The European Innovation Council (EIC) together with the Las Rozas de Madrid municipality is organising an EIC ePitching to Procurers session on 28 November 2022, at 12:00 PM (CET). This online session will be dedicated to matching EIC beneficiaries with procurers' needs in the field of Artificial Intelligence, and specifically, its use to address urban air quality challenges by enabling the implementation of a dynamic low-emission zone to improve air; Interested in this EIC ePitching opportunity? Make sure to register your interest before the deadline on 30 October 2022! And TAKE YOUR CHANCE to meet your future client! For the EIC ePitching to Las Rozas de Madrid, 6 companies will be; What's in store for throughout the day of the ePitching?

You probably already know about artificial intelligence (AI) and how it's being used more and more in our everyday lives. From the recommendation algorithms used by Netflix to the self-driving cars being developed by Tesla, AI is slowly but surely becoming a part of our world. But what you might not know is that there are many limitations in the current process of developing AI technology. Most AI systems are "dumb" in the sense that they can only be trained to do one specific task. For example, there might be a different algorithm for recognizing faces, another for understanding language, and yet another for driving a car.

I've read a couple of your books now, and what I want to know is this: Do you really think that artificial intelligence is a threat to the human race and could lead to our extinction? Yes, I do, but it also has the potential for enormous benefit. I do think it's probably going to be either very, very good for us or very, very bad. It's a bit like a strange attractor in chaos theory, the outcomes in the middle seem less likely. I'm reasonably hopeful because what will determine whether it's very good or very bad is largely us. We have time, certainly before artificial general intelligence (AGI) arrives. AGI is an artificial intelligence (AI) that has human-level cognitive ability, so can outperform us--or at least equal us--in every area of cognitive ability that we have. It also has volition and may be conscious, although that's not necessary. We have time before that arrives: We have time to make sure it's safe. At the same time as having scary potential, AI also brings the possibility of immortality and living forever by uploading your brain. Is that something you think will happen at some point? I certainly hope it will. Things like immortality, the complete end of poverty, the abolition of suffering, are all part of the very, very good outcome, if we get it right. If you have a superintelligence that is many, many times smarter than the smartest human, it could solve many of our problems. Problems like ageing and how to upload a mind into a computer, do seem, in principle, solvable. So yes, I do think they are realistic.

The world of data analytics is growing by the day. It is a clear debate of Data Science Vs Machine Learning. Businesses are beginning to understand more and more about their customers, their sales, and more. With businesses continuing to grow, data analytics is becoming more important and necessary. This is a question that many business owners are asking themselves: Which is more important, Data Science or Machine Learning?

Screening for lung cancer--the second-most common type of cancer worldwide--is a complex process. Doctors use Low-Dose Computed Tomography (LDCT) to scan patients and produce hundreds of 2D images. Physicians review them to identify the location and volume of tumors, which they then evaluate in context of the patient's medical history, lab work, biopsies, and other information, all of which help determine the stage of the illness and the best course of treatment. LDCT is an essential tool in fighting the deadly disease, but it's also a slow, painstaking process that leaves room for manual error. A new approach uses edge processing, AI, and secure data sharing to help doctors arrive at an accurate diagnosis much faster and start treatment sooner.

PRNewswire/ — A recent study published in European Radiology demonstrated that SwiftMR, an AI-powered MRI reconstruction solution from AIRS …

NFTs, as we think of them today, are relatively new and tend to be associated with digital content rather than physical goods. This framework suits trade finance assets because while they are linked to physical assets, the securities themselves are digital.

We are currently living in a world where we prioritize the use of self driving cars to drive us around, voice and facial recognition to keep our cell phones secured, and google search to answer our most basic questions. This type of technology is everywhere around us and we are only beginning to understand how to utilize it. Many people aren't aware of the rapid advancements scientists are making towards machine learning. This type of technology is being used by almost every large tech company and will continue to make advancements because of the potential it carries. Most AI in todays modern world are specialized, meaning they are meant to do one thing and process information and numbers for one specific outcome.

Combining machine learning with multimodal electrochemical sensing can significantly improve the analytical performance of biosensors, according to new findings from a Penn State research team. These improvements may benefit noninvasive health monitoring, such as testing that involves saliva or sweat. The findings were published this month in Analytica Chimica Acta. The researchers developed a novel analytical platform that enabled them to selectively measure multiple biomolecules using a single sensor, saving space and reducing complexity as compared to the usual route of using multi-sensor systems. In particular, they showed that their sensor can simultaneously detect small quantities of uric acid and tyrosine--two important biomarkers associated with kidney and cardiovascular diseases, diabetes, metabolic disorders, and neuropsychiatric and eating disorders--in sweat and saliva, making the developed method suitable for personalized health monitoring and intervention.

In recent years, deep learning techniques have proved to be highly valuable for tackling countless research and real-world problems. Researchers at Feedzai, a financial data science company based in Portugal, have demonstrated the potential of deep learning for the prevention and detection of illicit money laundering activities. In a paper presented at the 3rd ACM International Conference on AI in Finance, the team at Feedzai introduced LaundroGraph, a self-supervised model that could simplify the cumbersome process of reviewing large amounts of financial interactions looking for suspicious transactions or monetary exchanges. Their model is based on a graph neural network, an artificial neural network (ANN) designed to autonomously process large amounts of data that can be represented as a graph. "Wanting to strengthen our AML solution, and after identifying major pains with the current AML reviewing process, we thought about solutions to overcome these challenges using AI," Mario Cardoso, a Research Data Scientist at Feedzai, told TechXplore.