Question Answering (QA) systems are used to provide proper responses to users' questions automatically. Sentence matching is an essential task in the QA systems and is usually reformulated as a Paraphrase Identification (PI) problem. Given a question, the aim of the task is to find the most similar question from a QA knowledge base. In this paper, we propose a Multi-task Sentence Encoding Model (MSEM) for the PI problem, wherein a connected graph is employed to depict the relation between sentences, and a multi-task learning model is applied to address both the sentence matching and sentence intent classification problem. In addition, we implement a general semantic retrieval framework that combines our proposed model and the Approximate Nearest Neighbor (ANN) technology, which enables us to find the most similar question from all available candidates very quickly during online serving. The experiments show the superiority of our proposed method as compared with the existing sentence matching models.

Robustness Envelopes characterize the set of possible contingencies that a plan is able to address without re-planning, but their exact computation is extremely expensive; furthermore, general robustness envelopes are not amenable for efficient execution. In this paper, we present a novel, anytime algorithm to approximate Robustness Envelopes, making them scalable and executable. This is proven by an experimental analysis showing the efficiency of the algorithm, and by a concrete case study where the execution of robustness envelopes significantly reduces the number of re-plannings. 1 Introduction When planning and scheduling techniques are employed in practical applications, one of the major problems is the need for online re-planning when the observed contingencies are not aligned with the ones that were considered at planning time. These situations are common, because it is arguably impossible to predict the entire range of situations an autonomous system can encounter, especially when the planning domain encompasses time and temporal constraints. Unfortunately, re-planning can be costly in terms of time, and computational resources can be scarce on-board, so limiting the use of re-planning is very important for practical purposes. In principle, it is also possible to continue with the execution of a plan even when the observed contingencies are unexpected, optimistically hoping for a successful completion. However, this approach offers no formal guarantee, and is prone to the risk of continuing execution of a plan that is bound to fail. Several approaches have been proposed in the literature to address this problem (see (In-grand and Ghallab 2017) for a survey focused on robotics).

The IKEA Furniture Assembly Environment is one of the first benchmarks for testing and accelerating the automation of complex manipulation tasks. The environment is designed to advance reinforcement learning from simple toy tasks to complex tasks requiring both long-term planning and sophisticated low-level control. Our environment supports over 80 different furniture models, Sawyer and Baxter robot simulation, and domain randomization. The IKEA Furniture Assembly Environment is a testbed for methods aiming to solve complex manipulation tasks. The environment is publicly available at https://clvrai.com/furniture

Definition Extraction (DE) is one of the well-known topics in Information Extraction that aims to identify terms and thei r corresponding definitions in unstructured texts. This task can be formalized either as a sentence classification task (i.e., containing term-definition pairs or not) or a sequential labeling task (i.e., identifying the boundaries of the terms a nd definitions). The previous works for DE have only focused on one of the two approaches, failing to model the interdependencies between the two tasks. In this work, we propose a novel model for DE that simultaneously performs the two tasks in a single framework to benefit from their interdependencies. Our model features deep learning architectu res to exploit the global structures of the input sentences as we ll as the semantic consistencies between the terms and the definitions, thereby improving the quality of the representat ion vectors for DE. Besides the joint inference between sentenc e classification and sequential labeling, the proposed model is fundamentally different from the prior work for DE in that th e prior work has only employed the local structures of the input sentences (i.e., word-to-word relations), and not yet c on-sidered the semantic consistencies between terms and definitions. In order to implement these novel ideas, our model presents a multi-task learning framework that employs grap h convolutional neural networks and predicts the dependency paths between the terms and the definitions. We also seek to enforce the consistency between the representations of t he terms and definitions both globally (i.e., increasing seman - tic consistency between the representations of the entire s en-tences and the terms/definitions) and locally (i.e., promot ing the similarity between the representations of the terms and the definitions). The extensive experiments on three benchmark datasets demonstrate the effectiveness of our approach.

The bigger your company, the more important it is that every team member is on the same page. When you're as big as Genpact, with 90,000 employees and twice as many partners, then collaboration is a top priority. Sanjay Srivastava is well aware of the challenges. As Genpact's Chief Digital Officer, he is front and center at the effort to make sure the disparate teams and employees within the company are working successfully in a collaborative organizational culture, as well as offering a satisfying customer experience. For Sanjay, there are three main factors that need a strong collaboration platform within a company. It starts with the idea of the business as a connected ecosystem that drives a collective intelligence. Then there's the concept of continuous learning and innovation that requires a collaborative framework to be successful. Finally, there's the convergence of domains, the ability to pull people together from different disciplines, with different experiences, and across ...

You are invited to attend our event next Monday, Nov 18th @6:00 pm at Venture X. Come and join us as Dr. Huang gives a talk on how Deep Learning is used in Genomics. If you are curious about Artificial Intelligence & Data Science in Genomics and want to learn more, then this talk is for you. Dr. Huang's expertise is in the areas of Computational Biology, Computational Neuroergonomics, Brain-Computer Interface, Statistical Modeling, and Bayesian Methods. Dr. Yufei Huang is a Professor and Associate Chair in Research at the Department of Electrical and Computer Engineering at UTSA. He is also an adjunct professor at the Dept. of Epidemiology and Biostatistics at the University of Texas Health Science Center at San Antonio.

When it comes to keeping up with emerging cybersecurity trends, the process of staying on top of any recent developments can get quite tedious since there's a lot of news to keep up with. These days, however, the situation has changed dramatically, since the cybersecurity realms seem to be revolving around two words- deep learning. Although we were initially taken aback by the massive coverage that deep learning was receiving, it quickly became apparent that the buzz generated by deep learning was well-earned. In a fashion similar to the human brain, deep learning enables an AI model to achieve highly accurate results, by performing tasks directly from the text, images, and audio cues. Up till this point, it was widely believed that deep learning relies on a huge set of data, quite similar to the magnitude of data housed by Silicon Valley giants Google and Facebook to meet the aim of solving the most complicated problems within an organization.

DAKAR (Thomson Reuters Foundation) - Data analyst Fabrice Sonzahi enrolled in a course on artificial intelligence (AI) in Dakar, hoping to help struggling farmers improve crop yields in his home country of Ivory Coast. He is part of an inaugural batch of students at a new AI programming school in Senegal, one of the first in West Africa. Its mission is to train local people in using data to solve pressing issues like the impact of climate change on crops. The Dakar Institute of Technology (DIT), which opened in September, is running its first 10-week boot camp with nine students in partnership with French AI school VIVADATA. "I am convinced that by analyzing data we can give (farmers) better solutions," said Sonzahi, 30.

Multicenter studies are required to validate the added benefit of using deep convolutional neural network (DCNN) software for detecting malignant pulmonary nodules on chest radiographs. To compare the performance of radiologists in detecting malignant pulmonary nodules on chest radiographs when assisted by deep learning–based DCNN software with that of radiologists or DCNN software alone in a multicenter setting. Investigators at four medical centers retrospectively identified 600 lung cancer–containing chest radiographs and 200 normal chest radiographs. Each radiograph with a lung cancer had at least one malignant nodule confirmed by CT and pathologic examination. Twelve radiologists from the four centers independently analyzed the chest radiographs and marked regions of interest. Commercially available deep learning–based computer-aided detection software separately trained, tested, and validated with 19 330 radiographs was used to find suspicious nodules. The radiologists then reviewed the images with the assistance of DCNN software.

Artificial intelligence can be used to accurately examine electrocardiogram (ECG) test results, according to the findings of two preliminary studies being presented at the American Heart Association Scientific Sessions 2019 in Philadelphia, PA. In the first study, researchers evaluated 1.1 million ECGs that did indicate atrial fibrillation (AF) from more than 237,000 patients. They used specialized computational hardware to train a deep neutral network to assess 30,000 data points for each respective ECG. The results showed that approximately one in three people received an AF diagnosis within a year. Moreover, the model demonstrated the capacity for long-term prognostic significance as patients predicted to develop AF after one year had a 45% higher hazard rate in developing AF over a follow-up duration of 25-years compared to other patients.