Switzerland-based solution provider Kistler's measurement technology experts will welcome visitors to K 2019 where they can learn how artificial intelligence will smooth the path to the smart factories of the future. The new, ultra-compact 9239A sensor from Kistler allows contact-free measurement of cavity pressure during the injection moulding process – developed to meet the highest surface quality standards. ComoNeoPREDICT from Kistler adds artificial intelligence to the injection moulding process. The quality model generated by intelligent software is based on neural networks. This is the starting point for extremely accurate and efficient injection moulding processes that must meet the very high standards required in the automotive and medtech sectors.
Voting and electing are, from a machine learning perspective, classification problems: The „algorithm" in our brain has to choose one from several available options as the correct one. The options are candidates in the case of elections or „YES/NO" in the case of voting (like in the direct democracy in Switzerland). In a democracy, there is not a single „classifier" (King) making the decisions but a large number of people. What is the benefit of this method? Why is democracy historically so successful? We might say that democracies are more stable because everybody is allowed to participate in the decision process and the result will represent the wishes of everybody. But there are many cases in history where the majority in a democratic country suppressed a minority. I personally don't believe in this theory. I rather think that in a democracy, the decisions tend to be much smarter than what can be achieved by a single king or a small group of leaders. It is the collective intelligence formed by the whole voting population which makes democracy superior to other systems of government. The method is also well known in machine learning under the term „ensemble methods".
Click here to read the full article. The following essay was produced as part of the 2019 Locarno Critics Academy, a workshop for aspiring film critics that took place during the 72nd edition of the Locarno Film Festival. Artificial intelligence is everywhere: It can drive a car, chat with customers, or help patients with neuronal damage to recover their potential. But if data-assisted moviemaking can help predict a movie's outcome, what room is there left for artistic freedom? At this year's Locarno Film Festival, Sami Arpa, CEO and co-founder of Largo Films, a startup based in Lausanne, Switzerland, and creator of the LargoAI technology, shared his insight about the evolution of this maybe-not-so-unnatural union.
The following essay was produced as part of the 2019 Locarno Critics Academy, a workshop for aspiring film critics that took place during the 72nd edition of the Locarno Film Festival. Artificial intelligence is everywhere: It can drive a car, chat with customers, or help patients with neuronal damage to recover their potential. But if data-assisted moviemaking can help predict a movie's outcome, what room is there left for artistic freedom? At this year's Locarno Film Festival, Sami Arpa, CEO and co-founder of Largo Films, a startup based in Lausanne, Switzerland, and creator of the LargoAI technology, shared his insight about the evolution of this maybe-not-so-unnatural union. At Locarno last year to present sofy.tv, a VOD service for short films, Arpa recalled, "I was approached by industry professionals, mostly producers and distributors, who asked me if the AI developed for sofy could be used for their own purposes, to help them predict a movie's outcome. A few directors also approached me, although they were much more skeptical at first."
IBM Watson Health announced yesterday at the Intelligent Health 2019 summit on AI in Basel that it has signed a partnership with the university hospital in Geneva (Hôpitaux universitaires de Genève, HUG) to implement and use IBM's Watson for Genomics, making it the first university hospital in Switzerland and Europe to use the tool. IBM Watson for Genomics will empower oncologists in Geneva to come up with better-informed diagnoses faster. Deploying information extracted from peer-reviewed articles and validated by experts, the solution generates a report for clinicians that matches genetic alterations in a patient's tumour with the most relevant therapies and clinical trials. In 1950, it took about 50 years for it to double, and there are estimates reckoning that by 2020 this will have come down to three months. This makes it impossible for clinicians to stay on top of current research.
Amid the excitement surrounding the potential of artificial intelligence, financial institutions have a responsibility to ensure that AI is used in a way that's fair, transparent and accountable, according to TD Group President and Chief Executive Officer, Bharat Masrani. "TD is in the trust business, and we have worked hard to develop that trust over the past 163 years," he said. Masrani's comments came as part of a panel discussion among global financial sector leaders in Davos, Switzerland during the World Economic Forum on Thursday. AI is transforming the financial sector, giving financial institutions a tremendous opportunity to know their customer better and deliver experiences they would have never imagined. The panel discussion--which was hosted by Deloitte and also featured Sabine Keller-Busse, Chief Operating Officer of UBS, and Ann Cairns, Vice Chairman of Mastercard--focused on how financial institutions can unlock the opportunity of AI while upholding their responsibilities to customers, employees and society.
ABBOTT PARK, Ill., Sept. 12, 2019 -- Abbott announced that new research, published in the journal Circulation, found its algorithm could help doctors in hospital emergency rooms more accurately determine if someone is having a heart attack or not, so that they can receive faster treatments or be safely discharged.1 In this study, researchers from the U.S., Germany, U.K., Switzerland, Australia and New Zealand looked at more than 11,000 patients to determine if Abbott's technology developed using artificial intelligence (AI) could provide a faster, more accurate determination that someone is having a heart attack or not. The study found that the algorithm provided doctors a more comprehensive analysis of the probability that a patient was having a heart attack or not, particularly for those who entered the hospital within the first three hours of when their symptoms started. "With machine learning technology, you can go from a one-size-fits-all approach for diagnosing heart attacks to an individualized and more precise risk assessment that looks at how all the variables interact at that moment in time," said Fred Apple, Ph.D., Hennepin HealthCare/ Hennepin County Medical Center, professor of Laboratory Medicine and Pathology at the University of Minnesota, and one of the study authors. "This could give doctors in the ER more personalized, timely and accurate information to determine if their patient is having a heart attack or not." A team of physicians and statisticians at Abbott developed the algorithm* using AI tools to analyze extensive data sets and identify the variables most predictive for determining a cardiac event, such as age, sex and a person's specific troponin levels (using a high sensitivity troponin-I blood test**) and blood sample timing.
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.
Last week, I spoke at the Swiss Mobile Association. The event was held at one of the oldest cross-functional research institutes Gottlieb Duttweiler Institute just outside Zurich. Prior to being involved in IoT and AI, I worked for many years in Telecoms. So, this was a nice time to catch up with a few ideas for AI for Telecoms I believe that from an innovation standpoint – we are living in a post-mobile world. Today, just as the Web itself, Mobile is a mature industry.
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.