Neon, the artificial human prototype conceptualized by computer scientist and inventor Pranav Mistry, created waves recently. The President and CEO of Samsung's STAR Labs told ET in an exclusive interview that he created Neon because human beings are unable to connect with artificial intelligence (AI) assistants such as Apple's Siri. The Palanpur (Gujarat)-born Mistry, considered one of the best innovative minds in the world right now, said Neon will be a companion to the elderly and to those who are lonely and could even work as fashion models or news anchors. The 38-year-old also spoke about the dangers posed by AI,echoing Google parent Alphabet Inc's chief Sundar Pichai who recently called upon governments to regulate AI. Edited Excerpts: When you started thinking about Neon, what was the problem you were trying to solve?

The role of conversational assistants has become more prevalent in helping people increase their productivity. Document-centered assistance, for example to help an individual quickly review a document, has seen less significant progress, even though it has the potential to tremendously increase a user's productivity. This type of document-centered assistance is the focus of this paper. Our contributions are three-fold: (1) We first present a survey to understand the space of document-centered assistance and the capabilities people expect in this scenario. (2) We investigate the types of queries that users will pose while seeking assistance with documents, and show that document-centered questions form the majority of these queries. (3) We present a set of initial machine learned models that show that (a) we can accurately detect document-centered questions, and (b) we can build reasonably accurate models for answering such questions. These positive results are encouraging, and suggest that even greater results may be attained with continued study of this interesting and novel problem space. Our findings have implications for the design of intelligent systems to support task completion via natural interactions with documents.

Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach Lei Chen 1,2, Le Wu 1,2,, Richang Hong 1,2, Kun Zhang 3, Meng Wang 1,2 1 Key Laboratory of Knowledge Engineering with Big Data, Hefei University of Technology 2 School of Computer Science and Information Engineering, HeFei University of Technology 3 School of Computer Science and Technology, University of Science and Technology of China {chenlei.hfut,lewu.ustc, Abstract Graph Convolutional Networks (GCNs) are state-of-the-art graph based representation learning models by iteratively stacking multiple layers of convolution aggregation operations and nonlinear activation operations. Recently, in Collaborative Filtering (CF) based Recommender Systems (RS), by treating the user-item interaction behavior as a bipartite graph, some researchers model higher-layer collaborative signals with GCNs. These GCN based recommender models show superior performance compared to traditional works. However, these models suffer from training difficulty with nonlinear activations for large user-item graphs. Besides, most GCN based models could not model deeper layers due to the over smoothing effect with the graph convolution operation. In this paper, we revisit GCN based CF models from two aspects. First, we empirically show that removing non-linearities would enhance recommendation performance, which is consistent with the theories in simple graph convolutional networks. Second, we propose a residual network structure that is specifically designed for CF with user-item interaction modeling, which alleviates the over smoothing problem in graph convolution aggregation operation with sparse user-item interaction data.

With the explosive growth of online products and content, recommendation techniques have been considered as an effective tool to overcome information overload, improve user experience, and boost business revenue. In recent years, we have observed a new desideratum of considering long-term rewards of multiple related recommendation tasks simultaneously. The consideration of long-term rewards is strongly tied to business revenue and growth. Learning multiple tasks simultaneously could generally improve the performance of individual task due to knowledge sharing in multi-task learning. While a few existing works have studied long-term rewards in recommendations, they mainly focus on a single recommendation task. In this paper, we propose {\it PoDiRe}: a \underline{po}licy \underline{di}stilled \underline{re}commender that can address long-term rewards of recommendations and simultaneously handle multiple recommendation tasks. This novel recommendation solution is based on a marriage of deep reinforcement learning and knowledge distillation techniques, which is able to establish knowledge sharing among different tasks and reduce the size of a learning model. The resulting model is expected to attain better performance and lower response latency for real-time recommendation services. In collaboration with Samsung Game Launcher, one of the world's largest commercial mobile game platforms, we conduct a comprehensive experimental study on large-scale real data with hundreds of millions of events and show that our solution outperforms many state-of-the-art methods in terms of several standard evaluation metrics.

The need for transparency of predictive systems based on Machine Learning algorithms arises as a consequence of their ever-increasing proliferation in the industry. Whenever black-box algorithmic predictions influence human affairs, the inner workings of these algorithms should be scrutinised and their decisions explained to the relevant stakeholders, including the system engineers, the system's operators and the individuals whose case is being decided. While a variety of interpretability and explainability methods is available, none of them is a panacea that can satisfy all diverse expectations and competing objectives that might be required by the parties involved. We address this challenge in this paper by discussing the promises of Interactive Machine Learning for improved transparency of black-box systems using the example of contrastive explanations -- a state-of-the-art approach to Interpretable Machine Learning. Specifically, we show how to personalise counterfactual explanations by interactively adjusting their conditional statements and extract additional explanations by asking follow-up "What if?" questions. Our experience in building, deploying and presenting this type of system allowed us to list desired properties as well as potential limitations, which can be used to guide the development of interactive explainers. While customising the medium of interaction, i.e., the user interface comprising of various communication channels, may give an impression of personalisation, we argue that adjusting the explanation itself and its content is more important. To this end, properties such as breadth, scope, context, purpose and target of the explanation have to be considered, in addition to explicitly informing the explainee about its limitations and caveats...

The "Hey, Update My Voice" movement, in partnership with UNESCO, was born out of this context with the goal of teaching respect towards virtual assistants and, in addition, asking tech companies to update their assistants' responses. Because if that happens to them, imagine what happens in real life to real women. Every day around the world, virtual assistants suffer abuse and harassment of all kinds. In Brazil, for example, Lu, the virtual assistant of Magazine Luiza stores, has been victimized by this sort of violence. Worldwide, cases have been reported involving Siri and Alexa, among others.

Amazon and the UK's Royal National Institute of Blind People (RNIB) have worked together to make Alexa more useful to those suffering from visual impairment conditions. Thanks to this collaboration, the AI-powered personal assistant can offer advice on living with sight loss, obtained directly from RNIB's Sight Loss Advice Service. "Voice assistant technology is playing an ever-increasing role in transforming the lives of blind and partially sighted people," said David Clarke, director of services at RNIB. "Voice assistants can enable independence, helping to break down accessibility barriers to a more inclusive society. By using this technology to increase the reach of our own resources, we are ensuring that people can immediately get essential information about sight conditions, their rights, and the support available, simply by asking out loud." RNIB is a charity established in 1868, originally to provide better quality literature for the blind. Today, it offers information, support and advice to almost two million people in the UK, under the patronage of the Queen.

This project may be Apple's fallback to building its own car. From 2014 to 2019, roughly, Apple's Project Titan was a ground-up autonomous, electrified vehicle project. Apple found out that building a car is enormously complex, there are regulatory hurdles to clear far tougher than for phones or PCs, and you can't build a world-class auto factory in a couple of years. Apple also found out that not everyone wants to run a contract factory for Apple, including BMW and Daimler, and if there was an agreement, divorce court would have followed closely. Too many egos and everyone would want the final say.

Google is silently churning out many new technologies with wide ranging and far reaching impacts. The AI platform that Google is working on for quite some time has already been integrated with its digital assistant called Google Assistant that has been successfully leveraged into smart speakers of all types including the Google's own Google Home. But, this was just the beginning and company is on the verge of delivering something new this time. Google made tremendous research in the fields like natural language processing and synthesis. Google for quite some time is working on a new technology that can allow people more ease without relying too much on the Assistant. The company is trying to come up with something that doesn't require every project to incorporate Google Assistant for voice based commands.

We're constantly being bombarded with new and exciting technological developments – but few are as intriguing as the rise of artificial intelligence. Once the stuff of sci-fi stories, artificially intelligent devices are in homes around the world now, and this technology is a powerful force which needs forward-thinking professionals behind it. But what does human science have to do with any of this? We've teamed up with IE University and their School of Human Sciences and Technology to find out. Artificial intelligence and machines will most likely never be able to replicate emotional intelligence and human creativity.