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Traditional Gyms v/s Smart Gyms: Winner of the Fitness Industry

#artificialintelligence

IoT and artificial intelligence are two of the major disruptive technologies that have penetrated into the health and fitness industry in recent years. People are now more concerned with their health and fitness to keep their bodies fit from many potential diseases such as diabetes, thyroid, and many more. To keep up with the huge demand, the fitness industry has started transforming traditional gyms into smart gyms by implementing AI models in gyms. People are getting attracted to smart gyms to become more fit and healthy to raise the standard of living. The fitness industry is booming in the 21st century with the help of these user-friendly disruptive technologies.


Anticipating Safety Issues in E2E Conversational AI: Framework and Tooling

arXiv.org Artificial Intelligence

Over the last several years, end-to-end neural conversational agents have vastly improved in their ability to carry a chit-chat conversation with humans. However, these models are often trained on large datasets from the internet, and as a result, may learn undesirable behaviors from this data, such as toxic or otherwise harmful language. Researchers must thus wrestle with the issue of how and when to release these models. In this paper, we survey the problem landscape for safety for end-to-end conversational AI and discuss recent and related work. We highlight tensions between values, potential positive impact and potential harms, and provide a framework for making decisions about whether and how to release these models, following the tenets of value-sensitive design. We additionally provide a suite of tools to enable researchers to make better-informed decisions about training and releasing end-to-end conversational AI models.


WHO deploys virtual assistant called Florence to help smokers quit - Actu IA

#artificialintelligence

As part of the World No Tobacco Day, the World Health Organization (WHO) has multiplied initiatives to fight against smoking. The dissemination of digital tools and exploitation of artificial intelligence are two of the aspects on which the WHO wishes to rely to fight against tobacco. The UN agency specialized in public health has partnered with WhatsApp, Facebook, Viber, Soul Machines and AI Company to offer a virtual assistant to help consumers in their desire to quit smoking. The "Commit to Quit" campaign was launched by WHO to support tobacco users who want to quit but need help to do so. In 29 target countries, the agency has agreed on initiatives such as national awareness campaigns, new digital tools, policy reviews, youth engagement, training of health workers, opening of new smoking cessation clinics, support for nicotine replacement therapy through WHO partners, free telephone support services, and provision of smoking cessation courses.


Zero-Shot Recommender Systems

arXiv.org Artificial Intelligence

Performance of recommender systems (RS) relies heavily on the Many large scale e-commerce platforms (such as Etsy, Overstock, amount of training data available. This poses a chicken-and-egg etc) and online content platforms (such as Spotify, Overstock, Disney, problem for early-stage products, whose amount of data, in turn, Netflix, etc) have such a large inventory of items that showcasing relies on the performance of their RS. On the other hand, zero-shot all of them in front of their users is simply not practical. In learning promises some degree of generalization from an old dataset particular, in the online content category of businesses, it is often to an entirely new dataset. In this paper, we explore the possibility seen that users of their service do not have a crisp intent in mind of zero-shot learning in RS. We develop an algorithm, dubbed ZEro-unlike in the retail shopping experience where the users often have Shot Recommenders (ZESRec), that is trained on an old dataset a crisp intent of purchasing something. The need for personalized and generalize to a new one where there are neither overlapping recommendations therefore arises from the fact that not only it is users nor overlapping items, a setting that contrasts typical crossdomain impractical to show all the items in the catalogue but often times RS that has either overlapping users or items. Different users of such services need help discovering the next best thing from categorical item indices, i.e., item ID, in previous methods, -- be it the new and exciting movie or be it a new music album or ZESRec uses items' natural-language descriptions (or description even a piece of merchandise that they may want to consider for embeddings) as their continuous indices, and therefore naturally future buying if not immediately.


From Human-Computer Interaction to Human-AI Interaction: New Challenges and Opportunities for Enabling Human-Centered AI

arXiv.org Artificial Intelligence

While AI has benefited humans, it may also harm humans if not appropriately developed. We conducted a literature review of current related work in developing AI systems from an HCI perspective. Different from other approaches, our focus is on the unique characteristics of AI technology and the differences between non-AI computing systems and AI systems. We further elaborate on the human-centered AI (HCAI) approach that we proposed in 2019. Our review and analysis highlight unique issues in developing AI systems which HCI professionals have not encountered in non-AI computing systems. To further enable the implementation of HCAI, we promote the research and application of human-AI interaction (HAII) as an interdisciplinary collaboration. There are many opportunities for HCI professionals to play a key role to make unique contributions to the main HAII areas as we identified. To support future HCI practice in the HAII area, we also offer enhanced HCI methods and strategic recommendations. In conclusion, we believe that promoting the HAII research and application will further enable the implementation of HCAI, enabling HCI professionals to address the unique issues of AI systems and develop human-centered AI systems.


Semantic Modeling for Food Recommendation Explanations

arXiv.org Artificial Intelligence

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.


BI-REC: Guided Data Analysis for Conversational Business Intelligence

arXiv.org Artificial Intelligence

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.


Google Nest Hub (2nd gen) review: wearable-free sleep tracking smart display

The Guardian

Google's second-generation Nest Hub smart display now comes with radar-based sleep tracking as it attempts to keep Amazon's Alexa at bay. The new Nest Hub costs £89.99 on launch, which makes it cheaper than its predecessor and slightly undercuts competitors of a similar size. The second-generation unit has the same design as the original but is ever-so-slightly taller. The 7in LCD screen looks great and is crisp enough for viewing at arm's length or further, making it perfect for use as a digital photo frame. The body is now made of recycled plastic and the screen is covered in an edgeless glass, which makes it easier to wipe clean.


Using a Personal Health Library-Enabled mHealth Recommender System for Self-Management of Diabetes Among Underserved Populations: Use Case for Knowledge Graphs and Linked Data

arXiv.org Artificial Intelligence

Personal health libraries (PHLs) provide a single point of secure access to patients digital health data and enable the integration of knowledge stored in their digital health profiles with other sources of global knowledge. PHLs can help empower caregivers and health care providers to make informed decisions about patients health by understanding medical events in the context of their lives. This paper reports the implementation of a mobile health digital intervention that incorporates both digital health data stored in patients PHLs and other sources of contextual knowledge to deliver tailored recommendations for improving self-care behaviors in diabetic adults. We conducted a thematic assessment of patient functional and nonfunctional requirements that are missing from current EHRs based on evidence from the literature. We used the results to identify the technologies needed to address those requirements. We describe the technological infrastructures used to construct, manage, and integrate the types of knowledge stored in the PHL. We leverage the Social Linked Data (Solid) platform to design a fully decentralized and privacy-aware platform that supports interoperability and care integration. We provided an initial prototype design of a PHL and drafted a use case scenario that involves four actors to demonstrate how the proposed prototype can be used to address user requirements, including the construction and management of the PHL and its utilization for developing a mobile app that queries the knowledge stored and integrated into the PHL in a private and fully decentralized manner to provide better recommendations. The proposed PHL helps patients and their caregivers take a central role in making decisions regarding their health and equips their health care providers with informatics tools that support the collection and interpretation of the collected knowledge.


A smart cuckoo clock might be too weird even for Amazon

Engadget

Amazon announced its "Day 1" hardware program last year as a way to build unusual hardware products in limited quantities, get feedback from users and eventually make them more widely available. In fact, Amazon's Echo Frames are the first bit of hardware to "graduate" from Day 1 to general availability. Now, the company is expanding Day 1 with a new program called "Built It." Like Day 1, Built It features some unconventional devices, but it's directly taking consumer interest into account when deciding whether to sell the products at all in a way that's similar to what Kickstarter and Indiegogo have been doing. The first three Built It products -- a cuckoo clock, smart nutrition scale and sticky note printer -- were announced today. You can order them now, but Amazon will only make them if they hit a sales goal by March 19th.