Almost everything is somehow interrelated with everything else—and that should not surprise us.

We help companies recognise how changes in the external world may impact them and their sector. INSIGHT IMPACT Global Foresight Industry Insight Bespoke Research New Services New Products New Strategies Core Team Extended Team The Global Network 3. The World in 2020 The first global Future Agenda programme in 2010 was hosted by Vodafone. Fifty workshops across 16 topics in 25 locations with 1500 experts identified a wide range of 10-year shifts, over 80% of which have now come to pass. Lease Everything Global Pandemics Active Elderly People TrackingDrone Wars 4. The World in 2025 A second larger programme in 2015 involved 50 organisations as hosts. Issues identified have become a central focus for major innovation globally.

High breast density or a greater amount of glandular and connective tissue compared to fat are considered as one of the major risk factors for cancer. It is believed, while density may be incorporated into risk assessment, the current prediction models are likely to fail to take complete advantage of the expansive information present in mammograms. Experts allege this information can help to identify women who would benefit from the additional screening with MRI. Karin Dembrower, M.D., breast radiologist and Ph.D. candidate from the Karolinska Institute in Stockholm, Sweden has developed a risk model which is dependent on a wide neural network, a type of AI that can extract vast amounts of information from mammographic images. These have several advantages in comparison to other methods like visual assessment of mammographic density by the radiologists, which are not capable of capturing all risk-relevant information in the image. Sources state, the new model was developed and trained on mammograms from cases diagnosed between 2008 and 2012 and then studied on more than 2,000 women ages 40 to 74 who had undergone mammography in the Karolinska University Hospital system.

With late 2019 marking the 20th anniversary of the landmark report on medical errors "To Err is Human," now is time for a renewed focus on novel ways to improve patient safety. The report launched the modern patient safety movement by shedding some much-needed light on the prevalence of medical errors and preventable deaths in the U.S., spawning many improvements to patient safety over the subsequent two decades. But before the healthcare industry gets too self-congratulatory, we could use a quick reality check. Patient safety remains a persistent global issue that exacts a huge human cost, as well as a financial one, as a recent report from the World Health Organization (WHO) starkly illustrates. While it is estimated that there is a one in 3 million risk of dying while travelling by airplane, the risk of patient death while receiving healthcare due to a preventable medical accident is estimated to be one in 300, according to the WHO.

Innovate UK has announced that it will fund a two-year research programme that uses machine learning to better forecast risk. It will see RSA and Plymouth University partner with Exeter-based firm – Software Solved. The software firm will also work together with other insurance brands which have yet to be announced. And it will feed into ongoing'risk' work in the insurance industry and Innovate UK will be reporting back to the government on its progress. Martin Turner, risk consulting director at RSA Insurance, explained: "Having the ability to use machine learning to automate the assimilation of significant volumes of valuable data at our disposal enables us to forecast risk with a far greater degree of confidence. He claims it will allow RSA to more accurately deploy its risk management tools and resources to help reduce the probability of claims. Software Solved is already working with six of the top 10 insurers including RSA, AXA XL, XL Catlin and Aviva. This project will see the firm partner with the university to develop ways to use machine learning to automate data, predict future risks using open data and reduce ongoing claims. Plymouth University will be providing the research expertise for the project. Ian Howard, associate professor at the University of Plymouth, explained that the institution will also be providing pattern recognition, data modelling, statistics and predictive analytics. Jon Stace, principal technical consultant at Software Solved, added: "Focusing on how we understand, measure and predict future risks, by applying machine learning, has the potential to improve how we assess, carry out and automate risk assessment across the insurance sector.

A new study released by The Economist Intelligence Unit ran three econometric scenarios to 2030 on five countries -- the United States, the United Kingdom, Australia, Japan--and developing Asia as a whole. In'Risks and rewards: Scenarios around the economic impact of machine learning', commissioned by Google, two scenarios assumed greater human productivity through upskilling and greater investment in technology and access to open source data, while the third assumed insufficient policy support for structural changes in the economy. The results showed that, although the fears of those pessimistic about the impact of machine learning, and artificial intelligence in general, may be overblown, the optimists' claims are not entirely supported, either. The other area of the study, a look at the impact of machine learning on four industries, reaches a similar conclusion. For firms both developing machine learning and those using it, the reports finds that communication between themselves, and with the public and policymakers, needs to improve.

When the safety and security of an entire nation is at stake, the Central Intelligence Agency (CIA) of the US needs to be "ahead of the curve", said Teresa Smetzer, Director of Digital Futures. It needs to go beyond just reporting on events to actually anticipating the next crisis. The agency's anticipatory intelligence cell uses machine learning and data science to draw insights from events that had happened in the past, and "report to our policymakers any issues of instability that they might have to deal with". "Rather than responding, they are proactively able to understand what they can do to change the situation," Smetzer said at the recent AWS re:Invent conference in Las Vegas, Nevada. Data is the "lifeblood" of many organisations, whether public and private, said Smetzer.

In 2013, a man named Eric L. Loomis was sentenced for eluding police and driving a car without the owner's consent. When the judge weighed Loomis' sentence, he considered an array of evidence, including the results of an automated risk assessment tool called COMPAS. Loomis' COMPAS score indicated he was at a "high risk" of committing new crimes. Considering this prediction, the judge sentenced him to seven years. Loomis challenged his sentence, arguing it was unfair to use the data-driven score against him.