Machine learning can pinpoint "genes of importance" that help crops to grow with less fertilizer, according to a new study published in Nature Communications. It can also predict additional traits in plants and disease outcomes in animals, illustrating its applications beyond agriculture. Using genomic data to predict outcomes in agriculture and medicine is both a promise and challenge for systems biology. Researchers have been working to determine how to best use the vast amount of genomic data available to predict how organisms respond to changes in nutrition, toxins, and pathogen exposure--which in turn would inform crop improvement, disease prognosis, epidemiology, and public health. However, accurately predicting such complex outcomes in agriculture and medicine from genome-scale information remains a significant challenge.

Olaf Scholz, the centre-left Social Democrat (SPD) candidate to succeed Angela Merkel, is one of Germany's most influential politicians, with a reputation for being meticulous, confident and fiercely ambitious. As finance minister and vice-chancellor under Merkel, he enjoys a close relationship with the chancellor and has even sought to position himself as the true Merkel continuity candidate, despite hailing from a different party. He was pictured recently on the cover of the Sueddeutsche Zeitung magazine adopting Merkel's famous "rhombus" hand gesture -- a stunt that provoked consternation from rivals in Merkel's CDU camp. Nicknamed "Scholzomat" for his robotic speeches, Scholz has hardly stood out for his charisma in the run-up to Sunday's election. But unlike his two main rivals, Armin Laschet of Merkel's CDU-CSU alliance and Annalena Baerbock of the Greens, the 63-year-old has also managed not to make embarrassing mistakes on the campaign trail. As a result, he is now the favourite to head Germany's next coalition government.

A man taking a selfie is silhouetted against the overcast sky along the Chicago skyline Wednesday, July 21, 2021, in Chicago. Clearview AI, the controversial facial recognition company that scrapes public images from social media to aid law enforcement probes, has subpoenaed internal documents from some of the groups that first exposed its activities. The firm served subpoenas in August to civil society coalition Open The Government, its policy analyst Freddy Martinez and the police accountability nonprofit that he'd previously founded, Lucy Parsons Labs -- demanding any correspondence they'd had with journalists about Clearview and its leaders, as well as information they'd uncovered about the company and its founders in public records requests, over the last four years. The subpoenas, obtained by POLITICO, could draw the groups into lengthy court battles and, they argue, dissuade others from taking on Clearview or other companies working on potentially problematic technologies.

Artificial Intelligence (AI) is the ability of technology to think and act similar to a human and is being utilized to help fight off potential cyberattacks. Employing AI and machine learning to detect vulnerabilities significantly enhances human capabilities, as AI can analyze and report millions of cyber threats in a fraction of the time it might take a person to do the same. AI can get "smarter" and "learn"; as an AI algorithm continues to search and monitor data, it can improve its understanding of different types of potential attacks. We've all heard a lot about cyberattacks and data breaches on the news lately, but what exactly is a cyberattack and why is it important to protect our data? "In short, a cyberattack is a deliberate and often targeted attempt to mount an action via or against digital technology," says IEEE Senior Member Steven Furnell.

We develop a Distributionally Robust Optimization (DRO) formulation for Multiclass Logistic Regression (MLR), which could tolerate data contaminated by outliers. The DRO framework uses a probabilistic ambiguity set defined as a ball of distributions that are close to the empirical distribution of the training set in the sense of the Wasserstein metric. We relax the DRO formulation into a regularized learning problem whose regularizer is a norm of the coefficient matrix. We establish out-of-sample performance guarantees for the solutions to our model, offering insights on the role of the regularizer in controlling the prediction error. We apply the proposed method in rendering deep CNN-based image classifiers robust to random and adversarial attacks. Specifically, using the MNIST and CIFAR-10 datasets, we demonstrate reductions in test error rate by up to 78.8% and loss by up to 90.8%. We also show that with a limited number of perturbed images in the training set, our method can improve the error rate by up to 49.49% and the loss by up to 68.93% compared to Empirical Risk Minimization (ERM), converging faster to an ideal loss/error rate as the number of perturbed images increases.

Entity linking (EL) is the process of linking entity mentions appearing in web text with their corresponding entities in a knowledge base. EL plays an important role in the fields of knowledge engineering and data mining, underlying a variety of downstream applications such as knowledge base population, content analysis, relation extraction, and question answering. In recent years, deep learning (DL), which has achieved tremendous success in various domains, has also been leveraged in EL methods to surpass traditional machine learning based methods and yield the state-of-the-art performance. In this survey, we present a comprehensive review and analysis of existing DL based EL methods. First of all, we propose a new taxonomy, which organizes existing DL based EL methods using three axes: embedding, feature, and algorithm. Then we systematically survey the representative EL methods along the three axes of the taxonomy. Later, we introduce ten commonly used EL data sets and give a quantitative performance analysis of DL based EL methods over these data sets. Finally, we discuss the remaining limitations of existing methods and highlight some promising future directions.

In recent months, bills to regulate the use of artificial intelligence (AI) technology in the country have been advanced in the legislature. The most advanced proposal from the chamber, written by Representative Eduardo Bismarck (PDT-CE), is ready for a vote in the House plenary. Experts considered the projects to have positive points, but said that regulation may be premature, given the speed with which AI technology is developing. In fiction, AI is often portrayed in menacing stories, sometimes involving machines rebelling against humans. She is, for example, in films such as 2001: A Space Odyssey (1968), or The Matrix (1999).

The European Union is getting back to work after the summer break, and one of the key files on everyone's mind is the EU Artificial Intelligence Act (AIA). Over the summer, the European Commission held a consultation on the AIA that received 304 responses, with everyone from the usual Big Tech players down to the Council of European Dentists having their say. Access Now submitted a response to the consultation in August that outlined a number of key issues that need to be addressed in the next stages of the legislative process. If you want to regulate something, you need to define it properly; if not, you're creating problematic loopholes. Unfortunately, the definitions of emotion recognition (Article 3(34)) and biometric categorisation (Article 3(35)) in the current draft of the EU Artificial Intelligence Act are technically flawed.

Traditionally, policymakers and urban planners haven't had access to city data that can reveal complex patterns and relationships between factors that influence urban development. In some cases, data is too laborious or costly to measure at frequent time intervals, and in others, unexpected or unforeseen circumstances such as a pandemic like COVID-19 are responsible for invalidating earlier forecasts. But this is changing rapidly, with emerging technologies unlocking new possibilities for urban planning. Advances in emerging technologies like Artificial Intelligence and Machine Learning can help us understand our cities better and derive useful insights from real-time data collected through automated models that provide a much closer view of the situation on-ground compared to traditional approaches. These insights can properly assess public interests and help policymakers in making decisions that are more sustainable.

NEW YORK, September 23, 2021--(BUSINESS WIRE)--Helbiz Inc. (NASDAQ: HLBZ), a global leader in micro-mobility and the first in its industry to be publicly listed on Nasdaq, today announced a partnership with Drover AI to integrate its PathPilot safety technology onto Helbiz e-scooters. Helbiz will be the exclusive operator of PathPilot in Italy, with an initial deployment in Milan by end of the year. The company plans to expand the integration across other markets as the partnership grows. This press release features multimedia. The PathPilot technology is powered by artificial intelligence and computer vision, using onboard cameras to locate the surroundings of e-scooters.