Why it matters: Alibaba says that over the last year, it has redone about 1 million mom and pop shops like the Huangs' across China. It has done the same with about a hundred superstores. Big and small, these outlets buy all their goods through Alibaba's platform and pay using its affiliate Alipay app. A picture of the future: These stores are examples of an expanding battleground among China's cutthroat tech giants, and petri-dishes of the future of business around the world. Among the central ideas is that in the future, shoppers will not view e-commerce and brick-and-mortar as distinct things, but as a single merged organism -- as simply "commerce."

Mobile wallet company Mobikwik has announced today that it has invested Rs 2 crore in Pune-based Pivotchain Solutions, a data science startup focused on deep learning and advanced analytics. Founded in 2017 by Deepak Rao and Yogendra Pratap Singh, Pivotchain offers blockchain based asset registry platform and within a year of its existence, the startup is working closely with banking, insurance, retail and government sectors globally. Notably, Pivotchain has also built exclusive AI and deep learning models for MobiKwik and these models, Mobkwik official's statement, will be instrumental for MobiKwik as it rolls out numerous lending products to address the credit requirements of its user base. MobiKwik founder and CEO Bipin Preet Singh said the company's focus on delivering high quality fintech products will require immense focus on data, and an in-depth understanding of the user requirements across categories. "Pivotchain is doing incredible work in alternate data scoring, predictive modelling and risk management and this investment will give us an edge over competition. We will continue to invest in companies that can add value to our business," he said.

Technologically, the 20-year jump from 2015 to 2035 will be huge. During that time some elements of our world will change beyond recognition while others will stay reassuringly (or disappointingly) familiar. Consider the 20 years to 2015. Back in 1995 we were in the early days of the internet, we worked in cubicles and our computers were chunky and powered by Windows 95. There were no touch screen phones or flat screen TVs; people laughed at the idea of reading electronic books, and watching a home movie meant loading a clunky cassette into your VCR.

The US, China, Singapore, Greece and Japan – All have been gunning autonomous vehicles since years now. Now Israel is another country to join the club. Israel has become a focus for car technology in recent years. Large companies such as General Motors, Toyota, Skoda, Volvo, BMW, Honda, Hyundai and some others have built R&D centers in Israel to develop self-driving cars. Israeli startups don't find themselves much behind in this race of driverless tech.

China is currently testing unmanned, miniaturized assault boats that could be used by the People's Liberation Army (PLA) to attack enemies at sea. Artificial intelligence technology that has traditionally been implemented in aerial drones will be used in the boats. "Once equipped with weapons, unmanned small combat vessels can attack the enemy in large numbers, similar to drones," said Li Jie, a Beijing-based naval expert. The prototypes, which resemble shark fins, were developed in a collaboration between a Guangdong-based tech company and the PLA. China isn't alone in developing unmanned vehicles, as the United States and other Western counties are working on creating "ant swarms" for operations on the ground, "drone swarms" for aerial operations and "shark swarms" for the sea.

Recent work has shown that collaborative filter-based recommender systems can be improved by incorporating side information, such as natural language reviews, as a way of regularizing the derived product representations. Motivated by the success of this approach, we introduce two different models of reviews and study their effect on collaborative filtering performance. While the previous state-of-the-art approach is based on a latent Dirichlet allocation (LDA) model of reviews, the models we explore are neural network based: a bag-of-words product-of-experts model and a recurrent neural network. We demonstrate that the increased flexibility offered by the product-of-experts model allowed it to achieve state-of-the-art performance on the Amazon review dataset, outperforming the LDA-based approach. However, interestingly, the greater modeling power offered by the recurrent neural network appears to undermine the model's ability to act as a regularizer of the product representations.

Recently, a unified model for image-to-image translation tasks within adversarial learning framework has aroused widespread research interests in computer vision practitioners. Their reported empirical success however lacks solid theoretical interpretations for its inherent mechanism. In this paper, we reformulate their model from a brand-new geometrical perspective and have eventually reached a full interpretation on some interesting but unclear empirical phenomenons from their experiments. Furthermore, by extending the definition of generalization for generative adversarial nets to a broader sense, we have derived a condition to control the generalization capability of their model. According to our derived condition, several practical suggestions have also been proposed on model design and dataset construction as a guidance for further empirical researches.

Csaba Szepesvari Deepmind PAC-Bayes bounds have been proposed to get risk estimates based on a training sample. In this paper the PAC-Bayes approach is combined with stability of the hypothesis learned by a Hilbert space valued algorithm. The PAC-Bayes setting is used with a Gaussian prior centered at the expected output. Thus a novelty of our paper is using priors defined in terms of the data-generating distribution. Our main result estimates the risk of the randomized algorithm in terms of the hypothesis stability coefficients. We also provide a new bound for the SVM classifier, which is compared to other known bounds experimentally. Ours appears to be the first stability-based bound that evaluates to nontrivial values.

Incremental learning from non-stationary data poses special challenges to the field of machine learning. Although new algorithms have been developed for this, assessment of results and comparison of behaviors are still open problems, mainly because evaluation metrics, adapted from more traditional tasks, can be ineffective in this context. Overall, there is a lack of common testing practices. This paper thus presents a testbed for incremental non-stationary learning algorithms, based on specially designed synthetic datasets. Also, test results are reported for some well-known algorithms to show that the proposed methodology is effective at characterizing their strengths and weaknesses. It is expected that this methodology will provide a common basis for evaluating future contributions in the field.

Modern online platforms rely on effective rating systems to learn about items. We consider the optimal design of rating systems that collect {\em binary feedback} after transactions. We make three contributions. First, we formalize the performance of a rating system as the speed with which it recovers the true underlying ranking on items (in a large deviations sense), accounting for both items' underlying match rates and the platform's preferences. Second, we provide an efficient algorithm to compute the binary feedback system that yields the highest such performance. Finally, we show how this theoretical perspective can be used to empirically design an implementable, approximately optimal rating system, and validate our approach using real-world experimental data collected on Amazon Mechanical Turk.