Welcome to the latest episode of our Weekly Wrap series, for the week ending Friday, March 13, 2020.

Life's most valuable asset is health. Continuously understanding the state of our health and modeling how it evolves is essential if we wish to improve it. Given the opportunity that people live with more data about their life today than any other time in history, the challenge rests in interweaving this data with the growing body of knowledge to compute and model the health state of an individual continually. This dissertation presents an approach to build a personal model and dynamically estimate the health state of an individual by fusing multi-modal data and domain knowledge. The system is stitched together from four essential abstraction elements: 1. the events in our life, 2. the layers of our biological systems (from molecular to an organism), 3. the functional utilities that arise from biological underpinnings, and 4. how we interact with these utilities in the reality of daily life. Connecting these four elements via graph network blocks forms the backbone by which we instantiate a digital twin of an individual. Edges and nodes in this graph structure are then regularly updated with learning techniques as data is continuously digested. Experiments demonstrate the use of dense and heterogeneous real-world data from a variety of personal and environmental sensors to monitor individual cardiovascular health state. State estimation and individual modeling is the fundamental basis to depart from disease-oriented approaches to a total health continuum paradigm. Precision in predicting health requires understanding state trajectory. By encasing this estimation within a navigational approach, a systematic guidance framework can plan actions to transition a current state towards a desired one. This work concludes by presenting this framework of combining the health state and personal graph model to perpetually plan and assist us in living life towards our goals.

Deep learning-based recommendation models are used pervasively and broadly, for example, to recommend movies, products, or other information most relevant to users, in order to enhance the user experience. Among various application domains which have received significant industry and academia research attention, such as image classification, object detection, language and speech translation, the performance of deep learning-based recommendation models is less well explored, even though recommendation tasks unarguably represent significant AI inference cycles at large-scale datacenter fleets. To advance the state of understanding and enable machine learning system development and optimization for the commerce domain, we aim to define an industry-relevant recommendation benchmark for the MLPerf Training andInference Suites. The paper synthesizes the desirable modeling strategies for personalized recommendation systems. We lay out desirable characteristics of recommendation model architectures and data sets. We then summarize the discussions and advice from the MLPerf Recommendation Advisory Board.

This work aims to explore the quality of cold-start recommendations derived from collective matrix factorization models [11] in collaborative filtering with explicit-feedback data in the form of ratings. Recommender systems based on collaborative filtering are typically constructed solely based on data about useritem interactions [6], such as movies rated by different users, which result in domain-independent and easily-implementable models, but have the disadvantage of only being able to make recommendations about users and items for which there is interactions data available (known as warm-start recommendations in the literature). In many settings however, there is oftentimes additional side information available about users and/or items, which is not used in the most common models such as low-rank matrix factorization [6] or kNN-based formulas [10], but which can be used both to improve recommendation models that take interactions data, and to make recommendations in the absence of interactions data (so-called cold-start recommendations). This work focuses on the second case: studying recommendations from matrix factorization models that are based on attributes data without interactions data.

In today's world that we live in, it seems as if every industry is using artificial intelligence in one way or another and raving about its benefits. Artificial intelligence has made it possible for machines to receive information, process it using the record of past patterns in their database, and perform tasks that could previously only be performed by humans. From automated systems to self-driving cars and smart applications, there are many examples of artificial intelligence that we come across every day. However, the concept still appears to be unclear to most people. The common man does not know what AI is, how it used in different industries, and the fantastic benefits it has to offer.

When Kursat Ceylan, who is blind, was trying to find his way to a hotel, he used an app on his phone for directions, but also had to hold his cane and pull his luggage. He ended up walking into a pole, cutting his forehead. This inspired him to develop, along with a partner, Wewalk - a cane equipped with artificial intelligence (AI), that detects objects above chest level and pairs with apps including Google Maps and Amazon's Alexa, so the user can ask questions. Jean Marc Feghali, who helped to develop the product, also has an eye condition. In his case his vision is severely impaired when the light is not good. While the smart cane itself only integrates with basic AI functions right now, the aim is for Wewalk, to use information gathered from the gyroscope, accelerometer and compass installed inside the cane.

Every day, a large portion of the population is at the mercy of a rising technology, yet few actually understand what it is artificial intelligence. You know, HAL 9000 and Marvin the Paranoid Android? Thanks to books and movies, each generation has formed its own fantasy of a world ruled (or at least served) by robots. We've been conditioned to expect flying cars that steer clear of traffic and robotic maids whipping up our weekday dinner. But if the age of AI is here, why don't our lives look more like the Jetson's?

Conversational AI is a huge technology advancement – as momentous as the unveiling of the Internet in 1983 or when Steve Jobs launched the iPhone in January 2007. But within the last year or two, Conversational AI has evolved into a cornerstone of innovation. Gone are the days of single-use chatbots that execute pre-scripted, single-path programs or recite your service manual to customers. With Conversational AI, we are talking about complex, Machine Learning (ML)-powered, intelligent Digital Assistants that can drive unmatched customer and employee interactions based on the current context, past history, even predicting the flow of conversations and delivering next best actions – based on naturally expressive voice or text. Where the engagement with a digital assistant is intelligent enough that you think of it as a "cobot" – a co-pilot in your journey as a customer, employee, vendor or partner.

Technology is a tool that helps us live out our God-given callings. This is one of the most important things for us to learn as we engage the topic of technology and artificial intelligence. Because we often see the tremendous power that technology has over our lives, we are tempted to treat technology as more than a tool, as something with a value similar to our own if it is powerful enough or does enough work on its own. Technology will be misused and abused by broken people just like you and me. Nowhere in Scripture is a tool or a technology condemned for being evil.

Hinge is going to give people in the US a $100 Visa online gift card to go on date, a decision that'll presumably encourage them to schedule plans while also helping the company market itself and grow. The dating app previously partnered with bars to give its daters discounts, but this is the first time the company has given people what essentially amounts to cash. The company has $25,000, or enough money for 250 daters, set aside for the promotion. To qualify, Hinge users have to pause their accounts from 4PM ET on Friday, March 6th until 4PM ET on Saturday, March 7th. After reactivating, people have to click on their date's profile and select that they "met," which tells the app that they met in-person and prompts it to ask whether this is the type of person this dater would want to see again.