It is true that artificial intelligence (AI) will come to influence almost every aspect of our lives. In the scramble to realize the potential economic and societal benefits promised by AI, the ready availability of massive, complex, and assumed-to-be generalizable datasets with which to train and test new algorithms is vital. The interaction of governments with their citizens throughout their lives generates huge volumes of diverse information, and these continuously expanding repositories of data are now seen as a public good, providing the raw material for AI industries. In passing the National Artificial Intelligence Initiative Act of 2020 (NAIIA), the United States has adopted a path similar to that of the European Union, as defined within the European Commission's Coordinated Plan on Artificial Intelligence 2021 Review. Under the provisions of the NAIIA, the National Artificial Intelligence Research Resource Task Force (NAIRRTF) has been constituted to make recommendations to Congress on, among other things, the capabilities necessary to create shared computing infrastructure for use by AI researchers and potential solutions in respect to "barriers to the dissemination and use of high-quality government data sets."

Volcanologists can predict when volcanos are going to erupt if they have a full detail of its eruptions. But for potentially apocalyptic supervolcanoes, such as the one bubbling under Yellowstone National Park, it's nearly impossible, given how varied their known eruptions have been, according to a new study. Researchers at Cardiff University noted there is not a'single model' that can help scientists understand how eruptions from supervolcanoes happen, making it difficult to understand when they might occur in the future. The researchers looked at geochemical and petrological evidence of 13 supereruptions that have happened over the past 2 million years, including the most recent one, Taupō volcano in New Zealand, which happened more than 24,000 years ago. Experts said there is not a'single model' that can help them understand how eruptions from supervolcanoes happen There was no'single, unified mode' that showed how each of the 13 played out, with some starting gradually over a period of weeks to months, while others exploded suddenly and violently. The researchers also found that the eruptions lasted for varying times, some as short as a period of days or weeks, while others lasted decades.

Researchers at the U.S. Department of Energy's (DOE) Argonne National Laboratory have developed a new AI-based framework that can produce X-ray images in 3D. The team, which includes members from three divisions at Argonne, has developed a method to create 3D visualizations from X-ray data. Their efforts were meant to allow them to better use the Advanced Photon Source (APS) at their lab, but potential applications of this technology range from astronomy to electron microscopy. Lab tests showed that the new approach, called 3D-CDI-NN, can create 3D visualizations from data hundreds of times faster than existing technology. "In order to make full use of what the upgraded APS will be capable of, we have to reinvent data analytics. Our current methods are not enough to keep up. Machine learning can make full use and go beyond what is currently possible," says Mathew Cherukara of the Argonne National Laboratory, corresponding author of the paper.

If efforts by states and cities to pass privacy regulations curbing the use of facial recognition are anything to go by, you might fear the worst for the companies building the technology. But a recent influx of investor cash suggests the facial recognition startup sector is thriving, not suffering. Facial recognition is one of the most controversial and complex policy areas in play. The technology can be used to track where you go and what you do. It's used by public authorities and in private businesses like stores.

The average Jane is a tech-savvy individual. But, we can only work with the resources we are given. Online services for local governments are huge banks of information, making them particularly complex. This is a problem because the stakes are high – local citizens need access to vital information. But, many local governments have found a resolution in the form of artificial intelligence.

Despite the executive orders and congressional hearings of the " Biden antitrust revolution," the most profound anti-competitive shift is happening under policymakers' noses: the cornering of artificial intelligence and automation by a handful of tech companies. There is little doubt that the impact of AI will be widely felt. It is shaping product innovations, creating new research, discovery, and development pathways, and reinventing business models. AI is making inroads in the development of autonomous vehicles, which may eventually improve road safety, reduce urban congestion, and help drivers make better use of their time. AI recently predicted the molecular structure of almost every protein in the human body, and it helped develop and roll out a Covid vaccine in record time.

Jason Wang, technical director of the National Security Agency's Computer and Analytic Sciences Research Group, said he predicts the intelligence community will need artificial intelligence to protect U.S. networks in the future. Wang said at a virtual event on July 12th that intelligence community components need to pursue more partnerships to maximize capabilities against adversaries, according to an article published by NSA. "At the NSA, with most of our industry and academic counterparts, our journey started in this area of natural language processing and computer vision -- applying capabilities like machine transcription, machine translation … to our mission," he stated. Wang said NSA has been working to mature these foundational AI applications to support core missions, including the agency's cybersecurity triage mission.

The EU's forthcoming regulation on Artificial Intelligence could cost the bloc's economy up to €31 billion over the next 5 years and cause investments to shrink by as much as 20%, according to a study published on Monday (26 July). The assessment by the Centre for Data Innovation looked into the administrative costs of the Artificial Intelligence Act (AIA), a horizontal EU regulation to introduce increasing obligations based on the level of risk associated with the application of the groundbreaking technology. The study author stresses the administrative burden the new legislation is expected to create, which they say will disincentivise innovation and technology uptake. "The Commission has repeatedly asserted that the draft AI legislation will support growth and innovation in Europe's digital economy, but a realistic economic analysis suggests that argument is disingenuous at best," said senior policy analyst and report author Ben Mueller. That goal would require roughly 10 times the level of current investment in the technology, yet the study author says compliance costs would eat up just under 20% of those investments.

In this post, we summarise the final three invited talks from the International Conference on Machine Learning (ICML). These presentations covered: how machine learning can complement randomised controlled trials, encoding and decoding speech, and molecular science. Esther's work centres on the use of randomised controlled trials (RCT) and she runs policy experiments with the aim of understanding which policies work and which don't. Her work is particularly focussed on reducing poverty. Work of this type involves many causal questions, for which there are often many competing ideas. Such is the field that there is no real guidance for theory; experiments are needed to determine successful policies.

In collaboration with the United States Navy's Underwater Archaeology Branch, I taught a computer how to recognize shipwrecks on the ocean floor from scans taken by aircraft and ships on the surface. The computer model we created is 92% accurate in finding known shipwrecks. The project focused on the coasts of the mainland U.S. and Puerto Rico. It is now ready to be used to find unknown or unmapped shipwrecks. The first step in creating the shipwreck model was to teach the computer what a shipwreck looks like.