So, a few months ago, I was walking down the street in Shenzhen, in the Guangdong Province of southeastern China. I was hungry and looking for lunch. Armed with my credit card and plenty of the local currency, I strode out of my hotel to check out the many street vendors selling delicious-smelling food. Using Google Translate, I was able to order a fried fish dish, but when I went to pay, the vendor refused my credit card. Undaunted I pulled out cash, but that too was refused. The guy pointed me to a large QR code and asked me to pay using the WeChat app. As this was my first day in China, I hadn't yet set the app to pay for things, so I walked away, a little embarrassed. Still hungry, I came to a large junction and saw a promising looking restaurant across a busy street.

An algorithm combining high sensitive troponin testing with personal details can help A&E doctors better determine whether patients are having a heart attack, according to new research. The study, published in medical journal Circulation today, used Abbott's algorithm on 11,000 patients from the UK, the US, Germany, Switzerland, Australia and New Zealand, to see whether it could help deliver faster and more accurate evidence as to whether patients were suffering from a heart attack. Developed using machine learning – a branch of artificial intelligence – the algorithm uses a high sensitivity troponin-I blood test, and the time it was taken, to assess the patient's blood troponin protein levels, combining the results with personal details, such as age and sex, to deliver a bespoke assessment. It is thought this will help get around two current obstacles in heart attack diagnoses. The first is that women are currently at greater risk of misdiagnosis, because their troponin protein levels can be lower than those of men, and international guidelines for the use high sensitive troponin tests do not always account for sex in results.

SimplyGo signals change for ticketing in Singapore There is no doubting the significance of smart ticketing technology for cities worldwide as transport providers strive to create a seamless passenger experience. Intelligent Transport spoke to Yeo Teck Guan, Senior Group Director (Public Transport) at Singapore's Land Transport Authority (LTA), to discuss the country's account-based ticketing system, SimplyGo. Fare's fair: using AI to combat fare evasion At stations all over the world, fare evaders are often difficult to catch and just as difficult to stop. In Catalonia, however, the implementation of AI-based technology has led to the near-eradication of tailgating at fare gates – Oriol Juncadella i Fortuny, Director of FGC Operadora, the operations division of Ferrocarrils de la Generalitat de Catalunya (FGC), tells us more. How Queensland is earning its place among the smart ticketing elite Intelligent Transport sat down with Queensland Transport and Main Roads Minister, Mark Bailey, to learn how the Australian state will touch the future of travel through one of the most advanced ticketing systems in the world, joining the likes of Singapore, New York, London and Chicago on the global stage.

While we know many technology and solutions providers are building machine learning and artificial intelligence (AI) into their solutions, apps and platforms, the concept of what employees think about AI is less well understood. In the contact center, many solutions – most notably quality monitoring and workforce management – employ AI to analyze data, make predictions, and recommend next-best actions. In workforce management, the goal is to find hidden patterns in the historical data used to generate forecasts for volume and work time. These are all very employee-centric solutions, yet new research from Genesys (News - Alert) has found that few employees understand how AI affects them. The Genesys research was drawn from a study of broad attitudes of 1,103 employers and 4,207 employees in the United States, Germany, the United Kingdom, Japan, Australia and New Zealand regarding the current and future effects of AI on their workplaces.

THIS WEEK: 6 Lessons Learned 6 Years Into Co-Founding a VC Fund; Drone bubble bursts, wiping out startups and hammering VC firms; State Of AI And Machine Learning In 2019; New Zealand Fund of Funds VC Initiative; First Institutional Round is Redefining VC; How to know when to say'I do' to a term sheet; Should Founders Take the Plunge?; One health care startup at a time, this VC wants to harness the investing power of women; Intel Capital Diversity Initiative; Signia Venture Partners unveils $85 M early-stage fund; Foundation Capital just closed its 9th fund with $350 M; Sony announces a new $185M fund. If you have Venture Capital Firm news (New Fund Announcements, New Partners) to submit for consideration in this digest, submit a link at the end of the newsletter or tweet us at @VCStartupDigest. If you're a fan of our newsletter, please forward this sign-up link to your fellow Venture Capital enthusiasts: http://venture-capital.startupdigest.com/

Caption: Paramedics respond to an emergency. Scientists have developed an artificial intelligence tool that lets doctors determine whether someone is having a heart attack much faster than current methods. New research published by healthcare firm Abbott shows that its algorithm could enable hospital accident and emergency departments to more accurately identify and treat patients having a cardiac arrest. The study, which involved researchers from the U.S., Germany, U.K., Switzerland, Australia and New Zealand and more than 11,000 patients, found that AI could provide doctors a more comprehensive analysis of the probability that a patient was having a heart attack. Agim Beshiri, a senior medical director at Abbott, said: "AI technology has the capability to consider many variables, characteristics and data points and combine them in seconds into meaningful results. "Because of today's advancements in computational power and AI applications, healthcare stands to benefit greatly by this approach where clinicians have to do this with their patients every day." Developed by a team of physicians and statisticians at Abbott, the algorithm uses machine learning techniques to enable a more individualized calculation of a person's heart attack risk. The technology aims to improve and quicken heart attack diagnosis by analyzing extensive datasets and identifying factors such as age, sex and a person's specific troponin levels (a cardiac biomarker). Abbott said the algorithm is designed to help address two barriers that exist today for doctors looking for more individualized information when diagnosing heart attacks. The first is that international guidelines for using highly sensitive troponin tests don't always account for personal factors, impacting test results. And the second is that while these guidelines recommend that doctors carry out troponin testing at fixed times, they don't consider a person's age or sex and put patients into a one-size-fits-all situation. However, Abbott's algorithm differs from existing approaches as it takes into consideration personal factors and troponin blood test results over time. Beshiri added: "The World Heart Organization estimates that 17.9 million people die from cardiovascular disease each year, and 85% are due to heart attacks and strokes.

Caption: Paramedics respond to an emergency. Scientists have developed an artificial intelligence tool that lets doctors determine whether someone is having a heart attack much faster than current methods. New research published by healthcare firm Abbott shows that its algorithm could enable hospital accident and emergency departments to more accurately identify and treat patients having a cardiac arrest. The study, which involved researchers from the U.S., Germany, U.K., Switzerland, Australia and New Zealand and more than 11,000 patients, found that AI could provide doctors a more comprehensive analysis of the probability that a patient was having a heart attack. Agim Beshiri, a senior medical director at Abbott, said: "AI technology has the capability to consider many variables, characteristics and data points and combine them in seconds into meaningful results.

Some people say that publishing has already been disrupted, that this current state is the new model. But I don't think the disruption has even started yet. As Jeff Bezos says, "it's always Day One." In the last ten years, we've seen the rise of digital publishing, print on demand, and the independent author movement, as well as the growth of streaming audio and the use of internet marketing tools like Facebook and Amazon Ads to sell more books. In this episode, I'll talk about some of the possible disruptions to come for authors and the publishing industry due to the rise of Artificial Intelligence (AI) in the next 10 years. This episode is sponsored by my Patrons, authors who are passionate about the future of publishing and help support my time in producing episodes like this. Ten years ago, when I started self-publishing, I was over-the-top excited about the potential of ebooks (see my embarrassing video here!). I could see the incredible possibilities as a creator to reach the whole world with my words. Since starting out in 2008, I have built a multi-six-figure business as an author-entrepreneur, taking action on that feeling of optimism and learning everything I needed to know to write, publish, market, and make a living with my writing.

McDonald's Corp. is making a bet it can automate the task of taking drive-thru orders. The world's biggest restaurant company is buying startup Apprente Inc., a developer of voice-recognition technology for use in the restaurant industry, to help speed up lines. The idea is to eventually have a machine, instead of a person, on the other side of the intercom to relay orders to kitchen staff. In Chicago-area restaurants where the system is already being tested, employees still oversee drive-thru order-taking and can step in when needed. The acquisition is McDonald's third tech deal in the past six months, and fits into the company's push to lean more heavily on machines and artificial intelligence to boost sales.

Scientists have developed an artificial intelligence tool that lets doctors determine whether someone is having a heart attack much quicker than current methods. New research published by healthcare firm Abbott shows that its algorithm could enable hospital accident and emergency departments to more accurately identify and treat patients having a cardiac arrest. The study, which involved researchers from the U.S., Germany, U.K., Switzerland, Australia and New Zealand and more than 11,000 patients, found that AI could provide doctors a more comprehensive analysis of the probability that a patient was having a heart attack. Agim Beshiri, a senior medical director at Abbott, said: "AI technology has the capability to consider many variables, characteristics and data points and combine them in seconds into meaningful results. "Because of today's advancements in computational power and AI applications, healthcare stands to benefit greatly by this approach where clinicians have to do this with their patients every day." Developed by a team of physicians and statisticians at Abbott, the algorithm uses machine learning techniques to enable a more individualized calculation of a person's heart attack risk. The technology aims to improve and quicken heart attack diagnosis by analyzing extensive datasets and identifying factors such as age, sex and a person's specific troponin levels (a cardiac biomarker). Abbott said the algorithm is designed to help address two barriers that exist today for doctors looking for more individualized information when diagnosing heart attacks. The first is that international guidelines for using high sensitive troponin tests don't always account for personal factors, impacting test results. And the second is that while these guidelines recommend that doctors carry out troponin testing at fixed times, they don't consider a person's age or sex and put patients into a one-size-fits-all situation. However, Abbott's algorithm differs from existing approaches as it takes into consideration personal factors and troponin blood test results over time Beshiri added: "The World Heart Organization estimates that 17.9 million people die from cardiovascular disease each year, and 85% are due to heart attacks and strokes.