Follow this author to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Growing up around the rows of corn and soybeans in Fremont, Ohio, Mónica Ramírez spent her earliest days being looked after by her maternal bisabuela, or great-grandmother, Virginia Guardiola. From her home in town, Guardiola served as an informal, one-woman welcome committee for farmworkers who arrived in Fremont, disoriented and alone.

A Woman Was in the US Legally. María de Jesús Estrada Juárez was applying for her green card and thought she was doing everything right. Instead, she was arrested and deported to Mexico. María de Jesús Estrada Juárez came to the US from Mexico in 1998 at 15 years old. Later, she was a recipient of Deferred Action for Childhood Arrivals (DACA), the policy meant to protect undocumented immigrants who arrived in the country as minors from deportation .

This paper presents a deep reinforcement learning (DRL) framework for active flow control (AFC) to reduce drag in aerodynamic bodies. Tested on a 3D cylinder at Re = 100, the DRL approach achieved a 9.32% drag reduction and a 78.4% decrease in lift oscillations by learning advanced actuation strategies. The methodology integrates a CFD solver with a DRL model using an in-memory database for efficient communication between the two instances, making it scalable to more complex flows and higher Reynolds numbers. 1 INTRODUCTION In light of the current climate crisis, the transportation industry faces significant challenges in reducing fossil fuel emissions to mitigate the adverse effects of climate change.

Staring through a microscope at samples of material for hours on end, attempting to locate monolayers, is one of the most laborious and intimidating tasks for undergraduate assistants in university research laboratories. As a result of their unique properties, these two-dimensional materials -- which are less than 1/100,000th the width of a human hair -- are in high demand for use in photonics, electronics, and optoelectronic devices. Research labs hire armies of undergraduates to do nothing but look for monolayers. It's very tedious, and if you get tired, you might miss some of the monolayers or you might start making misidentifications. Jesús Sánchez Juárez, a Ph.D. student in the Cardenas Lab, has made work simpler for undergraduates, their research facilities, and companies that have difficulty identifying monolayers.

From traffic control and waste management to biometric surveillance systems and predictive policing models, the potential uses of artificial intelligence (AI) in cities are incredibly diverse, and could impact every aspect of urban life. In response to the increasing deployment of AI in cities – and the general lack of authority that municipal governments have to challenge central government decisions or legislate themselves – London, Barcelona and Amsterdam launched the Global Observatory on Urban AI in June 2021. The initiative aims to monitor AI deployment trends and promote its ethical use, and is part of the wider Cities Coalition for Digital Rights (CC4DR), which was set up in November 2018 by Amsterdam, Barcelona and New York to promote and defend digital rights. It now has more than 50 cities participating worldwide. Apart from city participants, the Observatory is also being run in partnership with UN-Habitat, a United Nations initiative to improve the quality of life in urban areas, and research group CIDOB-Barcelona Centre for International Affairs.

Earlier this year, Talent Garden hired Spain's Lorena Pérez as its new chief people officer. With more than two decades of experience in HR, Pérez plans to use her expertise to scale Talent Garden in Europe while coming up with ways to retain and motivate the company's talent. She discussed the importance of conserving company culture while a company scales, and some of the ways she uses technology to improve her work and efficiency in HR. I come from the HR world, of course! I've been working for multinational companies, mainly creating and developing HR and people departments. I have more than 20 years of experience now, with my first relevant experience being in a communications company in the tech sector.

Natural language understanding(NLU) is one of the richest areas in deep learning which includes highly diverse tasks such as reaching comprehension, question-answering or machine translation. Traditionally, NLU models focus on solving only of those tasks and are useless when applied to other NLU-domains. Also, NLU models have mostly evolved as supervised learning architectures that require expensive training exercises. Recently, researchers from OpenAI challenged both assumptions in a paper that introduces a single unsupervised NLU model that is able to achieve state-of-the-art performance in many NLU tasks. The idea of using unsupervised learning for different NLU tasks has been gaining traction in the last few months.

Artificial Intelligence is rapidly becoming more advanced. One of the organisations working on AI is OpenAI; the not-for-profit artificial intelligence research organisation co-founded by Elon Musk. Last week, they produced a paper demonstrating the progress they have made on predictive text software. The AI that they developed, called GPT2, is so efficient in writing a text based on just a few lines of input, that OpenAI decided not to release the comprehensive research to the public. Already, GPT2 has been described as the text version of deep fakes.

An artificial intelligence project backed by SpaceX founder Elon Musk has been so successful its developers are not releasing it to the public for fear it will be misused. Research group Open AI developed a'large-scale unsupervised language model' that is able to generate news stories from a simple headline. But the group insists it will not be releasing details of the programme and instead has unveiled a much smaller version for research purposes. Its developers claim the technology is poised to rapidly advance in the coming years and the full specification and details of the project will only be released when the negative applications have been discussed by researchers. Elon Musk's AI research group Open AI announced in a paper yesterday that it has generated'a large-scale unsupervised language model' that can write news stories from little more than a headline.

Imagine a robot able to retrieve a pile of laundry from the back of a cluttered closet, deliver it to a washing machine, start the cycle and then zip off to the kitchen to start preparing dinner. This may have been a domestic dream a half-century ago, when the fields of robotics and artificial intelligence first captured public imagination. However, it quickly became clear that even "simple" human actions are extremely difficult to replicate in robots. Now, MIT computer scientists are tackling the problem with a hierarchical, progressive algorithm that has the potential to greatly reduce the computational cost associated with performing complex actions. Leslie Kaelbling, the Panasonic Professor of Computer Science and Engineering, and Tomás Lozano-Pérez, the School of Engineering Professor of Teaching Excellence and co-director of MIT's Center for Robotics, outline their approach in a paper titled "Hierarchical Task and Motion Planning in the Now," which they presented at the IEEE Conference on Robotics and Automation earlier this month in Shanghai.