security analysts parse thousands of natural language research reports. IBM Watson, Big Blue's artificial intelligence (AI) platform, keeps on learning with the smart platform now well versed in cyber security. Watson for Cyber Security can now help security analysts parse thousands of natural language research reports which were previously not possible. It is claimed to be the industry's first augmented intelligence technology designed to power cognitive security operation centres (SOCs). The company said that for the past year, Watson has been trained on the language of cyber security and has studied more than one million security documents.

Data is growing at an astounding rate. Even the smartest humans could never consume all of the constantly generated data. New tools and solutions are helping companies connect and analyze internal, external and publicly available data to power decision-making across teams. This infographic series takes a deeper look at today's data landscape and how it is impacting industries including banking, telecom and manufacturing. What opportunities are you missing out on by not being more data-driven?

Many question answering systems have primarily focused on factoid questions. These systems require the answers to be explicitly stored in a knowledge base (KB) but due to this requirement, they fail to answer many questions for which the answers cannot be pre-formulated. This paper presents a question answering system which aims at answering non-factoid questions in the DIY domain using logic-based reasoning. Specifically, the system uses Answer Set Programming to derive an answer by combining various types of knowledge such as domain and commonsense knowledge. We showcase the system by answering one specific type of questions -- questions about alternatives. The evaluation result shows that our logic-based reasoning together with the KB (constructed from texts using Information Extraction) significantly improves the user experience.

But what need does Watson fill here?

IBM Watson has a new job: Cybersecurity specialist. At the RSA Conference…IBM announced the availability of Watson for Cyber Security, with the aim of assisting cybersecurity professionals with threat assessment and mitigation…The company said it is the industry's first augmented intelligence technology with the ability to power cognitive security operations centers (SOCs). But what need does Watson fill here?

Assessing uncertainty is an important step towards ensuring the safety and reliability of machine learning systems. Existing uncertainty estimation techniques may fail when their modeling assumptions are not met, e.g. when the data distribution differs from the one seen at training time. Here, we propose techniques that assess a classification algorithm’s uncertainty via calibrated probabilities (i.e. probabilities that match empirical outcome frequencies in the long run) and which are guaranteed to be reliable (i.e. accurate and calibrated) on out-of-distribution input, including input generated by an adversary. This represents an extension of classical online learning that handles uncertainty in addition to guaranteeing accuracy under adversarial assumptions. We establish formal guarantees for our methods, and we validate them on two real-world problems: question answering and medical diagnosis from genomic data.

The exponential growth of information on Community-based Question Answering (CQA) sites has raised the challenges for the accurate matching of high-quality answers to the given questions. Many existing approaches learn the matching model mainly based on the semantic similarity between questions and answers, which can not effectively handle the ambiguity problem of questions and the sparsity problem of CQA data. In this paper, we propose to solve these two problems by exploiting users' social contexts. Specifically, we propose a novel framework for CQA task by exploiting both the question-answer content in CQA site and users' social contexts. The experiment on real-world dataset shows the effectiveness of our method.

We propose a scalable approach to learn video-based question answering (QA): to answer a free-form natural language question about the contents of a video. Our approach automatically harvests a large number of videos and descriptions freely available online. Then, a large number of candidate QA pairs are automatically generated from descriptions rather than manually annotated. Next, we use these candidate QA pairs to train a number of video-based QA methods extended from MN (Sukhbaatar et al. 2015), VQA (Antol et al. 2015), SA (Yao et al. 2015), and SS (Venugopalan et al. 2015). In order to handle non-perfect candidate QA pairs, we propose a self-paced learning procedure to iteratively identify them and mitigate their effects in training. Finally, we evaluate performance on manually generated video-based QA pairs. The results show that our self-paced learning procedure is effective, and the extended SS model outperforms various baselines.

Nowadays the community-based question answering (CQA) sites become the popular Internet-based web service, which have accumulated millions of questions and their posted answers over time. Thus, question answering becomes an essential problem in CQA sites, which ranks the high-quality answers to the given question. Currently, most of the existing works study the problem of question answering based on the deep semantic matching model to rank the answers based on their semantic relevance, while ignoring the authority of answerers to the given question. In this paper, we consider the problem of community-based question answering from the viewpoint of asymmetric multi-faceted ranking network embedding. We propose a novel asymmetric multi-faceted ranking network learning framework for community-based question answering by jointly exploiting the deep semantic relevance between question-answer pairs and the answerers' authority to the given question. We then develop an asymmetric ranking network learning method with deep recurrent neural networks by integrating both answers' relative quality rank to the given question and the answerers' following relations in CQA sites. The extensive experiments on a large-scale dataset from a real world CQA site show that our method achieves better performance than other state-of-the-art solutions to the problem.