A drone carrying a package sails through the air, touching down to make a delivery right on a customer's doorstep. Inc. wowed the world in 2013 with a video purporting to show what the future of the delivery industry would look like. But are we any closer to that now? The answer seems to be no -- at least in Japan. The nation is set to take a step forward in the sector this year as the government prepares to deregulate aviation rules so delivery firms can use drones in rural areas.

Here's a question: do you consider yourself to be a trusting person? Or let me put it another way: would you put your life in the hands of a total stranger? This morning I woke up. I switched on the light -- trusting that I wouldn't be electrocuted by a faulty lamp, or cord, or socket. I prepared my breakfast -- trusting that I wouldn't be poisoned by salmonella in my factory-processed muesli.

Artificial Intelligence (AI) and Machine Learning (ML) are two buzzing terms that you are most likely to hear almost every day. Right from Google and Amazon to Microsoft, Huawei and Samsung are all deploying AI in their products. Now, Tecno Mobile, the Chinese smartphone company is all set to launch a new selfie-centric smartphone in India in its Camon-series. BGR India has learned that the key highlight of the smartphone will be its AI-enabled selfie camera. The smartphone will leverage AI technology to make you look good in your selfies.

The Data Science Summit will be held in Tel Aviv on May 27 -28. The event is for practitioners in artificial intelligence, machine learning and data science from industry and academy. The summit is a two-day event starting on May 27th with a hands-on Workshops Day. Registration is free and open as a service to the entire Data Science community, seats are limited. The actual conference will be held on May 28th at the Tel Aviv Convention Center.

At the moment, only about 12–15% of the engineers who are building the internet and its software are women. We don't want a repeat of these kinds of situations. And we've been working to address this at Women 2.0 for over a decade. We think a lot about how diversity -- or lack thereof. We think about how it has affected -- and is going to affect -- the technology outputs that enter our lives.

Bangalore: Artificial Intelligence (AI) has emerged as the most frequently mentioned theme in discussions among the key disruptive technologies during the first quarter (Q1) of 2018 on Twitter, according to GlobalData study. An analysis from GlobalData's influencer platform revealed that AI was way ahead of other disruptive technologies with more than one-fourth share of overall discussions, followed by Internet of Things (IoT), blockchain and augmented reality. Robotics and analytics were also among the leading themes that were discussed across disruptive portfolio. "AI's domination among influencer mentions is primarily driven by significant discussions related to machine learning. Technologies such as Insurtech, big data and deep learning too have helped AI in witnessing highest discussions on Twitter," said Vaibhav Mathur, Influencer Research Head - GlobalData.

Former technology veteran at global professional services firm Genpact, Prasad Vanga's core learning has been in scaling up large businesses. So that comes naturally to him. He, however, switched that into providing market support and capital for start-ups to help them scale via his early stage fund – Anthill Ventures, launched three years back. According to him, 'selling' is the fundamental criterion to judge entrepreneurs. "We look at start-up's target customers and customer feedbacks. Then we connect the entrepreneurs with the customer they want to on-board and measure their skill in selling their product," says Vanga.

Al Olama said, "Academic institutions in the UAE play a key role in developing educational and training programmes and introducing disciplines that prepare the next generation of leaders who are capable of developing key sector." The Minister of State for AI commended the initiatives of academic institutions in the UAE to develop their educational curricula. He praised HCT's initiative to launch this specialised programme in AI science and technologies. Dr Al Shamsi highlighted the importance of cooperating with Oracle, the global organisation specialised in modern technologies, especially AI. Over the past years, HCT have worked closely with Oracle in technology education.

Bike sharing provides an environment-friendly way for traveling and is booming worldwide. Yet, due to the high similarity of user travel patterns, the bike imbalance problem constantly occurs, especially for dockless bike sharing systems, causing significant impact on service quality and company revenue. Thus, it has become a critical task for bike sharing systems to resolve such imbalance efficiently. In this paper, we propose a novel deep reinforcement learning framework for incentivizing users to rebalance such sys- tems. We model this problem as a Markov decision process and take both spatial and temporal features into consideration. We develop a novel deep reinforcement learning algorithm called Hierarchical Reinforcement Pricing (HRP), which builds upon the Deep Deterministic Policy Gradient algorithm. Different from existing methods that often ignore spatial information and rely heavily on accurate prediction, HRP can capture both spatial and temporal dependencies using a divide-and-conquer structure with an embedded localized module. We conduct extensive experiments to evaluate HRP, based on a dataset from Mobike, a major Chinese dockless bike sharing company. Results show that HRP performs close to the 24-timeslot look-ahead optimization, and outperforms state-of-the-art methods in both service level and bike distribution. It also transfers well when applied to unseen areas.

Bioinformatics tools have been developed to interpret gene expression data at the gene set level, and these gene set based analyses improve the biologists' capability to discover functional relevance of their experiment design. While elucidating gene set individually, inter gene sets association is rarely taken into consideration. Deep learning, an emerging machine learning technique in computational biology, can be used to generate an unbiased combination of gene set, and to determine the biological relevance and analysis consistency of these combining gene sets by leveraging large genomic data sets. In this study, we proposed a gene superset autoencoder (GSAE), a multi-layer autoencoder model with the incorporation of a priori defined gene sets that retain the crucial biological features in the latent layer. We introduced the concept of the gene superset, an unbiased combination of gene sets with weights trained by the autoencoder, where each node in the latent layer is a superset. Trained with genomic data from TCGA and evaluated with their accompanying clinical parameters, we showed gene supersets' ability of discriminating tumor subtypes and their prognostic capability. We further demonstrated the biological relevance of the top component gene sets in the significant supersets. Using autoencoder model and gene superset at its latent layer, we demonstrated that gene supersets retain sufficient biological information with respect to tumor subtypes and clinical prognostic significance. Superset also provides high reproducibility on survival analysis and accurate prediction for cancer subtypes.