Google has announced a research group in Switzerland that will be dedicated to the field of machine learning.
As its name suggests, machine learning involves systems that can learn things and come up with predictions from sets of data, without being specifically programmed to do so. It’s essential to what we currently think of as “artificial intelligence.” Continue reading “Google announces new machine learning centre in Europe”
Cloud.diet is a fully mobile compatible online diet program that systematically changes itself based on your own particular lifestyle, always improving its prescribed plan based on what is working for you and other dieters. We like to think of it as a living, breathing diet that is constantly evolving based on real life data and research. Continue reading “Machine learning applied in diet system (Video)”
Martin Vesper, CEO, digitalSTROM AG, will speak about “The invisible butler can only be good if it learns from you. Why machine learning is so important for a great user experience in a smart home” during the Internet of Things Event, which will take place on June 07-07, 2016, at High Tech Campus Eindhoven, The Netherlands.
About Martin Vesper Continue reading ““The invisible butler can only be good if it learns from you” – Presented by Martin Vesper, digitalSTROM”
In years gone by there was Where’s Waldo? Then there was Finding Nemo. Today, on a more serious note but important even to small children, there is FIND FH.
FH, or familial hypercholesterolemia, is a genetic predisposition to very high levels of LDL cholesterol (the bad cholesterol) that may first manifest itself as a fatal heart attack at a premature age. There are therapies, statin-based, that reliably improve outcomes for patients with FH. But there is a big problem: 90 out of 100 patients who have FH do not know they have it or perhaps even what it is. Many physicians are equally in the dark about the disease and therefore do not screen patients for it. FIND FH hopes to change all that. Continue reading “Researchers Use Machine Learning to Identify Genetic Disease Patients”