Netflix has an excellent international library, including German sci-fi gem Dark -- one of the best series on Netflix full stop. This adult animated anthology series spans a range of genres, with plenty of episodes hitting the Black Mirror comparison button. Robots in a post-apocalyptic city, farmers piloting mech suits and a space mission gone wrong all pop up in the first season. While the episodes can be hit and miss (some have been criticized for their treatment of women), you'll find plenty of thought-provoking and impressive animation. This apocalyptic sci-fi from Belgium will probably turn you off from flying any time soon.
Summary: A new deep learning algorithm is able to quantify arousal and awareness in humans at the same time. New research supported by the EU-funded HBP SGA3 and DoCMA projects is giving scientists new insight into human consciousness. Led by Korea University and projects' partner University of Liège (Belgium), the research team has developed an explainable consciousness indicator (ECI) to explore different components of consciousness. Their findings were published in the journal Nature Communications. Consciousness can be described as having two components: arousal (i.e.
In the past years, Europe has been consolidating its AI ecosystems. Various countries, including Belgium, have developed their own strategy for AI governance, to finance their research organizations, support their startups and create future generations of AI developers. What is being done in other countries? What has been their approach in terms of national AI plans, scientific funding, and industry development plans? What are the different cultural perspectives on this technology?
General AI (Artificial Intelligence) is coming closer thanks to combining neural networks, narrow AI and symbolic AI. Yves Mulkers, Data strategist and founder of 7wData talked to Wouter Denayer, Chief Technology Officer at IBM Belgium, to share his enlightening insights on where we are and where we are going with Artificial Intelligence. Join us in our chat with Wouter. Yves Mulkers Hi and welcome, today we're together with Wouter Denayer, Chief Technology Officer at IBM. Wouter, you're kind of authority in Belgium and I think outside the borders of Belgium as well on artificial intelligence. Can you tell me a bit more about what you're doing at IBM and What keeps you busy? Wouter Denayer Yeah, Yves, thank you, and thanks for having me. Of course, if you call me an authority already, I think if you call yourself an authority, then something is wrong. It's almost impossible to follow everything that's going on in AI, the progress is actually amazing. I do love to follow everything that's going on as much as possible, especially focussing on what IBM Research is doing, we can come back to that later. In my role as CTO for IBM Belgium, I communicate a lot with C-level people in our strategic clients. Sometimes global clients that really want to know what's coming, what is this AI thing. People understand more or less.
One of those masterclasses, covers the topic of "intelligent automation" or as we refer to as "cognitive RPA". Last Tuesday, the first masterclass took place at our own offices in Leuven with over 30 enthusiastic participants. During these masterclasses, we demonstrate the possibilities of combining artificial intelligence (AI) and robotic process automation (RPA). Almost any potential business process for robotic process automation requires some form of human intelligence. Combining the expertise of our venture Brainjar with the people from Roborana, we we're able to deliver an in-depth workshop filled with use cases.
They are some of the most popular cartoon characters ever created and known the world over - but just what would the likes of Ned Flanders, Rapunzel and Moana look like in real life? Well, the images below offer a glimpse after being created by a digital artist who used artificial intelligence (AI) to help imagine what a host of characters from Disney films to The Simpsons might be like if they were'human'. Hidreley Leli Diao, from Brazil, who grew up watching The Simpsons, Hanna-Barbera shows, and Disney animations, experimented with a piece of software that creates photo-realistic portraits of people who do not actually exist. A digital artist from Brazil has used artificial intelligence software to create photo-realistic portraits showing what popular cartoon characters might look like in real life. FaceApp is a photo-morphing app that uses what it calls artificial intelligence and neural face transformations to make alterations to faces. The app can use photos from your library or you can snap a photo within the app.
Recent success in the field of single image super-resolution (SISR) is achieved by optimizing deep convolutional neural networks (CNNs) in the image space with the L1 or L2 loss. However, when trained with these loss functions, models usually fail to recover sharp edges present in the high-resolution (HR) images for the reason that the model tends to give a statistical average of potential HR solutions. During our research, we observe that gradient maps of images generated by the models trained with the L1 or L2 loss have significantly lower variance than the gradient maps of the original high-resolution images. In this work, we propose to alleviate the above issue by introducing a structure-enhancing loss function, coined Gradient Variance (GV) loss, and generate textures with perceptual-pleasant details. Specifically, during the training of the model, we extract patches from the gradient maps of the target and generated output, calculate the variance of each patch and form variance maps for these two images. Further, we minimize the distance between the computed variance maps to enforce the model to produce high variance gradient maps that will lead to the generation of high-resolution images with sharper edges. Experimental results show that the GV loss can significantly improve both Structure Similarity (SSIM) and peak signal-to-noise ratio (PSNR) performance of existing image super-resolution (SR) deep learning models.
Scientists have developed an artificial intelligence (AI) system that can analyze eye scans taken during a routine visit to an optician or eye clinic and identify patients at a high risk of a heart attack. Doctors have recognized that changes to the tiny blood vessels in the retina are indicators of broader vascular disease, including problems with the heart. In the research, led by the University of Leeds, deep learning techniques were used to train the AI system to automatically read retinal scans and identify those people who, over the following year, were likely to have a heart attack. Deep learning is a complex series of algorithms that enable computers to identify patterns in data and make predictions. Writing in the journal Nature Machine Intelligence, the researchers report that the AI system had an accuracy of between 70% and 80% and could be used as a second referral mechanism for in-depth cardiovascular investigation. The use of deep learning in the analysis of retinal scans could revolutionize the way patients are regularly screened for signs of heart disease. Professor Alex Frangi, who holds the Diamond Jubilee Chair in Computational Medicine at the University of Leeds and is a Turing Fellow at the Alan Turing Institute, supervised the research. He said: “Cardiovascular diseases, including heart attacks, are the leading cause of early death worldwide and the second-largest killer in the UK. This causes chronic ill-health and misery worldwide. “This technique opens up the possibility of revolutionizing the screening of cardiac disease. Retinal scans are comparatively cheap and routinely used in many optician practices. As a result of automated screening, patients who are at high risk of becoming ill could be referred to specialist cardiac services. “The scans could also be used to track the early signs of heart disease.” The study involved a worldwide collaboration of scientists, engineers, and clinicians from the University of Leeds; Leeds Teaching Hospitals NHS Trust; the University of York; the Cixi Institute of Biomedical Imaging in Ningbo, part of the Chinese Academy of Sciences; the University of Cote d’Azur, France; the National Centre for Biotechnology Information and the National Eye Institute, both part of the National Institutes for Health in the US; and KU Leuven in Belgium. The UK Biobank provided data for the study. Chris Gale, Professor of Cardiovascular Medicine at the University of Leeds and a Consultant Cardiologist at Leeds Teaching Hospitals NHS Trust, was one of the authors of the research paper. He said: “The AI system has the potential to identify individuals attending routine eye screening who are at higher future risk of cardiovascular disease, whereby preventative treatments could be started earlier to prevent premature cardiovascular disease.” Deep learning During the deep learning process, the AI system analyzed the retinal scans and cardiac scans of more than 5,000 people. The AI system identified associations between pathology in the retina and changes in the patient’s heart. Once the image patterns were learned, the AI system could estimate the size and pumping efficiency of the left ventricle, one of the heart’s four chambers, from retinal scans alone. An enlarged ventricle is linked with an increased risk of heart disease. With information on the estimated size of the left ventricle and its pumping efficiency combined with basic demographic data about the patient, their age, and sex, the AI system could predict their risk of a heart attack over the subsequent 12 months. Currently, details about the size and pumping efficiency of a patient’s left ventricle can only be determined if they have diagnostic tests such as echocardiography or magnetic resonance imaging of the heart. Those diagnostic tests can be expensive and are often only available in a hospital setting, making them inaccessible for people in countries with less well-resourced healthcare systems - or unnecessarily increasing healthcare costs and waiting times in developed countries. Sven Plein, British Heart Foundation Professor of Cardiovascular Imaging at the University of Leeds and one of the authors of the research paper, said: “The AI system is an excellent tool for unraveling the complex patterns that exist in nature, and that is what we have found here – the intricate pattern of changes in the retina linked to changes in the heart.”