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While automation and digital technologies are disrupting the workplace as we traditionally know it, it has become imperative for organisations to start reskilling their workforce. Automation and artificial intelligence are not only disrupting the assembly lines but right across the so-called blue-collar jobs. This is mainly because in most instances, artificial intelligence (AI), is actually doing a better job than humans. For example, the use of virtual assistants in the workplace is growing. By 2021, Gartner predicts that 25 percent of digital workers will use a virtual employee assistant on a daily basis.

Some people associate AI with science fiction robots, but in reality, the technology streamlines and improves many day-to-day processes, allowing us to accomplish more in less time. Today, AI plays a role in many aspects of our daily lives, from commuting to shopping to browsing the web. AI software development is a massive market, and the technology is improving all the time. Experts predict that the AI social media market will grow from $633 million in 2018 to $2.1 billion in 2023. As both artificial intelligence and social media become more prevalent in people's lives, the intersection of the two will also become more popular.

Explainability has been an important goal since the early days of Artificial Intelligence. Several approaches for producing explanations have been developed. However, many of these approaches were tightly coupled with the capabilities of the artificial intelligence systems at the time. With the proliferation of AI-enabled systems in sometimes critical settings, there is a need for them to be explainable to end-users and decision-makers. We present a historical overview of explainable artificial intelligence systems, with a focus on knowledge-enabled systems, spanning the expert systems, cognitive assistants, semantic applications, and machine learning domains. Additionally, borrowing from the strengths of past approaches and identifying gaps needed to make explanations user- and context-focused, we propose new definitions for explanations and explainable knowledge-enabled systems.

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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.