Conversational AI is a catch-all term for natural language models for artificial intelligence that can interpret human words, speak to people, or carry out tasks or computation with natural language. They can tell you jokes, answer factual questions, and even respond to multiple queries without the need to keep repeating a wake word, but conversation or chit-chat is still very much a human endeavor. To share progress towards deep learning designed to carry a conversation, Google today introduced Meena, a neural network with 2.6 billion parameters. Meena can handle multiturn dialogue, and Google claims it's better than other AI agents built for conversation and available online today. It even told an off-the-cuff joke.

You've heard it a million times: Americans don't care about our online privacy. Turns out that's not really true. Anxiety levels over privacy and security are peaking as the relentless collection of online data and the steady drumbeat of data incursions and breaches take a toll. People are worried like never before about eavesdropping by smart home devices such as Google Home and the Amazon Echo or having their microphone tapped to target them with personalized ads and increasingly they want a say over how their personal information gets used, according to a survey released Tuesday to observe Data Privacy Day. More than 8 in 10 American adults expect to have control over how a business handles their data, the survey released by privacy firm DataGrail found.

The News: Last week, Apple's acquisition of Xnor.ai was reported, no doubt aiming to deliver TinyML to edge devices. Xnor.ai, a Seattle startup specializing in low-power, edge-based artificial intelligence (AI) tools. Spun off from the Allen Institute for Artificial Intelligence, the three-year-old startup's technology embeds AI on the edge, enabling facial recognition, natural language processing, augmented reality, and other ML-driven capabilities to be executed on low-power devices rather than relying on the cloud. Analyst Take: Developers of AI applications for edge deployment are doing their work in a growing range of frameworks and deploying their models to myriad hardware, software, and cloud environments. This complicates the task of making sure that each new AI model is optimized for fast inferencing on its target platform, a burden that has traditionally required manual tuning.

Artificial intelligence (AI) is competent to have a revolutionary impact on businesses globally. Talking about the information technology sector, it is no longer merely about codifying business logic. Insight is indeed the modern currency, and the pace with which we all can scale that insight is the fundamental of value creation. As per a report by Gartner, AI is going to be one of the top investment preferences for over 30% of CIOs worldwide by 2020. A lot of corporations are yet in their initial phase in comprehending that how AI is scalable enough to transform their businesses.

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...