Imagine earning your engineering degree in 50 minutes? Sandia National Laboratories has developed a new machine-learning algorithm capable of performing simulations for materials scientists nearly 40,000 times faster than normal, according to a Sandia press release. Their results, published in the January issue of a journal called npj Computational Materials, could herald a dramatic acceleration in the development of new technologies for optics, aerospace, energy storage, and potentially medicine while simultaneously saving laboratories money on computing costs, according to the study. The research, funded by the U.S. Department of Energy's Basic Energy Sciences program, was conducted at the Center for Integrated Nanotechnologies, a Department of Energy user research facility jointly operated by Sandia and Los Alamos national labs. Sandia researchers used machine learning to accelerate a computer simulation that predicts how changing a design or fabrication process, such as tweaking the amounts of metals in an alloy, will affect a material.

Machine learning models $-$ now commonly developed to screen, diagnose, or predict health conditions $-$ are evaluated with a variety of performance metrics. An important first step in assessing the practical utility of a model is to evaluate its average performance over an entire population of interest. In many settings, it is also critical that the model makes good predictions within predefined subpopulations. For instance, showing that a model is fair or equitable requires evaluating the model's performance in different demographic subgroups. However, subpopulation performance metrics are typically computed using only data from that subgroup, resulting in higher variance estimates for smaller groups. We devise a procedure to measure subpopulation performance that can be more sample-efficient than the typical subsample estimates. We propose using an evaluation model $-$ a model that describes the conditional distribution of the predictive model score $-$ to form model-based metric (MBM) estimates. Our procedure incorporates model checking and validation, and we propose a computationally efficient approximation of the traditional nonparametric bootstrap to form confidence intervals. We evaluate MBMs on two main tasks: a semi-synthetic setting where ground truth metrics are available and a real-world hospital readmission prediction task. We find that MBMs consistently produce more accurate and lower variance estimates of model performance for small subpopulations.

The unprecedented interest, investment, and deployment of machine learning across many aspects of our lives in the past decade has come with a cost. Although there has been some movement towards moderating machine learning where it has been genuinely harmful, it's becoming increasingly clear that existing approaches suffer significant shortcomings. Nevertheless, there still exist new directions that hold potential for meaningfully addressing the harms of machine learning. In particular, new approaches to licensing the code and models that underlie these systems have the potential to create a meaningful impact on how they affect our world. This is Part I of a two-part essay.

Netherlands politicians just got a first-hand lesson about the dangers of deepfake videos. According to NL Times and De Volkskrant, the Dutch parliament's foreign affairs committee was fooled into holding a video call with someone using deepfake tech to impersonate Leonid Volkov (above), Russian opposition leader Alexei Navalny's chief of staff. The perpetrator hasn't been named, but this wouldn't be the first incident. The same impostor had conversations with Latvian and Ukranian politicians, and approached political figures in Estonia, Lithuania and the UK. The country's House of Representatives said in a statement that it was "indignant" about the deepfake chat and was looking into ways it could prevent such incidents going forward.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Syria's oil ministry said a fire has erupted in a tanker on its coast after what it said was a suspected drone attack on Saturday. The official state news agency said the fire in the oil tanker outside Baniyas refinery has been extinguished. The oil ministry said the fire started after a suspected drone attack that originated from the Lebanese territorial waters. It provided no further details and did not specify where the tanker was arriving from.

SpaceX successfully launches NASA astronauts from Kennedy Space Center into space. The International Space Station's population swelled to 11 on Saturday with the jubilant arrival of SpaceX's third crew capsule in less than a year. All of the astronauts -- representing the U.S., Russia, Japan and France -- managed to squeeze into camera view for a congratulatory call from the leaders of their space agencies. This image provided by NASA, astronauts from SpaceX join the astronauts of the International Space Station for an interview on Saturday, April 24, 2021. A recycled SpaceX capsule carrying four astronauts has arrived at the International Space Station, a day after launching from Florida.

If there's one thing the United States military gets right, it's lethality. Yet even once the US military has you in its sights, it may not know who you actually are -- such are, these so-called "signature strikes" -- even as that wrathful finger of God is called down from upon on high. As Kate Crawford, Microsoft Research principal and co-founder of the AI Now Institute at NYU, lays out in this fascinating excerpt from her new book, Atlas of AI, the military-industrial complex is alive and well and now leveraging metadata surveillance scores derived by IBM to decide which home/commute/gender reveal party to drone strike next. And if you think that same insidious technology isn't already trickling down to infest the domestic economy, I have a credit score to sell you. Excerpted from Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence by Kate Crawford, published by Yale University Press.

Last September, Nvidia, the American manufacturer of graphics processing chips, and the Japanese company SoftBank announced an agreement under which Nvidia would acquire the British chip designer Arm from SoftBank for $40bn. Since SoftBank had acquired Arm in 2016 for $32bn, you could say that a 25% profit on a five-year investment isn't to be sneezed at, especially if industry mutterings about SoftBank's crackpot investment strategy and Arm's internal difficulties with its China-based operation are to be believed. But even if one were foolish enough to sympathise with SoftBank's desire to climb out of the hole it had dug for itself, the idea that Arm should be sold to a US chip manufacturer is so daft that even Boris Johnson's administration had begun to smell a rat. And so on Monday it announced that the secretary of state for digital, culture, media and sport was "intervening in the sale on national security grounds", based on advice received "from officials across the investment security community". To which decision the only possible response is: what took him so long?

These advances will create a network where almost every device can be simultaneously connected, enabling technologies not possible today. Governments and private entities are just beginning to invest in the technology, and projections suggest commercial availability around 2030. But given 6G's anticipated ubiquity and potential to change the landscape, we would be wise to begin learning about it now. Artificial intelligence ("AI") represents a new frontier in the global economy: Some estimates say it could contribute up to $15.7 trillion worldwide by 2030. Increases in computing power and innovations in computer science have fueled AI innovation.

Security leaders are increasingly turning to AI and ML-based defenses against cyberattacks as pessimism grows over the efficacy of human-based cybersecurity defense efforts. A recent survey from MIT Technology Review Insights, sponsored by Darktrace, found more than half of business leaders think security strategies based on human-led responses to fast-moving attacks are failing; nearly all have begun to bolster their defenses in preparation for AI-enabled attacks. "Cyber AI autonomously stops threats in their tracks and surfaces relevant information in a digestible narrative, augmenting human teams and giving them time to focus on strategic tasks that matter," said Darktrace's director of threat hunting, Max Heinemeyer. "All that organizations can do to prepare is simply embrace self-learning AI as a force multiplier." He noted that AI-powered cybersecurity platforms can integrate with other tools in a security toolbox, ingest new forms of telemetry from existing investments for further enrichment, share detections and incidents with workflow tools and even orchestrate response actions across the rest of the digital estate, for example, by integrating with preventative tools.