The US will impose new restrictions on the export of certain AI programs overseas, including to rival China. The ban, which comes into force on Monday, is the first to be applied under a 2018 law known as the Export Control Reform Act or ECRA. This requires the government to examine how it can restrict the export of "emerging" technologies "essential to the national security of the United States" -- including AI. News of the ban was first reported by Reuters. When ECRA was announced in 2018, some in the tech industry feared it would harm the field of artificial intelligence, which benefits greatly from the exchange of research and commercial programs across borders. Although the US is generally considered to be the world leader in AI, China is a strong second place and gaining fast.

The course is targeted PhD students and postdocs in the field of diabetes and other Health areas, with a strong active interest and knowledge in complex data integration, as well as Big Data and Artificial Intelligence. Due to the limited number of seats on the course, you are not guaranteed a seat until you receive an e-mail with final confirmation. You will thus initially be placed on a waiting list, which will be prioritized according to your submitted abstract, motivational letter and CV. This course aims to highlight the state-of-the-art data methodologies in diabetes and other health areas within Big Data and AI and explore the strengths and limitations of using machine learning methodologies on complex diseases. In addition the course will focus on how data can contribute to molecular understanding of diseases; the diversity of methods used when working with data integration & artificial and gives the opportunity to the participants to have an idea on how their own methods relate to global efforts, and expand their horizon and network.

U.S. technology companies that build artificial intelligence software for analyzing satellite imagery will face new restrictions on exporting their products to China and elsewhere. The Commerce Department said new export rules take effect Monday that target emerging technology that could give the U.S. a significant military or intelligence advantage. A special license would be required to sell software outside the U.S. that can automatically scan aerial images to identify objects of interest, such as vehicles or houses. The rules could affect a growing sector of the tech industry using algorithms to analyze satellite images of crops, trade patterns and other changes affecting the economy or environment. The new export rules are the result of Congress passing a law in 2018 that updated national security-related export controls to protect "emerging and foundational" technology that could end up in the hands of foreign governments.

The cosmetics market remains as lucrative as ever, if the latest estimates are anything to go by. It's anticipated to be worth $806 billion by 2023, driven in part by spending on AI in retail, which alone is expected to top $7.3 billion by 2022 thanks to blossoming tech like computer vision. L'Oréal has its finger on the pulse. Following on the heels of My Skin Track pH, a strip co-developed with skincare brand La Roche-Posay that can measure skin acid on the fly, it today debuted the Perso, an AI-powered system that's designed to deliver personalized skincare and cosmetic formulas. The Perso, which measures 6.5 inches tall and weighs just over a pound, features an automatic mechanism that dispenses portioned doses of product at its top.

The U.S. government has taken the decision to limit exports of artificial intelligence software in a bid to keep it out of the hands of countries like Russia and China. With the new restrictions, which come into effect on Monday, companies that export geospatial imagery software from the country will need to acquire a license. The only time a license will not be needed is if the software is being exported to Canada. The Commerce Department has been responsible for devising a system to limit AI software exports after the Export Control Reform Act was passed in 2018. The law required the Commerce Department to boost controls on sensitive software which could potentially be used by countries such as China for reasons pertaining to economy and security.

President Trump continued issuing threatening warnings Sunday that more action would come if Iran retaliates against the U.S. for the killing of Iranian Gen. Qassem Soleimani, which critics have been calling an illegal action taken without consulting Congress. "These Media Posts will serve as notification to the United States Congress that should Iran strike any U.S. person or target, the United States will quickly & fully strike back, & perhaps in a disproportionate manner," he tweeted Sunday afternoon. "Such legal notice is not required, but is given nevertheless!" Many Democrats in Congress had said the Trump administration failed to consult with legislative leaders before conducting the drone attack Friday against Soleimani, the head of the Islamic Revolutionary Guard Corps' elite Quds Force, and the White House faced a barrage of questions about the killing's legality. "I really worry that the actions the president took will get us into what he calls another endless war in the Middle East. He promised we wouldn't have that," said Chuck Schumer of New York, the Senate's top Democrat.

BEIRUT – Hezbollah leader Sayyed Hassan Nasrallah said on Sunday he had warned Iranian Maj. Gen. Qassem Soleimani of the risk of assassination and met him in the Lebanese capital Beirut on New Year's Day before he was killed in a U.S. attack in Baghdad. Nasrallah, in a speech commemorating Soleimani, said he had told Iran's pre-eminent military commander some time ago of concern for his life. Soleimani was killed on Friday in a targeted U.S. drone strike on his convoy at Baghdad airport. "I told him … there is great focus on you in the American media, press and magazines and they're printing your pictures on the front page as'the irreplaceable general,' this is media and political priming for your assassination," Nasrallah said.

BAGHDAD – Two rockets hit near the U.S. Embassy in Iraq's capital Sunday, witnesses told AFP, hours after the ambassador was summoned over a U.S. strike that killed top Iraqi and Iranian commanders. Sunday's attack was the second night in a row that the Green Zone was hit and the 14th time over the last two months that U.S. installations have been targeted. A third rocket simultaneously hit a family home outside the Green Zone, wounding four, medical sources told AFP. Ties between Iraq and the U.S. have deteriorated after an American drone attack Friday on the Baghdad international airport that killed Iran's Maj. The precision strike came just days after a pro-Iran mob attacked the U.S. Embassy in Baghdad.

Current system thermal-hydraulic codes have limited credibility in simulating real plant conditions, especially when the geometry and boundary conditions are extrapolated beyond the range of test facilities. This paper proposes a data-driven approach, Feature Similarity Measurement FFSM), to establish a technical basis to overcome these difficulties by exploring local patterns using machine learning. The underlying local patterns in multiscale data are represented by a set of physical features that embody the information from a physical system of interest, empirical correlations, and the effect of mesh size. After performing a limited number of high-fidelity numerical simulations and a sufficient amount of fast-running coarse-mesh simulations, an error database is built, and deep learning is applied to construct and explore the relationship between the local physical features and simulation errors. Case studies based on mixed convection have been designed for demonstrating the capability of data-driven models in bridging global scale gaps.

Compact semiconductor device models are essential for efficiently designing and analyzing large circuits. However, traditional compact model development requires a large amount of manual effort and can span many years. Moreover, inclusion of new physics (eg, radiation effects) into an existing compact model is not trivial and may require redevelopment from scratch. Machine Learning (ML) techniques have the potential to automate and significantly speed up the development of compact models. In addition, ML provides a range of modeling options that can be used to develop hierarchies of compact models tailored to specific circuit design stages. In this paper, we explore three such options: (1) table-based interpolation, (2)Generalized Moving Least-Squares, and (3) feed-forward Deep Neural Networks, to develop compact models for a p-n junction diode. We evaluate the performance of these "data-driven" compact models by (1) comparing their voltage-current characteristics against laboratory data, and (2) building a bridge rectifier circuit using these devices, predicting the circuit's behavior using SPICE-like circuit simulations, and then comparing these predictions against laboratory measurements of the same circuit.