Coronavirus has killed hundreds of thousands of people and has strained health systems around the world, but for Tony Young there may be a patch of a silver lining. The pandemic is accelerating use of technology to radically advance medicine and save lives in the future. "There are so many fantastic examples of the way in which technology is empowering our patients and our professionals," says Prof Young, a surgeon and national clinical lead for NHS England. Having launched his own medical-technology start-ups, he is helping to introduce innovations across the UK health service. Digital tools, whether for data management and drug development or enhanced diagnosis and treatment, have sharply improved the response to the threat of infection and all sorts of disease.

Technology can now help you find the nearest restaurant that has your favorite food and have it delivered to you by speaking to your smart speaker or phone. Did you know that the same technology is being used to help save species from extinction? Our wild elephant conservation team wants to share how AI technology is helping elephant conservation efforts. Artificial intelligence (AI) is the ability of a machine to simulate human intelligence. These machines are programmed to think and act like humans.

It wasn't too long ago that concepts such as communicating with your friends in real time through text or accessing your bank account information all from a mobile device seemed outside the realm of possibility. Today, thanks in large part to the cloud, these actions are so commonplace, we hardly even think about these incredible processes. Now, as we enter the golden age of machine learning, we can expect a similar boom of benefits that previously seemed impossible. Machine learning is already helping companies make better and faster decisions. In healthcare, the use of predictive models created with machine learning is accelerating research and discovery of new drugs and treatment regiments.

With humans increasingly serving as computational elements in distributed information processing systems and in consideration of the profit-driven motives and potential inequities that might accompany the emerging thinking economy[1], we recognize the need for establishing a set of related ethics to ensure the fair treatment and wellbeing of online cognitive laborers and the conscientious use of the capabilities to which they contribute. Toward this end, we first describe human-in-the-loop computing in context of the new concerns it raises that are not addressed by traditional ethical research standards. We then describe shortcomings in the traditional approach to ethical review and introduce a dynamic approach for sustaining an ethical framework that can continue to evolve within the rapidly shifting context of disruptive new technologies.

Images of elephants roaming the African plains are imprinted on all of our minds and something easily recognized as a symbol of Africa. But the future of elephants today is uncertain. An elephant is currently being killed by poachers every 15 minutes, and humans, who love watching them so much, have declared war on their species. Most people are not poachers, ivory collectors or intentionally harming wildlife, but silence or indifference to the battle at hand is as deadly. You can choose to read this article, feel bad for a moment and then move on to your next email and start your day.

MarketsandResearch.biz has added an exhaustive research study of Global Machine Learning as a Service (MLaaS) Market 2020 by Company, Regions, Type and Application, Forecast to 2025 that represents a basic overview of the market in which historical information related to the market such as market size, status, competitor segment, key vendors, top regions, product types, and end industries has been provided. The report highlights new business opportunities and existing marketing strategies through insights regarding SWOT analysis, market valuation, competitive spectrum, regional share, and revenue predictions. The market is suitably segmented and sub-segmented so that it can shed light on every aspect of the global Machine Learning as a Service (MLaaS) market such as the type of product, application, and region. It also reveals the competition landscape of the companies and the flow of the global supply and consumption. The report offers a granular analysis of insights into various developments, historical data, current scenario, and future predictions.

BEGIN ARTICLE PREVIEW: Adam Benzion Contributor Adam Benzion is a serial entrepreneur, writer, tech investor, co-founder of Hackster.io and the CXO of Edge Impulse. Images of elephants roaming the African plains are imprinted on all of our minds and something easily recognized as a symbol of Africa. But the future of elephants today is uncertain. An elephant is currently being killed by poachers every 15 minutes, and humans, who love watching them so much, have declared war on their species. Most people are not poachers, ivory collectors or intentionally harming wildlife, but silence or indifference to the battle at hand is as deadly. You can choose to read this article, feel bad for a moment and then move on to your next email and start your day. Or, perhaps you will pause and think: Our opportunities to help save wildlife, especially elephants, are right in front of us and grow every day. And some of these opportunities are

IBM this week presented research investigating how AI and machine learning could be used to improve maternal health in developing countries and predict the onset and progression of Type 1 diabetes. In a study funded by the Bill and Melinda Gates Foundation, IBM researchers built models to analyze demographic datasets from African countries, finding "data-supported" links between the number of years between pregnancies and the size of a woman's social network with birth outcomes. In a separate work, a team from IBM analyzed data across three decades and four countries to attempt to anticipate the onset of Type 1 diabetes anywhere from 3 to 12 months before it's typically diagnosed and then predict its progression. They claim one of the models accurately predicted progression 84% of the time. Despite a global decline in child mortality rates, many countries aren't on track to achieving proposed targets of ending preventable deaths among newborns and children under the age of 5. Unsurprisingly, the progress toward these targets remains uneven, reflected in disparities in access to healthcare services and inequitable resource allocation. Toward potential solutions, researchers at IBM attempted to identify features associated with neonatal mortality "as captured in nationally representative cross-sectional data."

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