We address two shortcomings in online travel time estimation methods for congested urban traffic. The first shortcoming is related to the determination of the number of mixture modes, which can change dynamically, within day and from day to day. The second shortcoming is the wide-spread use of Gaussian probability densities as mixture components. Gaussian densities fail to capture the positive skew in travel time distributions and, consequently, large numbers of mixture components are needed for reasonable fitting accuracy when applied as mixture components. They also assign positive probabilities to negative travel times. To address these issues, this paper develops a mixture distribution with asymmetric components supported on the positive numbers. We use sparse estimation techniques to ensure parsimonious models. Specifically, we derive a novel generalization of Gamma mixture densities using Mittag-Leffler functions, which provides enhanced fitting flexibility and improved parsimony. In order to accommodate within-day variability and allow for online implementation of the proposed methodology (i.e., fast computations on streaming travel time data), we introduce a recursive algorithm which efficiently updates the fitted distribution whenever new data become available. Experimental results using real-world travel time data illustrate the efficacy of the proposed methods.

Distributed representation plays an important role in deep learning based natural language processing. However, the representation of a sentence often varies in different tasks, which is usually learned from scratch and suffers from the limited amounts of training data. In this paper, we claim that a good sentence representation should be invariant and can benefit the various subsequent tasks. To achieve this purpose, we propose a new scheme of information sharing for multi-task learning. More specifically, all tasks share the same sentence representation and each task can select the task-specific information from the shared sentence representation with attention mechanism. The query vector of each task's attention could be either static parameters or generated dynamically. We conduct extensive experiments on 16 different text classification tasks, which demonstrate the benefits of our architecture.

The quality of training data is one of the crucial problems when a learning-centered approach is employed. This paper proposes a new method to investigate the quality of a large corpus designed for the recognizing textual entailment (RTE) task. The proposed method, which is inspired by a statistical hypothesis test, consists of two phases: the first phase is to introduce the predictability of textual entailment labels as a null hypothesis which is extremely unacceptable if a target corpus has no hidden bias, and the second phase is to test the null hypothesis using a Naive Bayes model. The experimental result of the Stanford Natural Language Inference (SNLI) corpus does not reject the null hypothesis. Therefore, it indicates that the SNLI corpus has a hidden bias which allows prediction of textual entailment labels from hypothesis sentences even if no context information is given by a premise sentence. This paper also presents the performance impact of NN models for RTE caused by this hidden bias.

Are the U.S., China, and Russia recklessly undertaking an "AI arms race"? Clearly, there is military competition among these great powers to advance a range of applications of robotics, artificial intelligence, and autonomous systems. So far, the U.S. has been leading the way. AI and autonomy are crucial to the Pentagon's Third Offset strategy. Its Algorithmic Warfare Cross-Functional Team, Project Maven, has become a "pathfinder" for this endeavor and has started to deploy algorithms in the fight against ISIS.

OCBC Bank has launched artificial intelligence (AI) powered voice banking in collaboration with Google, following the launch of Google Home and Google Home Mini in Singapore. Consumers will be able to speak to the Google Assistant on a smartphone or a Google Home device to initiate a conversation about the bank's service offerings. OCBC Bank remains as the only bank to offer voice-based banking in Singapore. In addition, users can also speak to OCBC via the Google Assistance to calculate the mortgage loan amount they can afford, check unit trust prices and get foreign exchange rates among others. The Google Assistant will provide consumers with another self-service digital channel to interact with OCBC Bank that is both convenient and embedded in consumers' lives.

In many manufacturing plants today, monitoring is a highly manual process. FOURDOTONE Teknoloji analyzes data from sensors to enable manufacturers to respond immediately to problems, and predict when machines are likely to fail. Downtime can be expensive, and in a tightly coupled manufacturing line a problem with one machine can have an impact on the entire factory. For many factories, avoiding downtime is a matter of luck rather than science: machine inspections are infrequent, and only capture what's visible to the eye. Data is gathered from the machines and analyzed in the factory, enabling an immediate response to emergencies or imminent problems.

Machine learning, the most fundamental form of artificial intelligence, has started infiltrating the medical field, and it seems machines can play a crucial role in improving our health. A study of over 50 executives in the healtcare sector by TechEmergence revealed that by 2025 AI will be adopted on a broader scale. If there's one thing the healthcare industry has in abundance, it's undoubtedly data. And machine learning algorithms work better if they are exposed to more data. The savings would also be huge.

Generative Adversarial Networks (GANs)Generative Adversarial Nets, or GAN, in short, are neural nets which were first introduced by Ian Goodfellow in 2014. The algorithm has been hailed as an important milestone in Deep learning by many AI pioneers. Yann Le Cunn (father of convolutional neural networks) told that GANs is the coolest thing that has happened in deep learning within the last 20 years. Many versions of GAN have since come up like DCGAN, Sequence-GAN, LSTM-GAN, etc. GANs are neural networks composed up of two networks competing with each other. The two networks namely generator -- to generate data set and discriminator -- to validate the data set.

But 83% are unaware of how to deploy artificial intelligence & machine learning to address specific business problems, so much benefit is still to be realised Zurich, London, 18 April 2018 – The use of artificial intelligence (AI) and machine learning (ML) in financial services (FS) is on the rise, with 83% of banks having evaluated AI & ML solutions, and 67% having actively deployed them, according to a new study out today. The research with 200 global tier one and tier two banks was conducted by capital market research firm TABB Group on behalf of augmented intelligence solutions provider Squirro, and revealed that AI is the most important'disrupter' for banks today. The study – 'Enhanced Bankers – The Impact of AI'- also highlighted a lack of understanding around AI & ML as specifically applied to improving business processes, with 83% of respondents still unaware of how to apply the technology to solve business problems. Using AI and machine learning to source new leads and opportunities is key to bankers, with 87% of respondents saying that it would be highly impactful if an AI engine could spot relevant events that led to engaging with a client and closing a deal. Bankers recognize that AI driven insights will have a tremendous impact when it comes to anticipate market events to stay ahead of the competition.