JAXenter: Different approaches have been made to launch attacks against machine learning systems, as their use continues to become increasingly widespread. Can you tell us more about these angles of attack and how they differ from each other? During an evasion attack, the adversary aims to avoid detection by deliberately manipulating an example input. JAXenter: In your opinion, which type of attack on ML systems currently poses the greatest threat, and for what reason? David Glavas: Evasion attacks, because they can be performed with less knowledge about the target system.

In a recent competition for predicting consumer credit risk, AutoAI beat 90% of the participating data scientists. AutoAI is a new tool that utilizes sophisticated training features to automate many of the complicated and time-consuming tasks of feature engineering and building machine learning models, without the need to be a pro at data science. The next video shows a preview of the AutoAI tool. Today's UI is a bit different than the one in the video, which will be generally available soon. You can try it today here.

AI and machine learning model architectures developed by Alphabet's DeepMind have substantially improved the Google Play Store's discovery systems, according to Google. In a blog post this morning, DeepMind detailed a collaboration to bolster the recommendation engine underpinning the Play Store, the app and game marketplace that's actively used by over two billion Android users monthly. It claims that as a result, app recommendations are now more personalized than they used to be. In an email, a Google spokesperson told VentureBeat that the new system was deployed this year. It's not the first time the DeepMind team has contributed its expertise to the Android side of Google's business, it's worth noting.

When a patient has a stroke, every minute counts. Here, prompt action can prevent serious brain damage. If a clot is blocking a large blood vessel in the brain, surgeons can remove this occlusion by means of a catheter inserted in the patient's groin. However, this is a complicated procedure, requiring a lot of experience, and only a few specialists are capable of carrying it out. In new work, Fraunhofer researchers have been investigating whether artificial intelligence might be used to steer a catheter automatically and reliably to a blocked blood vessel.

Digital transformation is no longer a nice to have, it is the essential first step on a journey that may well ultimately embrace some, most or all of the following: big data, robotic process automation, blockchain, artificial intelligence, virtual and augmented reality and 3D printing. If this transformation is to deliver the expected return on investment, it is vital that procurement is at the heart of it. To make that happen, procurement and supply chain leaders must first confront some inconvenient truths. Read this Supply Management Insider report, in partnership with Expense Reduction Analysts (ERA), as we explore the path to a successful digital transformation.

Imagine your body is like a loaf of sliced bread. During an MRI scan, a powerful magnet and radio waves create detailed images of each "slice" of your body, then a computer puts the slices together to show a full picture of your anatomy. But before the slicing comes the choosing. Before an MRI technologist can scan a patient, they have to manually specify the slices they want the MRI to acquire. This process can take several minutes of tweaking and adjusting, leaving a patient waiting anxiously in the MRI scanner and adding unnecessary steps to set up each scan.

AIIA 2019 is organized by the Italian Association for Artificial Intelligence (AIIA – Associazione Italiana per l'Intelligenza Artificiale), which is a non-profit scientific society founded in 1988 devoted to the promotion of Artificial Intelligence. The society aims to increase the public awareness of AI, encourage the teaching of it and promote research in the field.

Machine learning is on a roll and has become an integral part of many niches such as robotics, e-commerce, spam filtering, etc. The experts use machine learning to develop models based on training data only to deploy them later on an unseen one to check it's functionality. Every model has parameters associated with it, which can be estimated. Though, there are some, which impacts the entire performance of the model but cannot be estimated. Such parameters are referred to as hyperparameters.

Jesús Gómez-Gardeñes received the degree in Physics from the University of Zaragoza, Spain, in the year 2002, and a Ph.D. in Science (Physics) from the same University in 2006 that was awarded with "Premio Extraordinario". After a two-years postdoctoral period at the Scuola Superiore di Catania (Italy) and the Universitat Rovira i Virgili (Tarragona) as a "Juan de la Cierva" researcher, he joined, in October 2008, the Applied Mathematics Department of the University Rey Juan Carlos (Madrid) as an Assistant Professor where he combined teaching and research duties until the end of the year 2010. In January 2011 he joined the University of Zaragoza as "Ramón y Cajal" senior researcher where, since January 2019, he is Associate Professor (Profesor Titular). In 2018 Dr. Gómez-Gardeñes founded the Group of Theoretical & Applied Modeling (GOTHAM) at the Institute for Biocomputation and Physics of Complex Systems (BIFI). The main research fields of his group are statistical physics, nonlinear dynamics and the theory of complex networks.

THERE IS no technology as powerful as artificial intelligence (AI), that much is certain. In the world of business, organizations have shown faith in the technology by investing in a number of pilot projects -- but unfortunately, they have been unable to scale up those projects to unleash the full potential of AI. According to a recent survey of 1,500 c-suite executives, 75 percent said they believe they risk going out of business in 5 years if they don't scale AI. Further, 84 percent of the executives surveyed said they believe they won't achieve their growth objectives unless they deploy AI. To top it all, the survey also revealed that the few companies that actually managed to strategically scale AI projects seem to have nearly twice the success rate and three-times the return from AI investments as compared to those pursuing pilot projects in silos.