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Comment Re:They are asking the wrong question.... (Score 1) 41

The question is not "IF" the AI bubble is going to burst, but "WHEN". It's impossible to keep up these spending levels in the long term, and I am yet to see AI profits outpacing the spending. There are only two kinds of people who believe otherwise: people with zero knowledge of what LLMs really are, or CEOs of AI companies

It is plausible we're in a bubble. And there's some evidence for it. That companies can just add the words "AI" to something to get investment is a serious sign. But the revenue situation, while weird, is not by itself definitive. Anthropic even made a profit in Q2 this year https://aitoolsrecap.com/Blog/anthropic-first-profit-2026-revenue-breakdown . Now, there's enough weird accounting going on, and circularity within the various companies, that interpreting that as a definite, genuine profit is something I'd be hesitant to do, but from all the public information it does look genuine.

There are two, related issues. First, if a "bubble" keeps going on for long enough, it stops being a bubble because society just reorganizes around it. For example, there's a decent argument that the industry was in a bubble since the 1930s (or even earlier) but that we reorganized cities and infrastructure to support it. And then when there was finally, almost a hundred years later, a danger that that bubble was going to pop, as it really had deserved to decades before, we had the auto-bailtouts (which were not just in the US, but also included the UK, Russia, France, Germany, Italy and others). https://en.wikipedia.org/wiki/2008%E2%80%932010_automotive_industry_crisis. There's a related saying which generally comes up in the context of people thinking of short-selling: "Markets can be irrational longer than you can be solvent." Second, even when a bubble does burst, the tech itself may still grow or be highly useful. When the train bubble burst in 1873, it was devastating, but by most major metrics of the industry (amount of track laid down, number of stations, estimated number of people-miles traveled per year) numbers barely dipped or kept going up. Similarly, when the dot-com bubble burst some specific things did get really damaged (with some early fiber optic being just abandoned) but by and large, the internet continued to grow to the point where it ended up more ubiquitous than even boosters in the late 1990s would have predicted. So a bubble bursting doesn't mean that LLM based AI is goes away.

Comment Optimization processes (Score 5, Interesting) 19

n general, when there's an optimization process involved where one is minimizing stress or something else, hexagons often show up. In fact, hexagons are more often in nature than square patterns. One nice example is Giant's Causeway which has large hexagonal basalt columns https://en.wikipedia.org/wiki/Giant's_Causeway (although a few columns have seven or eight sides). But neat to see another example, on another world no less. (We have on big hexagon on Saturn already.)

Submission + - Innocent man jailed 18 months on basis of mistyped userid. (ctvnews.ca)

belmolis writes: A Nova Scotia man was falsely convicted of child pornography and spent 18 months in prison because Wisconsin police mistyped his Kik userid, which led them to his IP address. Nova Scotia police found nothing incriminating on his devices and no other evidence; he was convicted solely on the basis of the connection between the mistyped userid and his IP address.

Submission + - Drifting Falcon 9 upper stage heading for accidental collision with the Moon (space.com)

fahrbot-bot writes: Space and The Guardian are reporting that the Falcon 9 upper stage leftover from the launch of the Firefly Blue Ghost-1 lander on Jan. 15, 2025 is due to impact the Moon on Aug. 5, 2026.

Onboard the same flight was the Hakuto-R Mission 2, called Resilience, a robotic lunar lander developed by the Japanese company ispace.

According to a new study by an international team, the resulting impact plume may briefly be bright enough to see against the dark sky near the moon's edge. That means it might be visible to moongazers with sufficiently sensitive telescopes.

This head-on collision of the errant stage is expected to occur near the Einstein and Bell craters near the western lunar limb. It may well be visible to ground and space-based assets.

Submission + - FBI gets voter's IP address in new fraud probe tactic (axios.com)

alternative_right writes: The Trump administration has a new tactic for trying to isolate cases of alleged voter fraud â" digging into the IP addresses of those who went online to register to vote.

The FBI recently obtained the IP address of someone who registered online in South Carolina, according to documents first shared with Axios.

Submission + - COLDCARD flaw may have exposed Bitcoin wallet seeds for years (nerds.xyz)

BrianFagioli writes: A firmware flaw affecting COLDCARD hardware wallets may have caused some devices to generate predictable Bitcoin wallet seeds. Block researchers traced the issue to firmware that used a deterministic software generator instead of the STM32 processor hardware random number generator.

Coinkite says COLDCARD Mk3 devices that generated seeds while running firmware version 4.0.1 through 5.0.3 may be affected. Installing newer firmware does not repair an existing seed, so users must create a new seed on an unaffected device and transfer their Bitcoin. Block also questions whether newer COLDCARD models provide enough secure randomness, although Coinkite disputes that those devices are affected.

Submission + - Australia sues Telegram for terrorist content

An anonymous reader writes: Australia sues Telegram for terrorist content

“Australia has taken legal action against Telegram over alleged failures to tackle terrorism-related content. Just the day before, Russia launched its own case against the messaging platform and its founder, Russian-born entrepreneur Pavel Durov.”

Comment Seems like variant of a known problem (Score 1) 78

This seems like essentially a variant of the Waluigi effect hypothesized here https://www.lesswrong.com/posts/D7PumeYTDPfBTp3i7/the-waluigi-effect-mega-post which had the advantage of a pretty fun name for the situation, and is worth reading. . There's also some related prior work by Ball, Gluch, Goldwasser, Kreuter, Reingold, and Rothblum https://arxiv.org/abs/2507.07341 which suggested that fundamental information issues or computational complexity issues meant that making an LLM AI "safe" from jailbreaks by having a less computationally powerful/less intelligent AI system look over its input would always fail, since one could construct inputs that would be complicated tasks or essentially hidden puzzles that the less powerful AI would fail at but would expand out by the more powerful AI into instructions/jailbreaks. Ball et. al's work is also discussed in detail in this Quanta piece https://www.quantamagazine.org/cryptographers-show-that-ai-protections-will-always-have-holes-20251210/ There's also some similar work by Rao, Choudhury, and Aditya https://arxiv.org/abs/2406.12702 which I haven't read in detail but seems also thematically very similar.

Submission + - A fundamental flaw leaves LLMs strikingly vulnerable to attack (technologyreview.com)

joshuark writes: It is impossible to make large language models fully secure against hacks because of a fundamental flaw in how they work, a team of researchers argue in a paper presented at the International Conference on Machine Learning, a top AI conference, this month. The claim has huge implications for the safety of this technology.

By taking advantage of this flaw, which concerns how LLMs identify who or what is giving them instructions, the researchers were able to make popular LLMs spit out information they had been trained not to provide, such as how to synthesize cocaine and how to sabotage a commercial aircraft’s navigation system.

“There’s a real probability that this is going to be a problem that’s fundamentally unsolvable,” says Charles Ye, an independent researcher and coauthor of the ICML paper.

Companies will typically hire teams of human testers to try to come up with novel attacks that break existing guardrails, a process known as red-teaming. Model makers also use LLM super-hackers (such as OpenAI’s GPT-Red) that find and exploit weaknesses in other models to automate parts of this process. The goal is then to take those attacks and train a new model to resist them and anything that looks like them.

The problem, says Jasmine Cui, another independent researcher and coauthor of the paper, is that the approach amounts to giving the models a list of things they shouldn’t do. But no list is exhaustive. “It’s like watching The Simpsons and they have Bart writing ‘I will not say something inappropriate to my teacher’ a hundred times,” she says. “And he still does things that are pretty crass anyway.”

The ICML paper describes attacks against several of OpenAI’s models, but Cui and Ye say that they have since seen similar results with models made by Anthropic, Alibaba, and DeepSeek.

Cui and her colleagues wanted to find out why an attack like chain-of-thought forgery was so effective. They suspected it had something to do with the mechanism that LLMs use to keep track of where their instructions are coming from.

But what Cui and her colleagues discovered is that LLMs are in fact very bad at keeping track of different roles. In a series of experiments that looked at what was going on inside a handful of different models, the researchers found that LLMs seem to identify the role of a specific chunk of text not by the tags around it but by the style of that text and the words it contains.

The upshot, the researchers claim, is that all an attacker needs to do to hack an LLM is write text that spoofs a certain role. And because roles are a fundamental part of how LLMs work, no amount of training will fully solve the problem.

Ye is worried that nobody is ready for what’s coming. “There’s going to be a huge economic incentive for people to do jailbreaks and prompt injections,” he says. The best defense could be to expect the worst. Organizations shouldn’t trust LLMs, and they should expect that anything done by agents could be unsafe, he says: “That’s not a great solution, but it just might be what we have to do.”

“It’s really incredible that these things are being deployed everywhere to control super-critical systems,” he adds. “There’s been no study of the fundamental science here. We’re all doing it ad hoc.”

Comment Re:Stupid story (Score 1) 67

It isn't "Gospel" but given the highly detailed timeline, and given that Hugging Face has said explicitly that after OpenAI cooperated with them they are confident that's what happened, it looks like the most likely hypothesis. Alternatives involve OpenAI hacking into multiple other companies in a highly illegal way for extremely unclear gains. And again, Hugging has more details than anyone else, and they are confident that that that happened.

Comment Re:Stupid story (Score 1) 67

Whether you call it thinking or merely prompting is besides the point. The AI was "prompted" if you prefer to accomplish a specific task, performing well on a benchmark. It then responded by escaping a sandbox and hacking Hugging Face. Whether you label it as thinking or merely responding to a prompt, it should still be alarming, in that highly unexpected and genuinely dangerous behavior can occur simply due to being prompted to accomplish a goal. This is exactly the point that people like Yudkowsky and Bostrom were making years ago before any of the LLMs, and people dismissed it as groundless, and said that AIs would not do things like that. Turns out, empirically, they do.

Submission + - Google Brings Its Age-Assurance Tech to Android Developers Worldwide (techcrunch.com)

An anonymous reader writes: Google is expanding its answer to Apple’s age-assurance tools with Wednesday’s news that it will bring its Play Signal API to users worldwide by the end of 2026. The technology, already available in Brazil, allows Android developers to identify younger users of their apps in order to provide safer, age-appropriate experiences. The expansion will initially bring the API to Australia and Canada by mid-August, before rolling out globally to all markets by the end of the year.

[...] Like Apple, Google’s technology allows developers to obtain a user’s age range without needing to access personal information, like their date of birth. Instead, it enables parents to share their child’s age range directly with apps. It also lets adults share their age when prompted by app developers as well, allowing for customized experiences. Parents won’t have to manage sharing this information on an app-by-app basis, either. To make it easier, Google centralizes these controls inside its parental controls dashboard, Family Link. Once entered, any developer that chooses to incorporate age-range information can access this signal to customize their apps accordingly.

Google notes, however, that the age ranges are not shared by default — parents must opt in by entering that information. The feature joins other safety tools on Google Play, including those that let developers restrict a child’s ability to discover their apps. Parents, meanwhile, can continue to use Google Play’s Family Link app to manage their child’s screen-time limits, approve app downloads, or set PIN-based content filters for specific apps.

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