We present a new dataset and model for textual entailment, derived from treating multiple-choice question-answering as an entailment problem. SciTail is the first entailment set that is created solely from natural sentences that already exist independently ``in the wild'' rather than sentences authored specifically for the entailment task. Different from existing entailment datasets, we create hypotheses from science questions and the corresponding answer candidates, and premises from relevant web sentences retrieved from a large corpus. These sentences are often linguistically challenging. This, combined with the high lexical similarity of premise and hypothesis for both entailed and non-entailed pairs, makes this new entailment task particularly difficult. The resulting challenge is evidenced by state-of-the-art textual entailment systems achieving mediocre performance on SciTail, especially in comparison to a simple majority class baseline. As a step forward, we demonstrate that one can improve accuracy on SciTail by 5% using a new neural model that exploits linguistic structure.

Inspired by the magic sets for Datalog, we present a novel goal-driven approach for answering queries over terminating existential rules with equality (aka TGDs and EGDs). Our technique improves the performance of query answering by pruning the consequences that are not relevant for the query. This is challenging in our setting because equalities can potentially affect all predicates in a dataset. We address this problem by combining the existing singularization technique with two new ingredients: an algorithm for identifying the rules relevant to a query and a new magic sets algorithm. We show empirically that our technique can significantly improve the performance of query answering, and that it can mean the difference between answering a query in a few seconds or not being able to process the query at all.

Creating systems that can learn to answer natural language questions has been a longstanding challenge for artificial intelligence. Most prior approaches focused on producing a specialized language system for a particular domain and dataset, and they required training on a large corpus manually annotated with logical forms. This paper introduces an analogy-based approach that instead adapts an existing general purpose semantic parser to answer questions in a novel domain by jointly learning disambiguation heuristics and query construction templates from purely textual question-answer pairs. Our technique uses possible semantic interpretations of the natural language questions and answers to constrain a query-generation procedure, producing cases during training that are subsequently reused via analogical retrieval and composed to answer test questions. Bootstrapping an existing semantic parser in this way significantly reduces the number of training examples needed to accurately answer questions. We demonstrate the efficacy of our technique using the Geoquery corpus, on which it approaches state of the art performance using 10-fold cross validation, shows little decrease in performance with 2-folds, and achieves above 50% accuracy with as few as 10 examples.

Many vision and language tasks require commonsense reasoning beyond data-driven image and natural language processing. Here we adopt Visual Question Answering (VQA) as an example task, where a system is expected to answer a question in natural language about an image. Current state-of-the-art systems attempted to solve the task using deep neural architectures and achieved promising performance. However, the resulting systems are generally opaque and they struggle in understanding questions for which extra knowledge is required. In this paper, we present an explicit reasoning layer on top of a set of penultimate neural network based systems. The reasoning layer enables reasoning and answering questions where additional knowledge is required, and at the same time provides an interpretable interface to the end users. Specifically, the reasoning layer adopts a Probabilistic Soft Logic (PSL) based engine to reason over a basket of inputs: visual relations, the semantic parse of the question, and background ontological knowledge from word2vec and ConceptNet. Experimental analysis of the answers and the key evidential predicates generated on the VQA dataset validate our approach.

Radiation oncology vendor Elekta has formed a new partnership with IBM Watson Health to offer the Watson for Oncology artificial intelligence (AI) platform along with its offerings for cancer care. Developed by IBM in collaboration with Memorial Sloan Kettering Cancer Center in New York City, Watson for Oncology is designed to summarize a patient's key medical attributes and provide information to oncologists to help them deliver treatment options based on training from the Memorial Sloan Kettering oncologists, according to Elekta. The software also ranks treatment options, linking to peer-reviewed studies that have been curated by Memorial Sloan Kettering. In addition, Watson for Oncology provides a large corpus of medical literature -- more than 300 medical journals, over 200 textbooks, and nearly 15 million of pages of text -- to offer insight into different treatment options, Elekta said. Beginning in early 2018, Elekta will sell Watson for Oncology as a clinical decision-support application paired within its cancer-care software, including the Mosaiq oncology information system.

When I heard the 60th annual Grammy Awards show was going to feature artificial intelligence, I immediately thought "this is a marketing ploy." But then I found out IBM's Watson was the AI in question. Watson, you see, doesn't have a problem rolling up its non-existent sleeves and doing some good old fashioned hard work. Don't expect a silly robot rolling around doing a human impersonation on the red carpet, IBM's machines show up to solve problems and optimize workflows. And while that isn't very sexy – hard work seldom is – it's incredibly important.

It's award season folks, which means that anyone who loves the addictive mix of celebrities, fashion, and the red carpet will be looking forward to getting their daily fix. Who cares who won the award, most of us just need to know who rocked it best in the Oscar de la Renta or Calvin Klein dress and this is where IBM's Watson could step in. Partnering up with Recording Academy, IBM is bringing artificial intelligence to the red carpet. Taking up the role of fashion police, Watson will be showcasing an innate ability to judge the red carpet outfits using an artificial intelligence platform. This will take place at the Grammys on Sunday night, 28th January 2018.

After winning Jeopardy and designing cancer-treatment plans, IBM Watson is now strutting off to the red carpet of the 60th Annual Grammy Awards. The tech giant's versatile AI system will be curating and distributing award-show content and images of everyone's favorite music stars in real time, straight from the red carpet to people's social media feeds. IBM and the Recording Academy announced their partnership to use the Watson Media Suite at the Grammys today. Want to quickly see who has the coolest looks on the red carpet? Want to gain AI-generated insights into the "emotional tone" of songs by this year's nominees?

Several leaders from the FAIR group, along with the head of Facebook's Applied Machine Learning Group, will report to Pesenti, who in turn will report to Facebook's CTO, Mike Schroepfer, the spokesman said. Facebook will accelerate its growth in AI under Pesenti, the spokesman said. The FAIR group has more than 130 people around the world. In addition to better accommodating for growth, the changes should also help assist in the transfer of technology from the FAIR group to the AML group, the spokesman said. In September Facebook announced the opening of an AI lab in Montreal, and earlier this week the company said it would double the size of its AI lab in Paris.

Some say that artificial intelligence (AI) will radically change healthcare in the future. But that prediction overlooks an important detail: AI is already significantly changing healthcare. IBM (NYSE:IBM) Watson Health general manager Deborah DiSanzo spoke at the annual J. P. Morgan Healthcare Conference on Wednesday. She provided an update on the progress that IBM Watson, the AI system famous for beating Jeopardy! DiSanzo highlighted four areas where AI is making a big difference today.