This is an ad for something CT-scan-related, but it contains a good breakdown of how an old car cigarette lighter works. And it has a couple interactive CT Scan explorers past the video.

More than 500 developers at Blizzard Entertainment who work on World of Warcraft have voted to form a union. The World of Warcraft GameMakers Guild, formed with the assistance of the Communication Workers of America (CWA), is composed of employees across every department, including designers, engineers, artists, producers, and more. Together, they have formed the largest wall-to-wall union — or a union inclusive of multiple departments and disciplines — at Microsoft.

This news comes less than a week after the formation of the Bethesda Game Studios union, which, at the time of the announcement, was itself the largest wall-to-wall Microsoft union.

The World of Warcraft GameMakers Guild is made up of over 500 members across Blizzard offices in California and Massachusetts. Despite its size — it is the second largest union at Microsoft overall behind Activision’s 600-member QA union — Cox said that Microsoft’s labor neutrality agreement helped get the organization ball rolling.

Interdisciplinary unions like Blizzard’s are relatively rare, especially in the video game industry, as each department has different needs and goals. Up until this point, the majority of unionization efforts in the video game industry have been spearheaded by QA workers who have traditionally been among the least paid and least protected employees.

The first union at Activision Blizzard was made up of QA workers, as was the first union at Microsoft’s game studios. In addition to the World of Warcraft GameMakers Guild, yet another new union made up of QA workers at Blizzard’s Austin, Texas, office has also formed. The Blizzard Quality Assurance United-CWA is a 60-person unit including QA testers for games like Diablo, Hearthstone, and other Blizzard games.

Switzerland has recently enacted a law requiring its government to use open-source software (OSS) and disclose the source code of any software developed by or for the public sector. According to ZDNet, this “public body, public code” approach makes government operations more transparent while increasing security and efficiency. Such a move would likely fail in the U.S. but is becoming increasingly common throughout Europe.

According to Switzerland’s new “Federal Law on the Use of Electronic Means for the Fulfillment of Government Tasks” (EMBAG), government agencies must use open-source software throughout the public sector.

The new law allows the codifies allowing Switzerland to release its software under OSS licenses. Not just that; it requires the source code be released that way “unless the rights of third parties or security-related reasons would exclude or restrict this.”

In addition to mandating the OSS code, EMBAG also requires Swiss government agencies to release non-personal and non-security-sensitive government data to the public. Calling this Open Government Data, this aspect of the new law contributes to a dual “open by default” approach that should allow for easier reuse of software and data while also making governance more transparent.

Researchers at Truffle Security have found, or arguably rediscovered, that data from deleted GitHub repositories (public or private) and from deleted copies (forks) of repositories isn't necessarily deleted.

Joe Leon, a security researcher with the outfit, said in an advisory on Wednesday that being able to access deleted repo data – such as APIs keys – represents a security risk. And he proposed a new term to describe the alleged vulnerability: Cross Fork Object Reference (CFOR).

"A CFOR vulnerability occurs when one repository fork can access sensitive data from another fork (including data from private and deleted forks)," Leon explained.

For example, the firm showed how one can fork a repository, commit data to it, delete the fork, and then access the supposedly deleted commit data via the original repository.

The researchers also created a repo, forked it, and showed how data not synced with the fork continues to be accessible through the fork after the original repo is deleted. You can watch that particular demo.

Police could lawfully use bulk surveillance techniques to access messages from encrypted communications platforms such as WhatsApp and Signal, following a ruling by the UK’s Investigatory Powers Tribunal (IPT), a court has heard.

cross-posted from: https://lemmy.ml/post/18426215

I mean, he's not wrong that the app wasn't ready. Which begs the question why they didn't un-roll-it-out. >.>

Source Nature research paper

The new global study, in partnership with The Upwork Research Institute, interviewed 2,500 global C-suite executives, full-time employees and freelancers. Results show that the optimistic expectations about AI's impact are not aligning with the reality faced by many employees. The study identifies a disconnect between the high expectations of managers and the actual experiences of employees using AI.

Despite 96% of C-suite executives expecting AI to boost productivity, the study reveals that, 77% of employees using AI say it has added to their workload and created challenges in achieving the expected productivity gains. Not only is AI increasing the workloads of full-time employees, it’s hampering productivity and contributing to employee burnout.

It is now clear that generative artificial intelligence (AI) such as large language models (LLMs) is here to stay and will substantially change the ecosystem of online text and images. Here we consider what may happen to GPT-{n} once LLMs contribute much of the text found online. We find that indiscriminate use of model-generated content in training causes irreversible defects in the resulting models, in which tails of the original content distribution disappear. We refer to this effect as ‘model collapse’ and show that it can occur in LLMs as well as in variational autoencoders (VAEs) and Gaussian mixture models (GMMs). We build theoretical intuition behind the phenomenon and portray its ubiquity among all learned generative models. We demonstrate that it must be taken seriously if we are to sustain the benefits of training from large-scale data scraped from the web. Indeed, the value of data collected about genuine human interactions with systems will be increasingly valuable in the presence of LLM-generated content in data crawled from the Internet.