"Illinois last week banned the use of artificial intelligence in mental health therapy," reports the Washington Post, "joining a small group of states regulating the emerging use of AI-powered chatbots for emotional support and advice." Licensed therapists in Illinois are now forbidden from using AI to make treatment decisions or communicate with clients, though they can still use AI for administrative tasks. Companies are also not allowed to offer AI-powered therapy services — or advertise chatbots as therapy tools — without the involvement of a licensed professional. Nevada passed a similar set of restrictions on AI companies offering therapy services in June, while Utah also tightened regulations for AI use in mental health in May but stopped short of banning the use of AI. The bans come as experts have raised alarms about the potential dangers of therapy with AI chatbots that haven't been reviewed by regulators for safety and effectiveness. Already, cases have emerged of chatbots engaging in harmful conversations with vulnerable people — and of users revealing personal information to chatbots without realizing their conversations were not private. Some AI and psychiatry experts said they welcomed legislation to limit the use of an unpredictable technology in a delicate, human-centric field.

Read more of this story at Slashdot.

"The planet Uranus emits more heat than it gets from the Sun," reports Science Daily , citing a new study led by University of Houston researchers, in collaboration with planetary scientists worldwide. "This means it's still slowly losing leftover heat from its early history," says the first author on the paper, "a key piece of the puzzle that helps us understand its origins and how it has changed over time." The study found the planet emitting about 12.5% more heat than it absorbs via sunlight, which "suggests Uranus does have its own internal heat — an advance that not only informs NASA's future missions but also deepens scientists' understanding of planetary systems, including processes that influence Earth's climate and atmospheric evolution." The discovery resolves a long-standing scientific mystery about the giant planet, because observational analyses from Voyager 2 in 1986 didn't suggest the presence of significant internal heat — contradicting scientists' understanding of how giant planets form and evolve... Additionally, the team's methodology provides testable theories and models that could also be applied to explore radiant energy of other planets within and beyond our solar system... It could even impact technology innovation and climate understanding on Earth [giving insights intoi "the fundamental processes that shape planetary atmospheres, weather systems and climate systems," said one of the paper's authors.] The article adds that the researchers now think the planet "may have a different interior structure or evolutionary history compared to the other giant planets."

Read more of this story at Slashdot.

"Several cybersecurity companies debuted advancements in AI agents at the Black Hat conference last week," reports Axios, "signaling that cyber defenders could soon have the tools to catch up to adversarial hackers." - Microsoft shared details about a prototype for a new agent that can automatically detect malware — although it's able to detect only 24% of malicious files as of now. - Trend Micro released new AI-driven "digital twin" capabilities that let companies simulate real-world cyber threats in a safe environment walled off from their actual systems. - Several companies and research teams also publicly released open-source tools that can automatically identify and patch vulnerabilities as part of the government-backed AI Cyber Challenge. Yes, but: Threat actors are now using those AI-enabled tools to speed up reconnaissance and dream up brand-new attack vectors for targeting each company, John Watters, CEO of iCounter and a former Mandiant executive, told Axios. The article notes "two competing narratives about how AI is transforming the threat landscape." One says defenders still have the upper hand. Cybercriminals lack the money and computing resources to build out AI-powered tools, and large language models have clear limitations in their ability to carry out offensive strikes. This leaves defenders with time to tap AI's potential for themselves. [In a DEF CON presentation a member of Anthropic's red team said its Claude AI model will "soon" be able to perform at the level of a senior security researcher, the article notes later] Then there's the darker view. Cybercriminals are already leaning on open-source LLMs to build tools that can scan internet-connected devices to see if they have vulnerabilities, discover zero-day bugs, and write malware. They're only going to get better, and quickly... Right now, models aren't the best at making human-like judgments, such as recognizing when legitimate tools are being abused for malicious purposes. And running a series of AI agents will require cybercriminals and nation-states to have enough resources to pay the cloud bills they rack up, Michael Sikorski, CTO of Palo Alto Networks' Unit 42 threat research team, told Axios. But LLMs are improving rapidly. Sikorski predicts that malicious hackers will use a victim organization's own AI agents to launch an attack after breaking into their infrastructure.

Read more of this story at Slashdot.

Matt Asay answered questions from Slashdot readers in 2010 as the then-COO of Canonical. Today he runs developer marketing at Oracle (after holding similar positions at AWS, Adobe, and MongoDB). And this week Asay contributed an opinion piece to InfoWorld reminding us of open source contributions from companies where "enlightened self-interest underwrites the boring but vital work — CI hardware, security audits, long-term maintenance — that grassroots volunteers struggle to fund." [I]f you look at the Linux 6.15 kernel contributor list (as just one example), the top contributor, as measured by change sets, is Intel... Another example: Take the last year of contributions to Kubernetes. Google (of course), Red Hat, Microsoft, VMware, and AWS all headline the list. Not because it's sexy, but because they make billions of dollars selling Kubernetes services... Some companies (including mine) sell proprietary software, and so it's easy to mentally bucket these vendors with license fees or closed cloud services. That bias makes it easy to ignore empirical contribution data, which indicates open source contributions on a grand scale. Asay notes Oracle's many contributions to Linux: In the [Linux kernel] 6.1 release cycle, Oracle emerged as the top contributor by lines of code changed across the entire kernel... [I]t's Oracle that patches memory-management structures and shepherds block-device drivers for the Linux we all use. Oracle's kernel work isn't a one-off either. A few releases earlier, the company topped the "core of the kernel" leaderboard in 5.18, and it hasn't slowed down since, helping land the Maple Tree data structure and other performance boosters. Those patches power Oracle Cloud Infrastructure (OCI), of course, but they also speed up Ubuntu on your old ThinkPad. Self-interested contributions? Absolutely. Public benefit? Equally absolute. This isn't just an Oracle thing. When we widen the lens beyond Oracle, the pattern holds. In 2023, I wrote about Amazon's "quiet open source revolution," showing how AWS was suddenly everywhere in GitHub commit logs despite the company's earlier reticence. (Disclosure: I used to run AWS' open source strategy and marketing team.) Back in 2017, I argued that cloud vendors were open sourcing code as on-ramps to proprietary services rather than end-products. Both observations remain true, but they miss a larger point: Motives aside, the code flows and the community benefits. If you care about outcomes, the motives don't really matter. Or maybe they do: It's far more sustainable to have companies contributing because it helps them deliver revenue than to contribute out of charity. The former is durable; the latter is not. There's another practical consideration: scale. "Large vendors wield resources that community projects can't match." Asay closes by urging readers to "Follow the commits" and "embrace mixed motives... the point isn't sainthood; it's sustainable, shared innovation. Every company (and really every developer) contributes out of some form of self-interest. That's the rule, not the exception. Embrace it." Going forward, we should expect to see even more counterintuitive contributor lists. Generative AI is turbocharging code generation, but someone still has to integrate those patches, write tests, and shepherd them upstream. The companies with the most to lose from brittle infrastructure — cloud providers, database vendors, silicon makers — will foot the bill. If history is a guide, they'll do so quietly.

Read more of this story at Slashdot.

Slashdot reader darwinmac writes: Volkswagen is offering a subscription model for extra horsepower on its ID.3 electric cars. Want to bump your ride from the standard 201 bhp to the full 228 bhp? That will be about £16.50 per month or £165 per year, or a one-time £649 "lifetime" fee that is tied to the car, not you. If you sell it, you have to pay again. VW defended this to the BBC by saying you are basically paying for a sportier experience without buying a higher powered model upfront, calling it "nothing new." Nothing changes mechanically. You are just paying VW to essentially flip a boolean somewhere in the car's software.

Read more of this story at Slashdot.