With Novel Coronavirus (2019-nCov) causing a bigger number of deaths than the 2003 SARS episode and giving no indications of containment, one thing turns out to be clear: the sickness is out of our control at this moment and we will need to get innovative if we need to catch it. The illness began in China back in December and keeping in mind that there's been a lot of discussion around how it was taken care of, it's essential to perceive that our energy is best spent discovering solutions. Presently, like never before, the world needs to meet up. We need to deliver the best personalities in healthcare and technology and innovate in case we will outflank this sickness. The ongoing coronavirus outbreak (or COVID-19, if you need to be progressively precise) is changing the manner in which individuals live their day-to-day lives and interact with one another. It's additionally affecting how organizations work, as employees request to work from home and the condition of supply chains changes erratically.

Air pollution kills an estimated seven million people every year and cities around the world are being forced to take action to do what they can to lower the risk to inhabitants. A team of Loughborough University computer scientists believe their AI system has the potential to provide new insight into the environmental factors that have significant impacts on air pollution levels. In particular it focuses on the amount of'PM2.5' In 2013, a study involving 312,944 people in nine European countries revealed that there was no safe level of particulates. PM2.5 particulates were found to be particularly deadly, blamed for a 36 per cent increase in lung cancer per 10 μg/m3 as they can penetrate deep into the lungs.

In 2018, California's state government began rolling out a new "mental health" initiative. The tech companies of Silicon Valley were creating smartphone apps that could prompt users to seek mental health care, and the state wanted to provide support. After all, researchers claim that more than half of Americans with mental health problems don't receive treatment, and one reason for that might be that treatment is expensive or unavailable in certain regions. Of the thousands of mental health apps in existence today, the state selected two. The first app is called 7 Cups, by a company called 7 Cups of Tea. They're focused on connecting mental health service users, in text-based chat sessions, with what they call "listeners"--volunteers who are trained in "active listening." But, according to The New York Times, the company has been plagued with issues, including listeners having inappropriate conversations with their clients and investigations of its alleged financial misconduct. The other company partnering with the state of California is Mindstrong Health. Their app (branded Mindstrong on March 17, 2020, previously known as Health) is available on the Google Play Store and the Apple App Store. However, you can only use the app if you have been given a code to participate by one of the health insurance companies they've partnered with. The company won't tell you which companies they work with--it's by invitation only.

In 2018, California's state government began rolling out a new "mental health" initiative. The tech companies of Silicon Valley were creating smartphone apps that could prompt users to seek mental health care, and the state wanted to provide support. After all, researchers claim that more than half of Americans with mental health problems don't receive treatment, and one reason for that might be that treatment is expensive or unavailable in certain regions. Of the thousands of mental health apps in existence today, the state selected two. The first app is called 7 Cups, by a company called 7 Cups of Tea. They're focused on connecting mental health service users, in text-based chat sessions, with what they call "listeners"--volunteers who are trained in "active listening." But, according to The New York Times, the company has been plagued with issues, including listeners having inappropriate conversations with their clients and investigations of its alleged financial misconduct. The other company partnering with the state of California is Mindstrong Health. Their app (branded Mindstrong on March 17, 2020, previously known as Health) is available on the Google Play Store and the Apple App Store. However, you can only use the app if you have been given a code to participate by one of the health insurance companies they've partnered with. The company won't tell you which companies they work with--it's by invitation only.

The Centers for Disease Control is using a health care bot to help the agency screen for coronavirus infections. The bot, developed by Microsoft and provided to the CDC for free, can assess symptoms and risk factors for people who fear they may have COVID-19, the respiratory disease caused by coronavirus. A Microsoft spokesperson said March 20 that the bot will be initially be available on the CDC website for people in select areas but will expand over time. The bot is meant to reduce the strain on the health care providers by reducing the amount of patients who come to facilities thinking they may have the virus. It uses artificial intelligence to allow the "CDC and other frontline organizations respond to these inquiries, freeing up doctors, nurses, administrators and other healthcare professionals to provide critical care to those who need it."

University of Massachusetts Amherst researchers have invented a portable surveillance device powered by machine learning - called FluSense - which can detect coughing and crowd size in real time, then analyze the data to directly monitor flu-like illnesses and influenza trends. The FluSense creators say the new edge-computing platform, envisioned for use in hospitals, healthcare waiting rooms and larger public spaces, may expand the arsenal of health surveillance tools used to forecast seasonal flu and other viral respiratory outbreaks, such as the COVID-19 pandemic or SARS. Models like these can be lifesavers by directly informing the public health response during a flu epidemic. These data sources can help determine the timing for flu vaccine campaigns, potential travel restrictions, the allocation of medical supplies and more. "This may allow us to predict flu trends in a much more accurate manner," says co-author Tauhidur Rahman, assistant professor of computer and information sciences, who advises Ph.D. student and lead author Forsad Al Hossain.

The hot topic which has been in our minds lately is the novel Coronavirus, or the COVID-19. It has affected the lives of many of us, and caused a huge turmoil across financial markets. As a data scientist there is one thing I can always rely on, even in these troubled times -- data. Luckily, data on the COVID-19 spread can be found online (e.g data.humdata.org). While there have been many fields in which data science and machine learning methods have practically solved long-standing problems (language translation, image recognition and sentiment analysis, among many others), there is no possibility that these methods will magically "solve" the Coronavirus crisis; and yet– a better understanding of its behavior, a more careful analysis based on data and meaningful predictions can be beneficial for decision making and policy setting.

Reading through a data science book or taking a course, it can feel like you have the individual pieces, but don't quite know how to put them together. Taking the next step and solving a complete machine learning problem can be daunting, but preserving and completing a first project will give you the confidence to tackle any data science problem. This series of articles will walk through a complete machine learning solution with a real-world dataset to let you see how all the pieces come together. We'll follow the general machine learning workflow step-by-step: Along the way, we'll see how each step flows into the next and how to specifically implement each part in Python. The complete project is available on GitHub, with the first notebook here. After completing the work, I was offered the job, but then the CTO of the company quit and they weren't able to bring on any new employees. I guess that's how things go on the start-up scene!) The first step before we get coding is to understand the problem we are trying to solve and the available data. In this project, we will work with publicly available building energy data from New York City. The objective is to use the energy data to build a model that can predict the Energy Star Score of a building and interpret the results to find the factors which influence the score. We want to develop a model that is both accurate *-- it can predict the Energy Star Score close to the true value -- and *interpretable -- we can understand the model predictions. Once we know the goal, we can use it to guide our decisions as we dig into the data and build models. Contrary to what most data science courses would have you believe, not every dataset is a perfectly curated group of observations with no missing values or anomalies (looking at you mtcars and iris datasets). Real-world data is messy which means we need to clean and wrangle it into an acceptable format before we can even start the analysis. Data cleaning is an un-glamorous, but necessary part of most actual data science problems.

Diligent Robotics today announced the close of a $10 million round to expand its fleet of nurse assistant robots for hospitals. The round was led by DNX Ventures, with participation from True Ventures, Ubiquity Ventures, Next Coast Ventures, Grit Ventures, E14 Fund, and Promus Ventures. Moxi is designed to reduce nurse workloads by handling tasks like collecting supplies, gathering soiled linens, and delivering fresh ones, and it's coming to market during the COVID-19 crisis, when nurses are in short supply. In addition to tackling mundane aspects of the job, the robot can also help reduce health care professionals' exposure to disease. Moxi was created by Diligent Robotics at University of Texas, Austin by CEO Dr. Andrea Thomaz, a roboticist and professor who previously ran the Georgia Tech Socially Intelligent Machines Lab. "It's a really good time to be working on this problem," Thomaz told VentureBeat in a phone interview.

EU trade policy should carve out space for the regulation of ethical and responsible artificial intelligence (AI) in future trade talks. This is the finding of a new study by researchers from the University of Amsterdam's (UvA) Institute for Information Law. The Dutch Ministry of Foreign Affairs commissioned the study to generate further knowledge about the interface between international trade law and European norms and values when it comes to the use of AI. As AI seeps ever more comprehensively into our daily lives--through our phones, our cars, even in our doctors' offices--the need to ensure responsible use of such technologies becomes ever greater. Responsible use of AI is therefore a top priority for the Dutch government and for the EU as a whole.