Fumbling to find flashlights during blackouts may soon be a distant memory, as quantum computing and artificial intelligence could learn to decipher an electric grid's problematic quirks and solve system hiccups so fast, humans may not notice. Rather than energy grid faults turning into giant problems--such as voltage variations or widespread blackouts--blazing fast computation blended with artificial intelligence could rapidly diagnose trouble and find solutions in tiny splits of seconds, according to Cornell research forthcoming in Applied Energy (Dec. 1, 2021). "Energy power system failures are an old problem and we are still using classic computational methods to resolve them," said Fengqi You, the Roxanne E. and Michael J. Zak Professor in Energy Systems Engineering in the College of Engineering. "Today's power systems can benefit from AI and the computational power of quantum computing, so power systems can be stable and reliable." You, along with doctoral student Akshay Ajagekar, are co-authors of "Quantum Computing-based Hybrid Deep Learning for Fault Diagnosis in Electrical Power Systems."

For the past decade in Los Angeles and the State of California, the question is not if there will be wildfires--but rather when and where they will sprout up and how to protect people from these threats. As such, firefighters need to know how to plan and deploy limited resources. One such solution is controlled burns of flammable brush to prevent worst-case scenarios of growing tinder that left unattended, provides fodder for megafires. With $5 million in support from the National Science Foundation's Convergence Accelerator program, a team of researchers, which includes UC San Diego's San Diego Supercomputer Center (SDSC), the University of Southern California's Viterbi School of Engineering and the Tall Timbers Research Station in Florida, will bring the power of AI to help firefighters strategize how best to plan these controlled burns, as well as manage unexpected blazes. SDSC will lead the effort through the development of "BurnPro3D," a new decision support platform to help the fire response and mitigation community quickly and accurately understand risks and tradeoffs presented by a fire to more effectively plan controlled burns and manage wildfires.

US and EU officials opened their two-day, high-level meetings in Pittsburgh on Wednesday, an effort to repair relations damaged under the administration of former president Donald Trump and boost cooperation on technology issues. The inaugural meeting of the Trade and Technology Council (TTC) comes as industries worldwide grapple with shortages of crucial semiconductors and is being held in Pittsburgh, a Pennsylvania city that was once the heart of the American steel industry and has since evolved into a tech hub. The ministers met at Mill 19, a massive World War II-era munitions factory and later steel mill on the shores of the Monongahela River that has been reborn as an advanced robotics facility for researchers from Carnegie Mellon University. The shadow of steel hangs over the meetings in other ways as well, especially as the two sides have yet to resolve a conflict over Trump-era tariffs on steel and aluminum. The former president cited US national security concerns in June 2018 when he imposed punitive tariffs of 25 percent on steel imports and 10 percent on aluminum, which have been a thorn in the side of trans-Atlantic relations since.

Graph neural networks are emerging as promising methods for modeling molecular graphs, in which nodes and edges correspond to atoms and chemical bonds, respectively. Recent studies show that when 3D molecular geometries, such as bond lengths and angles, are available, molecular property prediction tasks can be made more accurate. However, computing of 3D molecular geometries requires quantum calculations that are computationally prohibitive. For example, accurate calculation of 3D geometries of a small molecule requires hours of computing time using density functional theory (DFT). Here, we propose to predict the ground-state 3D geometries from molecular graphs using machine learning methods. To make this feasible, we develop a benchmark, known as Molecule3D, that includes a dataset with precise ground-state geometries of approximately 4 million molecules derived from DFT. We also provide a set of software tools for data processing, splitting, training, and evaluation, etc. Specifically, we propose to assess the error and validity of predicted geometries using four metrics. We implement two baseline methods that either predict the pairwise distance between atoms or atom coordinates in 3D space. Experimental results show that, compared with generating 3D geometries with RDKit, our method can achieve comparable prediction accuracy but with much smaller computational costs. Our Molecule3D is available as a module of the MoleculeX software library (https://github.com/divelab/MoleculeX).

CEIPAL, an industry-leading talent management platform, will be showcasing some of its latest advances in talent acquisition and recruitment CRM technology at ITServe Alliance's Synergy Conference 2021. The annual conference for IT & business transformation is being held September 30 - October 1 in Dallas. At the event, technology and industry leaders will discuss the challenges around business growth, trends and innovations in technology, and understanding immigration laws. Attendees can learn about CEIPAL's complete talent management ecosystem which improves recruiting, increases new hires, and offers easy workforce management. Those who are not attending can request a personalized demo of CEIPAL here.

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Open source technology has been a driving factor in many of the most innovative developments of the digital age, so it should come as no surprise that it has made its way into artificial intelligence as well. But with trust in AI's impact on the world still uncertain, the idea that open source tools, libraries, and communities are creating AI projects in the usual wild west fashion is creating yet more unease among some observers. Open source supporters, of course, reject these fears, arguing that there is just as little oversight into the corporate-dominated activities of closed platforms. In fact, open source can be more readily tracked and monitored because it is, well, open for all to see. And this leaves us with the same question that has bedeviled technology advances through the ages: Is it better to let these powerful tools grow and evolve as they will, or should we try to control them?

As a cybersecurity data scientist, hacker, researcher, and assistant professor, Christian Camilo Urcuqui López has been working on cybersecurity, data science, and e-health. He has worked for the software industry and his experiences include different projects from public and private institutions participating as a software engineer, researcher, director of IT, and nowadays as a data scientist. He is interested in malware (anomaly) detection, adversarial techniques, secure learning, privacy, threat hunting, responsible AI, and ethical hacking. He is the author of the book "Ciberseguridad: un enfoque desde la ciencia de datos", published by Editorial ICESI in 2019. Nowadays Urcuqui is Data Scientist at Globant (an IT and Software Development company). Interested in how a cybercriminal thinks and on security AI, the author's idea on the topic started when he was reading the book "The art of the war" by Sun Tzu, a well-known publication about the Chinese military techniques and tactics.

Research guided by a plant pathologist in Penn State's College of Agricultural Sciences suggests that machine-learning algorithms that are programmed to recognize changing weather patterns could show producers and agricultural managers how to increase soybean and corn yields in the United States. The approach could prove valuable in addressing climate change realities that have presented challenges in growing enough food for a rising global population, noted Paul Esker, associate professor of epidemiology and field crop pathology. "Soybean and corn are among the most valuable crops in terms of food supply and economic output in the U.S. agricultural sector," said Esker, who pointed to U.S. Department of Agriculture statistics that place corn as the most widely produced crop in the U.S., with soybean following close behind. Not only are these crops vital to food security in the U.S. and beyond, but their combined total value to the nation's economy is more than $100 billion. While Esker acknowledges that is an impressive figure, he points out that many scientists predict that that by 2050, the world must feed 9 billion people, so current outputs must increase.

US and EU officials on Wednesday pledged to join forces to deal with a host of technology and trade issues to secure semiconductor supplies and counter China's dominance. The inaugural meeting of the Trade and Technology Council (TTC) laid out a lengthy to-do list, but perhaps the most significant achievement was the symbolic restoration of good relations after the damage suffered under the administration of former president Donald Trump. The high-level meetings were held as industries worldwide grapple with shortages of crucial semiconductors that are harming manufacturing, including of autos, and pushing prices higher. But the summit also set its sights on forced labor, artificial intelligence, digital privacy and protecting human rights activists online, as well as monitoring foreign investment in key sectors and controlling exports of sensitive products. "We intend to collaborate to promote shared economic growth that benefits workers on both sides of the Atlantic, (and) grow the transatlantic trade and investment relationship," the officials said in their final communique.

The crucial component making smart technologies possible – from something as small as a ring to as large as an entire city – is the IoT. Although there are varying definitions, the term IoT is mainly used for previously'dumb' devices that didn't have an Internet connection, but that now communicate with the network independently of human action. For this reason, a smartphone isn't explicitly defined as an IoT device – although it's crammed with sensors. A connected refrigerator or microwave oven however is. Nowadays, these smart technology devices devices include billions of objects of all shapes and sizes – coffee machines, lightbulbs, driver-less trucks, wearable fitness devices, jet engines and children's smart toys – all equipped with sensors and communicating data through the Internet.