With the rapid development of computing technology, wearable devices such as smart phones and wristbands make it easy to get access to people's health information including activities, sleep, sports, etc. Smart healthcare achieves great success by training machine learning models on a large quantity of user data. However, there are two critical challenges. Firstly, user data often exists in the form of isolated islands, making it difficult to perform aggregation without compromising privacy security. Secondly, the models trained on the cloud fail on personalization. In this paper, we propose FedHealth, the first federated transfer learning framework for wearable healthcare to tackle these challenges. FedHealth performs data aggregation through federated learning, and then builds personalized models by transfer learning. It is able to achieve accurate and personalized healthcare without compromising privacy and security. Experiments demonstrate that FedHealth produces higher accuracy (5.3% improvement) for wearable activity recognition when compared to traditional methods. FedHealth is general and extensible and has the potential to be used in many healthcare applications.

Home care is often singled out for being slow to embrace and implement technology, but as the demand for care services grows, providers are forced to think outside of the box when it comes curbing caregiver turnover. San Francisco-based home care startup Honor understands this all too well, according to CEO Seth Sternberg. The company is using insights gleaned from machine learning to examine and address turnover internally and with its network of home care partners. Honor, which has raised $115 million since launching in 2014, teams up with independently owned and operated agencies by taking over caregiver recruiting, onboarding and training, in addition to day-to-day logistics. Currently, the company operates in Arizona, California, New Mexico and Texas.

As AI moves increasingly into actual commercial use, the leading nations are positioning themselves to standardize the field to their own advantage. This includes everything from minute technical standards to procedures for removing bias from algorithms. Countries and companies have a lot to gain from leaving a mark on the process. Beijing got out of the starting gate first: Last year, China published a detailed report focused on ethical norms and technical standards that are meant to allow companies to work together more easily. A few months later, Beijing hosted the first major international meeting on AI standards.

Sorry, science fiction fans, but the "replicants" of the Blade Runner saga or the "terminators" of the eponymous action movie franchise are not on the horizon. "Don't imagine human-like, humanoid robots when you think of the future of robots," said Kim Sang-bae, the world-renowned robot scientist who developed a four-legged walking robot called "Cheetah," which has gained widespread media exposure. Not only is it impossible to develop human-like robots now, it may remain impossible in the future, according to Kim, a mechanical engineering professor at the Massachusetts Institute of Technology. While the ultimate stage of robotics may, indeed, be creating machines that can think and work on their own, there is a yawning gap between where robot technologies stand now and that final-stage development. In an interview with Asia Times, Kim predicted that the robot industry would continue to expand by creating robots which can do very specific things better than humans. But he conceded there is a real risk that the social inequalities in the sector will accelerate.

Many may not know this but Dr. Fatmah Baothman is the first woman in Saudi Arabia and the Middle East to hold a PhD in Modern Artificial Intelligence (AI), a milestone for the entire region and definitely a proud achievement for the Kingdom. This week, the Middle East's first female specialist in AI has been awarded the first-ever Women AI Award, which was announced at the VB AI Summit Transform 2019 in San Francisco, United States, according to Saudi Gazette. According to the award's website, this first-of-its-kind award aims to honor changemakers in the field, women leaders paving the way in rethinking process, policy, technology, and education as AI advances. Dr. Baothman was awarded under the category AI Research, which honors a woman whose research in AI has made a significant impact by helping accelerate progress within her organization, as part of academic research, or by influencing approaches to AI technology. As reported by the news site, Dr. Baothman expressed her gratitude in receiving such a global honor and for the recognition women in AI are receiving for their accomplishments.

In almost every field (and especially in any kind of data science), there will be times when we wish to estimate data we don't have by using the data that we do have. Given the plethora of machine learning algorithms and the similarly-daunting number of implementations of these algorithms (in different languages, packages, etc), it can be difficult to know where to start. This post shares my biggest takeaways from diving into using some of these tools, in the context of estimating air pollution from wildfires over the western US. Remotely sensed image of the 2015 Washington Wildfires, showing smoke mixed with cloud cover. Since September 2017 I've worked on the Environmental Health Team in Earth Lab, investigating the impacts of exposure to air pollution from wildfires on human respiratory and cardiovascular health in the western US.

Where once banks and credit unions routinely left technology to specialists, the subject now has become elevated to the highest-ranking issue impacting retail banking. Research by The Economist Intelligence Unit (EIU) for Temenos finds that coping with new technology is the top concern of retail bankers, ahead of changing consumer behavior, political and economic instability and dealing with bad loans, among other factors. No institution can afford to ignore the combination of new competition from fintechs and big technology companies, multiple new technologies, and soaring consumer expectations is bringing unprecedented change to retail banking that And few are ignoring it, as the EIU survey indicates. However, the how quickly and how extensively organizations respond varies sharply by institution and sometimes even by country. In a study of 161 publicly traded banking institutions around the world, Accenture found that just over half are "digital laggards," with no plans to go digital or just "half-hearted efforts."

In the past few years, machine learning (ML) has become commercially successful and AI firmly established as a field. With its success, more attention is being paid specifically to the gender gap in AI. Compared to the general population, men are overrepresented in technology. While this has been the case for several decades, the opposite was true in the early days of computing when programming was considered a woman's job. Diversity has been shown to lead to good business outcomes like improved revenue.

In its rush to solve all the world's problems through deep learning, Silicon Valley is increasingly embracing the idea of AI as a universal solver that can be rapidly adapted to any problem in any domain simply by taking a stock algorithm and feeding it relevant training data. The problem with this assumption is that today's deep learning systems are little more than correlative pattern extractors that search large datasets for basic patterns and encode them into software. While impressive compared to the standards of previous eras, these systems are still extraordinarily limited, capable only of identifying simplistic correlations rather than actually semantically understanding their problem domain. In turn, the hand-coded era's focus on domain expertise, ethnographic codification and deeply understanding a problem domain has given way to parachute programming in which deep learning specialists take an off-the-shelf algorithm, shove in a pile of training data, dump out the resulting model and move on to the next problem. Truly advancing the state of deep learning and way in which companies make use of it will require a return to the previous era's focus on understanding problems rather than merely churning canned models off assembly lines.

Small and medium-sized businesses are the keystone of the modern-day labor market. In the United States alone, small businesses employ almost 50% of the private workforce, and recent data shows that companies with fewer than 20 employees have added 1.2 million net new jobs. But although their growth is vital to a sustainable global economy, SMBs continue to struggle to get the funding they need. The traditional lending system simply isn't set up to meet the smaller capital needs of these types of enterprises: taking into account the risks and the long review process, small business loans typically don't pay off for banks. Chances of being accepted are incredibly low for businesses that aren't already well-established, and they rarely have the structure to carry them through the long review process anyway.