The search only for documents is outdated. Users who have already adopted a question-answering (QA) approach with their personal devices, e.g., those powered by Alexa, Google Assistant, Siri, etc., are also appreciating the advantages of using a "search engine" with the same approach in a business context. Doing so allows them to not only search for documents, but also obtain precise answers to specific questions. QA systems respond to questions that someone can ask in natural language. This technology is already widely adopted and now rapidly gaining importance in the business environment, where the most obvious added value of a conversational AI platform is improving the customer experience.

Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of Things (IoT) applications and services, spanning from recommendation systems to robotics control and military surveillance. This is driven by the easier access to sensory data and the enormous scale of pervasive/ubiquitous devices that generate zettabytes (ZB) of real-time data streams. Designing accurate models using such data streams, to predict future insights and revolutionize the decision-taking process, inaugurates pervasive systems as a worthy paradigm for a better quality-of-life. The confluence of pervasive computing and artificial intelligence, Pervasive AI, expanded the role of ubiquitous IoT systems from mainly data collection to executing distributed computations with a promising alternative to centralized learning, presenting various challenges. In this context, a wise cooperation and resource scheduling should be envisaged among IoT devices (e.g., smartphones, smart vehicles) and infrastructure (e.g. edge nodes, and base stations) to avoid communication and computation overheads and ensure maximum performance. In this paper, we conduct a comprehensive survey of the recent techniques developed to overcome these resource challenges in pervasive AI systems. Specifically, we first present an overview of the pervasive computing, its architecture, and its intersection with artificial intelligence. We then review the background, applications and performance metrics of AI, particularly Deep Learning (DL) and online learning, running in a ubiquitous system. Next, we provide a deep literature review of communication-efficient techniques, from both algorithmic and system perspectives, of distributed inference, training and online learning tasks across the combination of IoT devices, edge devices and cloud servers. Finally, we discuss our future vision and research challenges.

Last week, the United States Senate played host to a number of social media company VPs during hearings on the potential dangers presented by algorithmic bias and amplification. While that meeting almost immediately broke down into a partisan circus of grandstanding grievance airing, Democratic senators did manage to focus a bit on how these recommendation algorithms might contribute to the spread of online misinformation and extremist ideologies. The issues and pitfalls presented by social algorithms are well-known and have been well-documented. So, really, what are we going to do about it? "So I think in order to answer that question, there's something critical that needs to happen: we need more independent researchers being able to analyze platforms and their behavior," Dr. Brandie Nonnecke, Director of the CITRIS Policy Lab at UC Berkeley, told Engadget. Social media companies "know that they need to be more transparent in what's happening on their platforms, but I'm of the firm belief that, in order for that transparency to be genuine, there needs to be collaboration between the platforms and independent peer reviewed, empirical research."

With the increased use of AI methods to provide recommendations in the health, specifically in the food dietary recommendation space, there is also an increased need for explainability of those recommendations. Such explanations would benefit users of recommendation systems by empowering them with justifications for following the system's suggestions. We present the Food Explanation Ontology (FEO) that provides a formalism for modeling explanations to users for food-related recommendations. FEO models food recommendations, using concepts from the explanation domain to create responses to user questions about food recommendations they receive from AI systems such as personalized knowledge base question answering systems. FEO uses a modular, extensible structure that lends itself to a variety of explanations while still preserving important semantic details to accurately represent explanations of food recommendations. In order to evaluate this system, we used a set of competency questions derived from explanation types present in literature that are relevant to food recommendations. Our motivation with the use of FEO is to empower users to make decisions about their health, fully equipped with an understanding of the AI recommender systems as they relate to user questions, by providing reasoning behind their recommendations in the form of explanations.

Recent studies have shown that providing personalized explanations alongside recommendations increases trust and perceived quality. Furthermore, it gives users an opportunity to refine the recommendations by critiquing parts of the explanations. On one hand, current recommender systems model the recommendation, explanation, and critiquing objectives jointly, but this creates an inherent trade-off between their respective performance. On the other hand, although recent latent linear critiquing approaches are built upon an existing recommender system, they suffer from computational inefficiency at inference due to the objective optimized at each conversation's turn. We address these deficiencies with M&Ms-VAE, a novel variational autoencoder for recommendation and explanation that is based on multimodal modeling assumptions. We train the model under a weak supervision scheme to simulate both fully and partially observed variables. Then, we leverage the generalization ability of a trained M&Ms-VAE model to embed the user preference and the critique separately. Our work's most important innovation is our critiquing module, which is built upon and trained in a self-supervised manner with a simple ranking objective. Experiments on four real-world datasets demonstrate that among state-of-the-art models, our system is the first to dominate or match the performance in terms of recommendation, explanation, and multi-step critiquing. Moreover, M&Ms-VAE processes the critiques up to 25.6x faster than the best baselines. Finally, we show that our model infers coherent joint and cross generation, even under weak supervision, thanks to our multimodal-based modeling and training scheme.

Public knowledge graphs such as DBpedia and Wikidata have been recognized as interesting sources of background knowledge to build content-based recommender systems. They can be used to add information about the items to be recommended and links between those. While quite a few approaches for exploiting knowledge graphs have been proposed, most of them aim at optimizing the recommendation strategy while using a fixed knowledge graph. In this paper, we take a different approach, i.e., we fix the recommendation strategy and observe changes when using different underlying knowledge graphs. Particularly, we use different language editions of DBpedia. We show that the usage of different knowledge graphs does not only lead to differently biased recommender systems, but also to recommender systems that differ in performance for particular fields of recommendations.

One of the priorities announced in the 2021 Examination Priorities Report of the U.S. Securities and Exchange Commission's Division of Examinations ("EXAMS") is a review of robo-advisory firms that build client portfolios with exchange-traded funds ("ETF's") and mutual funds. EXAMS notes that these clients are almost entirely retail investors without investments large enough to support the costs of regular human investment advisers. EXAMS sees that the risks involved in these robo-advisor accounts pose particular issues, that retail clients may well not recognize. Accordingly, it may help to reflect on the Laws of Robotics invented by that science fiction author Isaac Asimov (for "I Robot," a short story in his 1950 collection), particularly the First Law: A robot may not injure a human being or, through inaction, allow a human being to come to harm. Investors may not understand the risks associated with specific investments; the risk profiles of mutual funds and of ETF's vary widely, from diversified to concentrated, from simple to complex strategies.

Amazon is holding one of its larger device sales in recent memory, and it's particularly good news if you want a smart display as an alarm clock or kitchen helper. The internet giant has cut the price of its Echo Show 5 to $50, well below its official $90 price and just $5 higher than the best deal we've seen so far. You'll also find the swivelling Echo Show 10 back to its record-low $200, while the mid-tier Echo Show 8 has dropped to $75 from its original $125. You'll also find discounts on Amazon's other media devices. The Fire TV Stick Lite has received a slight discount to $25, while the Stick 4K has dipped to $38.

Conversational interfaces to Business Intelligence (BI) applications enable data analysis using a natural language dialog in small incremental steps. To truly unleash the power of conversational BI to democratize access to data, a system needs to provide effective and continuous support for data analysis. In this paper, we propose BI-REC, a conversational recommendation system for BI applications to help users accomplish their data analysis tasks. We define the space of data analysis in terms of BI patterns, augmented with rich semantic information extracted from the OLAP cube definition, and use graph embeddings learned using GraphSAGE to create a compact representation of the analysis state. We propose a two-step approach to explore the search space for useful BI pattern recommendations. In the first step, we train a multi-class classifier using prior query logs to predict the next high-level actions in terms of a BI operation (e.g., {\em Drill-Down} or {\em Roll-up}) and a measure that the user is interested in. In the second step, the high-level actions are further refined into actual BI pattern recommendations using collaborative filtering. This two-step approach allows us to not only divide and conquer the huge search space, but also requires less training data. Our experimental evaluation shows that BI-REC achieves an accuracy of 83% for BI pattern recommendations and up to 2X speedup in latency of prediction compared to a state-of-the-art baseline. Our user study further shows that BI-REC provides recommendations with a precision@3 of 91.90% across several different analysis tasks.

The idea of Artificial Intelligence dominating the world is a scary thought. Imagine if there were tech humans which are also known as AI's, working alongside us to help with anything we need! A future dominated by artificial intelligence isn't a far-fetched fantasy. "In 2029, a machine can pass the Turing Test, according to Ray Kurzweil." This ensures that a device would exhibit intelligent behavior comparable to, if not identical to, that of a person. Who is father of artificial intelligence?