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With the snow melting away and warmer temps making a comeback, you may be peering out your window wondering how in the world you're going to bring your lawn back from the dead. Breathing new life into your lawn may sound like a challenge after a long winter, but a smart sprinkler controller like our favorite, the Rachio 3 Smart Sprinkler Controller, can help take the guesswork out of bringing your grass back to a healthy shade of green for springtime. The second-generation Rachio 3 Smart Sprinkler Controller, pictured, is our favorite smart sprinkler because it offers a ton of easy-to-use options and isn't intimidating to install. In short, a smart sprinkler controller automates your in-ground sprinkler system's watering schedule and gives you control over the system from virtually anywhere via the companion app, which is especially convenient when you're out of town. Most smart sprinkler controllers can also be regulated using smart assistants like Amazon Alexa, Google Assistant, and Apple's Siri.

Accelerate Estonia and Garage48 hosted an online hackathon „Hack the crisis", which was inspired by the global COVID-19 crisis. Over 1000 specialists and enthusiasts joined forces this past weekend to develop solutions that could help Estonia to recover from the pandemic quickly and gain an edge in the post-crisis world. And all of it was done in just 48 hours! AlphaBlues alongside with other AI enthusiasts developed and launched Coronabot, a virtual assistant that gives information about COVID-19 and crisis management to Estonian people. In tough times like these, mass media usually gets over-saturated with constant news and information noise, which makes it hard for an average person to act knowingly and maintain peace. As people struggle finding relevant and trustworthy information on their own, they turn to national to the National Health Board and doctors who already are occupied with numerous cases and cannot handle all of the enquiries.

Voice assistants enable us to issue voice commands to our phones and even smart home devices. We can set alarms using our voice, search for a bookstore on Google, and even turn/off the sound system. Some of the most popular voice assistants include Apple's Siri, Google Assistant, Google Duplex, Amazon's Alexa, Samsung's Bixby, and Microsoft's Cortana. On the other hand, voice assistants recognize human speech using Natural Language Processing (NLP). The speech is then converted to numbers using machine language before a response is formulated and returned. Web services such as Email rely on machine learning algorithms to categorize messages into folders. This is an email filtering process that makes it easier for users to access the most important messages. In this case, the subject of the message is assessed and then the message is sent to the appropriate folder. An instance of this is the Gmail app that uses machine learning to arrange messages into folders such as primary, promotion, social.

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.