Artificial intelligence is one of the fastest moving and least predictable industries. Just think of all the things that were unthinkable a few years ago: deepfakes, AI-powered machine translations, bots that can master the most complex games, etc. But it never hurts to increase our chances of predicting the future of AI to try. We asked scientists and AI leaders what they think will happen in the AI space in the coming year. This is what you need to know. AI will make healthcare more accurate and cheaper. Jeroen Tas, Chief Innovation & Strategy Officer at Philips, told Applied Sciences: “The main impact of AI in 2020 will be to transform healthcare workflows for both patients and healthcare professionals, while reducing costs. the ability to obtain real-time data from multiple hospital information flows – electronic medical records, admissions to the emergency service, use of equipment, staffing, etc. – and to interpret and analyze it in a meaningful way will provide a wide range of efficiency and care-enhancing options enable”. This will be done in the form of optimized planning, automated reporting and automatic initialization of the equipment settings, explains Tas, who will be adapted “to the way an individual clinician works and the condition of an individual patient – functions which improve the experience of the patient and the staff, result in better results and contribute to lower costs “. “There is a huge waste in many healthcare systems with regard to complex administration processes, lack of preventive care, and over- and under-diagnosis and treatment. These are areas where AI can really make a difference,” Tas told TNW. “Furthermore, one of the most promising applications of AI in the field of” Command Centers “will be to optimize patient flow and resource allocation. Philips is a major player in the development of necessary AI-supported apps that seamlessly integrated into existing healthcare workflows, one in every two researchers at Philips is currently working with data science and AI, thereby leading the way in applying this technology in healthcare, for example explaining how combining AI with expert clinical and domain knowledge routine and simple yes / no diagnoses will begin to accelerate – not to replace clinicians, but to give them more time to focus on the difficult, often complex, decisions about individual patient care : “AI systems will facilitate the allocation of eyesight and availability of medical personnel, Track, predict and support ICU beds, operating rooms and diagnostic and therapeutic equipment. Explanation and trust get more attention “2020 will be the year of AI reliability,” said Karthik Ramakrishnan, head of advisory and AI authorization at Element AI, against Applied Sciences. “The first principles for ethics of AI and risk management came into existence in 2019, and early attempts were made to operationalize these principles in toolkits and other research approaches. Also the concept of accountability (being able to explain the forces behind AI-based decisions) The awareness of AI ethics has certainly increased in 2019. At the beginning of this year, the European Commission published a series of seven guidelines for the development of ethical AI, and in October Element AI, co-founded by Yoshua Bengio, a of in-depth learning pioneers, partnered with the Mozilla Foundation to create data trusts and insist on the ethical use of AI, and major technical companies such as Microsoft and Google have also taken steps to align their AI development with ethical norms The growing interest in ethical AI comes after a few visible failures related to vertro yours and AI in the market, Ramakrishnan reminded us, such as the rollout of Apple Payout, or the recent interest rate hike related to the Cambridge Analytica scandal. “In 2020, companies will pay more attention to trust in AI, whether they are ready for it or not. Expect to see VCs attention, too, with new start-ups emerging to help with solutions,” Ramakrishnan said. Pay attention, “Ramakrishnan. AI will become less data hungry.” We will see an increase in data synthesis methodologies to combat data challenges in AI, “Rana el Kaliouby, CEO and co-founder of Affectiva, told Applied Sciences Deep. Learning techniques are data hungry, meaning that AI algorithms based on in-depth learning can only work accurately if they are trained and validated on huge amounts of data, but companies that develop AI often find it a challenge to access the right types data and the required amounts of data. “Many researchers in the AI space begin testing and use emerging data synthesis methodologies to overcome the limitations of the data available to them in practice. With these methodologies, companies can take data that has already been collected and merge it to create new data, “said el Kaliouby. Source: https://thenextweb.com/artificial-intelligence/2019/12/17/8-biggest- ai-trends-of-2020-according-to-experts /