E-commerce companies are focusing on artificial intelligence and virtual reality with a view to cut logistics costs and identify fraudulent orders, said a report by global auditing and consulting firm PwC. With an emerging middle-class population of more than 500 million and approximately 65 per cent of the population aged 35 or below, India represents a highly aspirational consumer market for retailers across the globe, said the PwC TechWorld report. "E-commerce players are revamping their technology strategies to maintain their competitive edge. Most e-commerce platforms are upping their investments in areas such as conversational commerce, artificial intelligence (AI), virtual reality (VR)/augmented reality (AR) and analytics technologies," it said. It observed that to identify fraudulent orders, reduce return rate and also cut down on logistics cost, e-commerce companies are investing in robotics and AI heavily.

Case study: Some car makers, including Volvo, Google, and Mercedes, have already said they would accept full liability for their vehicles' actions when they are in autonomous mode. Even without such a pledge, it's likely that manufacturers would end up paying if their autonomous car caused harm. If the offending car were considered a defective product, its maker could be held liable under strict product-design standards, potentially leading to class-action lawsuits and expensive product recalls -- like Takata faced for its dangerous airbags. Another possibility: Going deeper into the system, the AI itself could be held responsible, according to Gabriel Hallevy, a law professor at Ono Academic College in Israel, who wrote a book about AI and criminal negligence. That still means its programmer or manufacturer could be found negligent as well, or even accomplice to a crime.

For the past five years, the hottest thing in artificial intelligence has been a branch known as deep learning. The grandly named statistical technique, put simply, gives computers a way to learn by processing massive amounts of data. Thanks to deep learning, computers can easily identify faces and recognize spoken words, making other forms of humanlike intelligence suddenly seem within reach. Companies like Google, Facebook and Microsoft have poured money into deep learning. And the technology's perception and pattern-matching abilities are being applied to improve progress in fields such as drug discovery and self-driving cars. But now some scientists are asking whether deep learning is really so deep after all.

Big data, artificial intelligence and machine learning are ruling the tech structure of most industries. We all know how Amazon combines a customer's historical data and other customers' data to power recommendations. Likewise, for Google, it's not difficult to predict our preferences and interests. They make use of big data, analytics and machine learning to be able to process huge amounts of data, identify patterns, analyze them and consequently indulge in predictive analysis. The most complicated disease of the most important organ of the body – the brain, is a clear beneficiary of this AI approach.

AMMAN – Prince William on Sunday praised "historic ties and friendship" between Britain and Jordan, as he kicked off a historic, politically delicate five-day tour of the desert kingdom, Israel and the Palestinian territories. Though billed as nonpolitical, it's a high-profile foreign visit for William, 36, second in line to the throne. He is meeting with young scientists, refugees and political leaders in a tumultuous region Britain controlled between the two world wars. On Sunday, he was welcomed in Jordan by 23-year-old Crown Prince Hussein, a member of the Hashemite dynasty Britain helped install in then-Transjordan almost a century ago. William was greeted by an honor guard after his plane landed at a small airport on the outskirts of the capital of Amman.

Britain's Prince William on Sunday praised "historic ties and friendship" with Jordan and the kingdom's commitment to Syrian and Palestinian refugees, as he began a historic five-day tour that also includes Israel and the Palestinian territories. Though billed as non-political, it's a high-profile visit for William, 36, second in line to the throne. He is meeting with young scientists, refugees and political leaders in a tumultuous region Britain controlled between the two world wars. In Jordan, the prince was hosted by Crown Prince Hussein, 23, a member of the Hashemite dynasty Britain helped install in then-Transjordan almost a century ago. The pair capped the day Sunday by watching England's World Cup match against Panama which the heir to the Jordanian throne had recorded earlier, Press Association said.

As artificial intelligence is increasingly affecting all parts of society and life, there is growing recognition that human interpretability of machine learning models is important. It is often argued that accuracy or other similar generalization performance metrics must be sacrificed in order to gain interpretability. Such arguments, however, fail to acknowledge that the overall decision-making system is composed of two entities: the learned model and a human who fuses together model outputs with his or her own information. As such, the relevant performance criteria should be for the entire system, not just for the machine learning component. In this work, we characterize the performance of such two-node tandem data fusion systems using the theory of distributed detection. In doing so, we work in the population setting and model interpretable learned models as multi-level quantizers. We prove that under our abstraction, the overall system of a human with an interpretable classifier outperforms one with a black box classifier.

Many applications, including rank aggregation, crowd-labeling, and graphon estimation, can be modeled in terms of a bivariate isotonic matrix with unknown permutations acting on its rows and columns. We consider the problem of estimating such a matrix based on noisy observations of a subset of its entries, and design and analyze polynomial-time algorithms that improve upon the state of the art. In particular, our results imply that any such $n \times n$ matrix can be estimated efficiently in the normalized, squared Frobenius norm at rate $\widetilde{\mathcal O}(n^{-3/4})$, thus narrowing the gap between $\widetilde{\mathcal O}(n^{-1})$ and $\widetilde{\mathcal O}(n^{-1/2})$, hitherto the rates of the most statistically and computationally efficient methods, respectively. Additionally, our algorithms are minimax optimal in another natural metric that measures how well an estimate captures each row of the matrix. Along the way, we prove matching upper and lower bounds on the minimax radii of certain cone testing problems, which may be of independent interest.

This paper presents details of our winning solutions to the task IV of NIPS 2017 Competition Track entitled Classifying Clinically Actionable Genetic Mutations. The machine learning task aims to classify genetic mutations based on text evidence from clinical literature with promising performance. We develop a novel multi-view machine learning framework with ensemble classification models to solve the problem. During the Challenge, feature combinations derived from three views including document view, entity text view, and entity name view, which complements each other, are comprehensively explored. As the final solution, we submitted an ensemble of nine basic gradient boosting models which shows the best performance in the evaluation. The approach scores 0.5506 and 0.6694 in terms of logarithmic loss on a fixed split in stage-1 testing phase and 5-fold cross validation respectively, which also makes us ranked as a top-1 team out of more than 1,300 solutions in NIPS 2017 Competition Track IV.

Mapping and translating professional but arcane clinical jargons to consumer language is essential to improve the patient-clinician communication. Researchers have used the existing biomedical ontologies and consumer health vocabulary dictionary to translate between the languages. However, such approaches are limited by expert efforts to manually build the dictionary, which is hard to be generalized and scalable. In this work, we utilized the embeddings alignment method for the word mapping between unparalleled clinical professional and consumer language embeddings. To map semantically similar words in two different word embeddings, we first independently trained word embeddings on both the corpus with abundant clinical professional terms and the other with mainly healthcare consumer terms. Then, we aligned the embeddings by the Procrustes algorithm. We also investigated the approach with the adversarial training with refinement. We evaluated the quality of the alignment through the similar words retrieval both by computing the model precision and as well as judging qualitatively by human. We show that the Procrustes algorithm can be performant for the professional consumer language embeddings alignment, whereas adversarial training with refinement may find some relations between two languages.