Predicting the economy's short-term dynamics -- a vital input to economic agents' decision-making process -- often uses lagged indicators in linear models. This is typically sufficient during normal times but could prove inadequate during crisis periods. This paper aims to demonstrate that non-traditional and timely data such as retail and wholesale payments, with the aid of nonlinear machine learning approaches, can provide policymakers with sophisticated models to accurately estimate key macroeconomic indicators in near real-time. Moreover, we provide a set of econometric tools to mitigate overfitting and interpretability challenges in machine learning models to improve their effectiveness for policy use. Our models with payments data, nonlinear methods, and tailored cross-validation approaches help improve macroeconomic nowcasting accuracy up to 40\% -- with higher gains during the COVID-19 period. We observe that the contribution of payments data for economic predictions is small and linear during low and normal growth periods. However, the payments data contribution is large, asymmetrical, and nonlinear during strong negative or positive growth periods.

John Laird, the John L. Tishman Professor of Engineering, has been awarded the 2018 Herbert A. Simon Prize for Advances in Cognitive Systems along with his collaborator Prof. Paul Rosenbloom of the University of Southern California. This award recognizes the pair's research on cognitive architectures, especially their Soar project, their applications to knowledge-based systems and models of human cognition, and their contributions to theories of representation, reasoning, problem solving, and learning. The recipients, the awarding committee writes, have been "energetic contributors to AI and cognitive science" for over 30 years. Laird's and Rosenbloom's interdisciplinary and integrative research, both jointly and individually, has addressed many facets of high-level cognition, and their contributions to Soar have helped create one of the industry's most successful tools for developing intelligent systems. Soar is a general cognitive architecture for developing systems that exhibit intelligent behavior.

The Conseil d'État, France's highest administrative court, has pooh-poohed the idea that artificial intelligence poses an existential threat to humanity, while reassuring the public that they themselves are not secretly killer robots. Rapporteur and State Counsel Alexandre Lallet said: "We're not at the point which some call the'singularity,' when machines take control and humans get forced into their service, like in some American blockbusters or works of science fiction. French President Emmanuel Macron looks through a microscope during a visit on the topic of artificial intelligence at the Institut Curie Hospital in 2018 in Paris, France Pool photo by Benoit Tessier/AFP via Getty Images "All the scientists that we talked to think that [singularity] is a fantasy, pure and simple, and it amounts to mere marketing," Tuot continued, "We can confirm the revelation that none of us are, in fact, robots." Renaud Vedel, who was in the working group as France's top AI coordinator and is now Digital Affairs Minister Jean-Noël Barrot's cabinet director, said in an interview in May that the argument had been put to rest, "The debate was a bit too focused on the singularity and those kinds of things, but that's over now, thankfully." PARIS -- The Conseil d'État, France's highest administrative court, has pooh-poohed the idea that artificial intelligence poses an existential threat to humanity, while reassuring the public that they themselves are not secretly killer robots. Rapporteur and State Counsel Alexandre Lallet said: "We're not at the point which some call the'singularity,' when machines take control and humans get forced into their service, like in some American blockbusters or works of science fiction.

NICEATM, other scientists within the NIEHS Division of the NTP, the DOE's Oak Ridge National Laboratory, and FDA are collaborating to automate the process of identifying high-quality developmental toxicity studies in the published scientific literature. The approach applies natural language processing and machine learning methods to identify specific data elements in the full text of scientific publications using both unsupervised and supervised approaches. Preliminary models were trained using a uterotrophic database (Kleinstreuer et al. 2016) built for the EPA Endocrine Disruptor Screening Program. The models leveraged natural language processing and multivariate machine learning models to identify papers that meet minimum criteria to be considered guideline-like studies (Herrmannova et al. 2018). Supervised and unsupervised approaches were developed to automatically extract text features that correspond to study descriptors and classify papers based on their adherence to minimum criteria derived from regulatory guideline studies.

From Amazon recommendations to self-driven cars, Artificial Intelligence (AI) seems to be fuelling the world in recent times. The term, along with Machine Learning (ML), is the talk of the town. Together, AI and ML are changing the space of business, tech, and even our personal lives at an astonishing rate. But are they two interchangeable terms? How can we distinguish one from the other, and what are the salient features of each?

Modern cyber attackers' tactics, techniques, and procedures (TTPs) have become both rapid and abundant while advanced threats such as ransomware, cryptojacking, phishing, and software supply chain attacks are on an explosive rise. The increasing dependence global workforces have on digital resources adds another facet to a growing cyber attack surface we all now share. In an effort to stand up to these challenges, businesses task their CISOs with developing, maintaining, and constantly updating their cybersecurity strategies and solutions. From a tactical standpoint, CISOs ensure that their business's security architecture can withstand the ever-shifting modern threat landscape. This means choosing the right tool stack that is capable of combating complex cyber threats at the breakneck speed in which they appear.

Taipei, Taiwan – Taiwan's military has said it shot down an unidentified civilian drone over the outlying island of Kinmen amid a continuing increase in Chinese military activity around the island since last month's controversial visit by US House of Representatives Speaker Nancy Pelosi. The drone, which was shot down on Thursday, is the first to be hit following a warning from Taiwan that it would use live ammunition against drones. The threat came after a video of Taiwanese soldiers throwing rocks at a Chinese drone went viral. Drone flights have reportedly escalated near Kinmen, which is located a few kilometres off the coast of China, and around the Matsu Islands in the East China Sea. The decision to fire on Chinese drones is a departure for Taiwan's military, said Yen-Chi Hsu, an assistant researcher at Taiwan's Council on Strategic and Wargaming Studies.

Fox News host Bret Baier analyzes China's ambitions for agriculture dominance as it competes with the U.S. on Wednesday's'Special Report.' An unidentified civilian drone was shot down by the Taiwanese military Thursday after its entered its airspace by flying over one of Taipei's smallest islands which sits just a couple miles from mainland China. The drone reportedly flew over the waters of Shiyu Islet, also referred to as Lion Islet, which houses a small contingent of stationed Taiwanese military personnel. The incident comes as tensions between Taiwan and China continue to mount following Speaker of the House Nancy Pelosi's visit to Taipei last month. Shiyu, or Lion Islet, which is part of Kinmen county, one of Taiwan's offshore islands, is seen in front of China's Xiamen, on Lieyu island, Kinmen county, Taiwan Aug. 20, 2018.

Sept 1 (Reuters) - Chip designer Nvidia Corp (NVDA.O) said on Wednesday that U.S. officials told it to stop exporting two top computing chips for artificial intelligence work to China, a move that could cripple Chinese firms' ability to carry out advanced work like image recognition and hamper Nvidia's business in the country. The announcement signals a major escalation of the U.S. crackdown on China's technological capabilities as tensions bubble over the fate of Taiwan, where chips for Nvidia and almost every other major chip firm are manufactured. Nvidia shares fell 6.6% after hours. The company said the ban, which affects its A100 and H100 chips designed to speed up machine learning tasks, could interfere with completion of developing the H100, the flagship chip it announced this year. Shares of rival Advanced Micro Devices Inc (AMD.O) fell 3.7% after hours. An AMD spokesman told Reuters it had received new license requirements that will stop its MI250 artificial intelligence chips from being exported to China but it believes its MI100 chips will not be affected.

The US government has introduced and immediately started imposing a new rule that would restrict the export of computer chips used for supercomputers and artificial intelligence to Russia and China. NVIDIA has revealed in a SEC filing that the government has informed the company of the new license requirement affecting one of its current (A100) and one of its upcoming (H100) GPUs, which were designed to speed up machine learning tasks. The government apparently indicated to NVIDIA that the new license requirement will address the risk that the affected products may be used for military purposes by either country. As The New York Times notes, AI and machine learning are used for various applications that include weapons development and surveillance. "While we are not in a position to outline specific policy changes at this time, we are taking a comprehensive approach to implement additional actions necessary related to technologies, end-uses, and end-users to protect US national security and foreign policy interests."