An infamous intelligent robot that escaped from his lab twice has shaken hands with Vladimir Putin. The Russian president was touring an IT exhibition in Perm, which is becoming known as the country's Silicon Valley, when he met Promobot. The big-eyed, Russian-made machine is said to have immediately recognised Putin and enthusiastically introduced himself with a handshake. Promobot is used as a guide, model and promotional salesman across Russia and keeps a database of prominent public figures. However, the android has hit the headlines in the last year for his escape attempts.

A group of researchers from the UK and India have developed an AI that can identify people using deep learning to enhance facial recognition capabilities, even when certain physical features are obscured. Translation: you could be identified from surveillance footage and photos, no matter if you're covering your face with a hat, scarf, sunglasses or a beard. The deep learning system looks at 14 points on a face and measures the distances between them in order to recognize people. By training the AI with two datasets containing 2,000 images each – one with simple backgrounds and another with more varied elements, including multiple people per picture – the team has been able to achieve a 56 percent success rate in identifying subjects in disguise. So yes, the technology hasn't been perfected yet.

China will unveil a slate of policies, including tax cuts and setting up national artificial intelligence innovation centers, to beef up support for AI and clear legal risks, local media reported. The Ministry of Industry and Information Technology is teaming up with other ministries and related departments to draft new policies to better cultivate the AI industry, Economic Information Daily reported on Thursday. Favorable tax policies will be rolled out to encourage small and medium-sized enterprises that are working on AI. More efforts will also be made to open government data and experiment with new ways to tap into data, the report said. Meanwhile, new policies will be unveiled to channel more resources into AI research, in the hope of advancing innovation capability.

Answer Set Programming (ASP) is a well-known paradigm of declarative programming with roots in logic programming and non-monotonic reasoning. Similar to other closely related problem-solving technologies, such as SAT/SMT, QBF, Planning and Scheduling, advancements in ASP solving are assessed in competition events. In this paper, we report about the design and results of the Sixth ASP Competition, which was jointly organized by the University of Calabria (Italy), Aalto University (Finland), and the University of Genoa (Italy), in affiliation with the 13th International Conference on Logic Programming and Non-Monotonic Reasoning. This edition maintained some of the design decisions introduced in 2014, e.g., the conception of sub-tracks, the scoring scheme, and the adherence to a fixed modeling language in order to push the adoption of the ASP-Core-2 standard. On the other hand, it featured also some novelties, like a benchmark selection stage classifying instances according to their empirical hardness, and a "Marathon" track where the top-performing systems are given more time for solving hard benchmarks.

We propose Deep Asymmetric Multitask Feature Learning (Deep-AMTFL) which can learn deep representations shared across multiple tasks while effectively preventing negative transfer that may happen in the feature sharing process. Specifically, we introduce an asymmetric autoencoder term that allows reliable predictors for the easy tasks to have high contribution to the feature learning while suppressing the influences of unreliable predictors for more difficult tasks. This allows the learning of less noisy representations, and enables unreliable predictors to exploit knowledge from the reliable predictors via the shared latent features. Such asymmetric knowledge transfer through shared features is also more scalable and efficient than inter-task asymmetric transfer. We validate our Deep-AMTFL model on multiple benchmark datasets for multitask learning and image classification, on which it significantly outperforms existing symmetric and asymmetric multitask learning models, by effectively preventing negative transfer in deep feature learning.

This paper presents a Semantic Attribute Modulation (SAM) for language modeling and style variation. The semantic attribute modulation includes various document attributes, such as titles, authors, and document categories. We consider two types of attributes, (title attributes and category attributes), and a flexible attribute selection scheme by automatically scoring them via an attribute attention mechanism. The semantic attributes are embedded into the hidden semantic space as the generation inputs. With the attributes properly harnessed, our proposed SAM can generate interpretable texts with regard to the input attributes. Qualitative analysis, including word semantic analysis and attention values, shows the interpretability of SAM. On several typical text datasets, we empirically demonstrate the superiority of the Semantic Attribute Modulated language model with different combinations of document attributes. Moreover, we present a style variation for the lyric generation using SAM, which shows a strong connection between the style variation and the semantic attributes.

Five years ago, in 2012, I decided to experiment in improving my teaching by creating a flipped classroom (and semi-MOOC) for my course "Business Analytics Using Data Mining" (BADM) at the Indian School of Business. I initially designed the course at University of Maryland's Smith School of Business in 2005 and taught it until 2010. When I joined ISB in 2011 I started teaching multiple sections of BADM (which was started by Ravi Bapna in 2006), and the course was fast growing in popularity. Repeating the same lectures in multiple course sections made me realize it was time for scale! I therefore created 30 videos, covering various supervised methods (k-NN, linear and logistic regression, trees, naive Bayes, etc.) and unsupervised methods (principal components analysis, clustering, association rules), as well as important principles such as performance evaluation, the notion of a holdout set, and more.

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The UAE investments in artificial intelligence (AI) has seen significant growth in the last three years and now analysts predict that it will reach $9bn by the end of 2017. WAM reported that Abdullah Alfan Al Shamsi, assistant Under-Secretary for Industrial Affairs, Ministry of Economy, said official statistics purporting that scientific research contributions to the country's GDP reached 0.87%. "The UAE has been among the first countries to realise the central importance of artificial intelligence to build a knowledge-based economy that adopts scientific research and high-end technology as among the key enabler of the UAE Vision 2021," he added. With AI investments to reach $9bn, Al Shamsi noted that the emerging technology will enhance competitiveness across all economic platforms, particularly industrialisation. Additionally AI will continue to emerge in government services, consumer services and work environments.

The service debuted in India in April 2016, and has since made its way to Singapore, and now Indonesia. The platform uses biometric user authentication and a mobile interface based on Kasisto's AI assistant platform, KAI Banking, which is designed to offer conversational interactivity supporting English and Bahasa Indonesia languages. The expansion of this platform may point to a larger trend underway in mobile fintech, with Sensory having recently announced a virtual bank teller AI system that can respond to natural speech inquiries, and even features a digital avatar that dynamically animates its responses.