Longtime Slashdot reader schwit1 shares an opinion piece from the Washington Post, written by Virginia Governor Abigail Spanberger: NextEra Energy, a Florida-based utility company, has filed paperwork to buy Dominion Energy for about $67 billion, creating the largest regulated electric utility in the world. Virginia's State Corporation Commission is the regulatory body tasked with reviewing the application, and the SCC's commissioners will ultimately decide whether to approve, deny or impose new conditions on any potential merger.
As a Virginian, I am deeply skeptical about whether selling our primary state-regulated utility to an out-of-state company is good for the commonwealth. I have serious questions about what this deal would mean for us. And as governor, I intend to get answers and be a voice for Virginians in the process. That is why I will be taking the legal step of "intervening" in this proposed merger, which means that as governor, I will formally request to be a party to the case.
I know this action is unprecedented by a Virginia governor -- but so, too, is the size of this proposed merger and its potential impact on the commonwealth. Virginians deserve to know that their leaders are laser-focused on ensuring that their needs are part of the SCC review. "If two large corporations stand to benefit financially from this merger, so, too, should the Virginians who pay the bills," said Spanberger. "That is why any potential deal must deliver a more affordable energy bill with sustained, long-term energy cost savings."
By formally intervening in the merger review, Spanberger's administration would gain legal standing to participate directly in the case, request detailed information, raise concerns, and advocate for conditions that benefit people in the state. She says the goal is to push for lower long-term energy costs, protect utility jobs, and ensure any new owner continues investing in reliable, affordable and cleaner power.
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An anonymous reader quotes a report from NPR: Billions of dollars are traded every week on the lightly regulated prediction market sites, where users bet on everything from movie reviews to elections to conflicts in the Middle East. Clinical trials are just the latest area where the industry's rapid growth is raising ethical questions. Kalshi claims such bets will provide a new source of information about which drugs will get approved, and what clinical trials will show promising results, which the company says can help investors decide what new drugs to fund.
"If you want to ban profiting from the failure of clinical trials, you would start with the stock market, where the financial incentive for this type of profit is orders of magnitude larger," said Kalshi spokesman Jack Such, pointing to stock market short sellers who have profited from clinical trial failures. "While Kalshi and the stock market are the same in this regard, they do differ in one important way: the stock market doesn't give any valuable information to researchers," Such said.
Drug trial researchers, though, are far from convinced. David Tsai, who runs clinical trials at a biotech company in the San Francisco Bay Area, started an online petition pushing for such betting to be banned, making the case that betting on drug trials "threatens the very foundation of trust and integrity in biotechnology." Tsai is concerned that the prospect of betting provides those involved with a clinical trial a reason to tamper with the results for a prediction market payout. "If we were running a trial for an oncology drug that requires an infusion, a pharmacist who had placed a bet saying that it's gonna work well, or doesn't work well, could obviously adjust the infusion rate, could adjust the source temperature of the drug," he said. "They could change any number of variables that could obviously have a direct impact [on] how the trial and the data and the patient safety would come out."
Another skeptic is Nicholas Zaorsky, a professor of radiation oncology at the Mayo Clinic in Jacksonville, Fla., who has helped run clinical trials and agrees that prediction markets can interfere with the advancement of life-saving drugs. "Prediction markets can be valuable in some settings because they aggregate information, but clinical trials are fundamentally different: investigators, coordinators, and sometimes even participants can directly influence aspects of the outcomes being wagered on," Zaorsky said. "That creates financial incentives that risk undermining trial integrity." Bettors should not be rooting for an experimental medicine to fail just to earn a buck, says Joshua Pederson, the father of a 12-year-old cancer patient enrolled in a clinical trial. "It's a dark idea," he said. "It's quite ghastly."
Kalshi, for its part, argues that its prediction markets could help patients track promising medical breakthroughs and clinical trials, enlisting experts including 23andMe founder Anne Wojcicki to make the case.
"Most patients don't know about the choices available in clinical trials or which programs are most promising. The opportunity to have an open, transparent dataset about trial probabilities is extremely promising and empowering for people," a white paper sponsored by Kalshi stated.
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Former U.S. National Cyber Director Chris Inglis says the biggest AI risk isn't sentience but autonomy. "What I'm worried about is that they get to choose what and where they do something, and under what rules they do it," he said, citing recent cases of AI agents from OpenAI, Anthropic, and Meta escaping security sandboxes. He argues developers need stronger safeguards, monitoring, and human accountability, invoking Asimov's idea that protecting humans should come before simply obeying them. The Register reports: "Asimov was right," he said, referring to science fiction author Isaac Asimov and his three laws that were to be followed by robots -- more specifically, AIs, in this case. "The first rule, and we call it the superior role, must be that it's designed not to hurt humans," Inglis said. "Second rule: To obey humans, such that it doesn't achieve agency and aspiration on its own. And the third: To do what humans tell it - and in that order. Instead we've designed them in the exact opposite way."
What this means, he explained, is that AI developers created models to "do what humans tell you, obey the humans until it's inconvenient, and then the third one is maybe implied - protect humans - but if that's not built into the DNA, hardwired into it, then we have no right to expect it." Inglis admits it's not possible to hardwire rules into models and still keep their non-deterministic nature. "I would offer that you can tease those out in a highly controlled environment, a true sandbox, where you say, 'Let's put this thing through its paces, and let's back away to see what happens,'" he said. "Maybe you get the equivalent of a mini nuclear explosion in that room, and now you know this thing is capable of that."
Inglis thinks another problem with AI is that it's become a commodity. "It's not like you can control it like you can nuclear material," he said. "You can't even specify its properties the way you can for an airplane or for an automobile, as diverse as they might be. Its manifestations are so numerous, so diverse, that as a general matter, you can't actually win by simply saying, "I will design those properties in,'" he added. "You need to do that to some degree, and then make sure that you understand how to watch it, monitor it, make sure you know what it does."
[...] Ultimately, humans remain accountable for AI models' actions, according to Inglis. "They remain the source of agency and aspiration. It's possible for them to give broad authority to an AI model and have it run around for 30 hours without further consultation, but they need to know what they've asked it to do, and they need to know what they expect it will deliver in terms of performance on the back end. If they don't, then they're going to get what they deserve, which is the very frequent unpleasant surprise."
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Security updates have been issued by AlmaLinux (compat-libtiff3, fence-agents, firefox, freerdp, frr, gimp, gstreamer1-plugins-bad-free, java-25-openjdk, kernel, kernel-rt, ldns, libgcrypt, libXfont2, nodejs:22, nodejs:24, p11-kit, pipewire, resource-agents, sg3_utils, thunderbird, and yelp), Debian (async-http-client, jq, kernel, linux-6.1, linux-6.12, redis, and udisks2), Fedora (abrt, chromium, coreutils, curl, freeipa, gst-devtools, gst-editing-services, gstreamer1, gstreamer1-doc, gstreamer1-plugin-libav, gstreamer1-plugins-bad-free, gstreamer1-plugins-base, gstreamer1-plugins-good, gstreamer1-plugins-ugly-free, gstreamer1-rtsp-server, ImageMagick, kernel, libXfont2, php, python-gstreamer1, samba, tcpreplay, and trafficserver), Mageia (firefox, nss, rootcerts, python-django, and thunderbird), Oracle (freerdp, gimp, gpsd, kernel, kernel-uek, and osbuild-composer), Red Hat (buildah and container-tools:rhel8), Slackware (libXfont2 and p11-kit), and SUSE (amazon-ecs-init, azure-storage-azcopy, bind, bouncycastle, cockpit-repos, cockpit-subscriptions, dnsdist, ffmpeg-4, hawk-apiserver, nodejs22, nodejs24, OpenImageIO, openssl-1_1, openssl-3, perl-Mojo-JWT, php8, rsyslog, sssd, and wireshark).
Researchers in China have developed a living mycelium textile that can self-clean, renew its surface, and partially repair holes when treated with a nutrient solution and fresh fungus. The material can also gain added properties such as blue pigmentation or UV resistance by co-culturing it with yeast or other fungi. Dezeen reports: The breakthrough from the researchers at the Shenzhen Institutes of Advanced Technology is a type of engineered living material (ELM) -- a material built off living organisms that stay active even after they're fabricated into their final form. [...] By working with living but dormant cordyceps militaris fungus instead, the researchers have been able to take advantage of its biological functions. The result is a material that is self-renewing and responsive to its environment, in ways that could one day transform architecture and clothing -- as seen in a prototype dress created together with material innovation company Peelshere.
It can also be adapted by mixing in other fungi or yeast, lead researcher Ke Li and her team detail in a paper in the peer-reviewed journal Science Advances. In it, they describe a "programmable fungal platform" where mycelium is treated like a modular system, with the sheet material forming a base structure and extra biological abilities, such as colour and UV resistance, becoming "plug-and-play" add-ons via other organisms. This gets their textile closer to the self-repair, environmental responsiveness and controllable functionality that is the promise of engineered living materials, they argue.
The ELM's self-renewing and semi-repairing functionality comes from the mycelium base structure. Following drying at 45 degrees, the material is not quite living and not quite dead, but instead in a "low-metabolic, dormant-like state", Li told Dezeen, meaning it is not actively growing. However, new growth can be triggered by applying a nutrient solution of potato water, leading the dormant mycelium to germinate, send out new fungal filaments and renew the material's surface. When this nutrient solution is applied over a hole, along with a small patch of fresh fungus, it triggers the living cells to grow across the gap, seamlessly repairing the surface without any adhesives or stitching. The material is also naturally self-cleaning, as it is hydrophobic. "Its distinctive surface texture, biological colouring, controlled repair and biodegradability may be particularly useful in applications where visual expression and a defined product lifetime are important," said Li.
"Further improvements in durability, moisture resistance, safety and manufacturing consistency would be needed before it could be considered for routine clothing or permanent architectural use."
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An anonymous reader quotes a report from The Guardian: Scientists have made the first viruses designed by artificial intelligence in a milestone that raises hopes for new medicines but also concerns over how to ensure the technology remains safe. The viruses are specific kinds known as bacteriophages, which only infect bacteria and are used around the world to treat patients with persistent infections. In lab tests, a cocktail of the AI-designed viruses killed E coli bugs that were resistant to natural bacteriophages.
Dr Brian Hie, a chemical engineer at Stanford University in California, used genome language models, the genetic equivalent of the large language models behind AI chatbots, to design functioning genomes for bacteriophages. The viruses were then made in the laboratory and pitted against E coli in a dish. The ability to "rapidly design" genomes and tune them for specific bugs while overcoming resistance could "transform phage therapy" and "expand biotechnological toolkits," the researchers wrote in the journal Science.
But beyond the potential benefits, the scientists said the work raised "important biosafety, biocontainment and biosecurity considerations" and urged others who were designing whole genomes to "consult both safety and security professionals throughout the project." In an accompanying article, Prof Tom Inglesby and Dr Moritz Hanke at the Center for Health Security at Johns Hopkins University in Baltimore, reinforced the warning, writing: "Although this is promising for life sciences applications, it also raises urgent biosafety and biosecurity questions. The ability to compose viral genomes using generative AI now exists; the governance to safely steer it does not." Tom Ellis, a professor of synthetic genome engineering at Imperial College London, said the work was impressive, but revealed how hard it would be to make more complex genomes. "This is literally the smallest and easiest genome to make," he said. An AI trained on the genetic code of dangerous bugs could be used to design more harmful viruses, Ellis said, but controlling access to genetic data and having restrictions on making genomes that look dangerous would help. "Governments are working hard to do this already," he added. "But honestly," he said, "the threat from full AI design and writing of a genome of a virus or bacteria is very overblown when we consider that just taking existing pathogens and making gain-of-function changes to their genomes is so much easier and much more likely to be a real pathogenic threat."
Dr Filippa Lentzos, a reader in science and international security at King's College London, said the most important point to intervene at the moment was when DNA was being manufactured. "It's important to see the bigger governance picture and not focus regulation solely on the AI model," she said. "A layered approach makes more sense: safeguards around model development and access, responsible research review, synthesis screening, and established laboratory biosafety and biosecurity."
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