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.

Satz and Averso, who met while students at DSI, are deeply committed to using "data for good." The pair has worked together for several years at the intersection of data science and health care and formed EVQLV in December 2019 to use AI to accelerate the speed at which healing is discovered, developed, and delivered. The company has already grown to 12 team members with skills ranging from machine learning and molecular biology to software engineering and antibody design, cloud computing, and clinical development.

Roni Rosenfeld makes predictions for a living. Typically, he uses artificial intelligence to forecast the spread of the seasonal flu. But with the coronavirus outbreak claiming lives all over the world, he's switched to predicting the spread of Covid-19. It was the Centers for Disease Control and Prevention (CDC) that asked Rosenfeld to take on this task. As a professor of computer science at Carnegie Mellon University, he leads the machine learning department and the Delphi research group, which aims "to make epidemiological forecasting as universally accepted and useful as weather forecasting is today."

In today's competitive business environment, managers rely heavily on insight from their analytics software. Current performance, feedback from product releases, rate of new customers – these are just a few of countless questions that analytics applications answer for us. But using these analytics programs – to their fullest extent – is still an emerging discipline. As critical as their insights are, actually gleaning those insights requires surmounting myriad challenges. These include everything from lack of training to inability to formulate an effective query.

Almost every industry is seeking top quality Artificial Intelligence (AI) and analytics professionals across the world. Apart from top academic institutions, industry has also been targetting scientific research labs in order to tap those who possess competencies in quantitative techniques proficient in building models and are getting them oriented to design business solutions. The AI as a service market size was valued at $1.13 billion in 2017 and is expected to be $10.88 billion by 2023, thus opening up a huge demand for AI talent pool. The AI-powered services in the form of Application Programming Interface (API) and Software Development Kit (SDK) are primarily driving the demand for AI and analytics professionals. In addition to these, startups working on path breaking ideas are also in need of smart data science professionals.

A self-driving car hurtling along the highway and weaving through traffic has less understanding of what might cause an accident than a child who's just learning to walk. A new experiment shows how difficult it is for even the best artificial intelligence systems to grasp rudimentary physics and cause and effect. It also offers a path for building AI systems that can learn why things happen. The experiment was designed "to push beyond just pattern recognition," says Josh Tenenbaum, a professor at MIT's Center for Brains Minds & Machines, who led the work. "Big tech companies would love to have systems that can do this kind of thing."

As a consequence, a powerful technology with great potential for good is at the moment deployed mainly for privatised gain. In the process, it has been characterised by unregulated premature deployment, algorithmic bias, reinforcing inequality, undermining democratic processes and boosting covert surveillance to toxic levels. That it doesn't have to be like this was vividly demonstrated last week with a report in the leading biological journal Cell of an extraordinary project, which harnessed machine learning in the public (as compared to the private) interest. The researchers used the technology to tackle the problem of bacterial resistance to conventional antibiotics – a problem that is rising dramatically worldwide, with predictions that, without a solution, resistant infections could kill 10 million people a year by 2050.

Is artificial intelligence getting too smart (and intrusive) for its own good? A growing number of nations have concluded that it's time to take a close look at AI's impact on an array of critical issues, including privacy, security, human rights, crime, and finance. A proposal for an international oversight panel, the Global Partnership on AI, already has the support of six members of The Group of Seven (G7), an international organization comprised of nations with the largest and most advanced economies. The G7's dominant member, the United States, remains the only holdout, claiming that regulation could hamper the development of AI technologies and hurt US businesses. The Global Partnership on AI and OECD's G20 AI principles represent a good first step toward building a worldwide AI regulatory structure, noted Robert L. Foehl, an executive-in-residence for business law and ethics at Ohio University.