Meta is preparing a major round of layoffs that could cut up to 8,000 jobs as the company restructures and leans further into AI-driven operations. The post Meta Plans Up to 8,000 Job Cuts in New Round of Layoffs appeared first on TechRepublic.

Agibot says its project marks a significant shift in embodied AI from pilots to industrial use.

AI platform, Bobyard, has unveiled Bobyard 2.0, its latest platform update delivering accelerated takeoff workflows and a unified AI workbench, designed to keep pace with the estimators (those responsible for calculating project budgets) who use it every day throughout the construction and landscaping industry. By speeding up takeoff operations, a important part of estimating a […] The post Bobyard 2.0 offers improved takeoffs and unified AI for estimators appeared first on AI News.

Maddy and Sam get into the launch of Mothership and the importance of owning one's own work.

The pasta-sauce company has partnered with the nonprofit StoryCorps on a device designed to record family conversations around the table and save them for all time.

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Jess Weatherbed / The Verge: Huawei launches the Pura X Max, a passport-style foldable with a 5.4-inch cover display and a 7.7-inch internal display, in China, starting at ~$1,613  —  The Pura X Max launched in China today. The Pura X Max launched in China today. … Huawei has launched its passport-style foldable in China …

The government capital program joins other sovereign AI financial initiatives around the world.

Table of Contents Pytest Tutorial: MLOps Testing, Fixtures, and Locust Load Testing Introduction to MLOps Testing: Building Reliable ML Systems with Pytest Why Testing Is Non-Negotiable in MLOps What You Will Learn: Pytest, Fixtures, and Load Testing for MLOps From… The post Pytest Tutorial: MLOps Testing, Fixtures, and Locust Load Testing appeared first on PyImageSearch.

People tend to be offended when they get a personal note written by AI – if they know. Ekaterina Buravleva/iStock via Getty Images Two new experiments show that most people do not even consider that a personal message could be AI-generated, even when they themselves use artificial intelligence to write. To see how people judge someone based on their writing in the age of ChatGPT, my colleague Jiaqi Zhu and I recruited more than 1,300 U.S.-based participants, ages 18 to 84, and showed them AI-generated messages like an apology sent in an email. We split our volunteers into four groups: Some people saw the messages with no information about who or what wrote them, as in everyday life. Others were told the messages were definitely written by a human, definitely AI-generated, or that the source could be either. An AI-generated fictional apology sent via text was one of the messages participants evaluated in a recent study. Zhu & Molnar (2026) We found a clear “AI disclosure penalty.” When people knew a message was AI-generated, they rated the sender much more negatively – “lazy,” “insincere,” “lack of effort” – than when they believed that the same text was written by a person – “genuine,” “grateful,” “thoughtful.” But here is the twist: The participants who were not told anything about authorship formed impressions that were just as positive as those from people who were told the messages were genuinely human. This complete lack of skepticism surprised us – and it raises new questions. Maybe participants were not familiar enough with AI to realize that today’s models can produce detailed and personal messages. (They can.) Or perhaps participants have never used AI themselves. (They likely have.) So we also tested whether participants’ own AI use changed how they judged senders. To our even bigger surprise, we found little to no effect. People who use generative AI quite frequently in their daily lives – at least every other day – did penalize AI use slightly less when AI authorship was disclosed, compared with people who never or rarely use AI. But participants were no more skeptical by default: When authorship was not disclosed, heavy AI users, light AI users and nonusers all tended to assume the text was written by a person and formed essentially the same impressions. Word clouds depict participants’ first impressions of senders who wrote messages themselves, left, and those who used AI, right. Andras Molnar Why it matters Lack of skepticism and a lack of negative impressions matter because people make social judgments from text all the time. Recipients consider taking the time and effort to send written messages as an insight into the writer’s sincerity, authenticity or competence, and those impressions shape people’s decisions in friendships, dating and work. Yet our main findings reveal a striking disconnect: People usually do not suspect AI use unless it is obvious. This unawareness creates a moral dilemma: People who use AI in secret can enjoy the benefits while facing almost no risk of detection. Meanwhile, paradoxically, people who are upfront and admit to using AI suffer a reputational hit. Over time, lack of skepticism and awareness could reshape what writing means in everyday life. Readers might learn to treat writing as a less reliable signal of someone’s character or effort, and instead rely on other forms of communication. For example, widespread AI use has already prompted employers to discount the value of cover letters from job applicants. Instead, they are relying more on personal recommendations from an applicant’s current supervisor or connections made through in-person networking. What other research is being done Other researchers have documented a wide range of negative impressions about people who disclose their AI use. Studies show it makes job applicants seem less desirable and employees seem less competent. Readers of creative writing perceive AI users as less creative and inauthentic. People see personal apologies and corporate apologies that stem from AI as less effective. In general, disclosing AI use decreases trust and undermines legitimacy. Yet without disclosure, there is clear evidence that most people cannot reliably detect AI-generated text, even with the help of detection tools, especially when the text is a mix of human-written and AI-generated content. Even when people feel confident about their ability to spot AI text, their confidence may be nothing more than a self-affirming illusion. What’s next Even though our experiments did not reveal suspicion of AI use, that doesn’t mean people never suspect it in the real world. In some settings, people may already be hypervigilant about AI. Use in academia is an obvious example. In our next studies, we want to understand when and why people naturally start to suspect AI use, and what flips the switch between trust and doubt. Until then, if you want your personal message to be judged as heartfelt, the safest strategy may be to make a phone call, leave a voicemail or, better yet, say it in person. The Research Brief is a short take on interesting academic work. Andras Molnar does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

In the absence of legislative regulation, federal and state agencies pursue oversight.

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. No one’s sure if synthetic mirror life will kill us all In February 2019, a group of scientists proposed a high-risk, cutting-edge, irresistibly exciting idea that the National Science Foundation should…

Zero-shot text classification is a way to label text without first training a classifier on your own task-specific dataset.

How to turn data into a strategic asset that enables faster decisions, reduces uncertainty, and helps the organization move toward its goals. The post From Risk to Asset: Designing a Practical Data Strategy That Actually Works appeared first on Towards Data Science.

Jake Rudnitsky / Bloomberg: A Blue Origin rocket failed to correctly place a BlueBird satellite from satellite networking company AST into its intended orbit; ASTS falls 14% pre-market  —  AST SpaceMobile Inc. shares plunged in early trading on Monday after Blue Origin's flagship New Glenn rocket failed to correctly place …

For all the possibilities AI gives us, there is always a chance of the technology malfunctioning or becoming compromised. In the event of an AI system crisis, new research from ISACA has found that the majority of organisations surveyed couldn’t explain how quickly they could stop an AI system emergency, or even report on what […] The post How to prepare for and remediate an AI system incident appeared first on AI News.

This past quarter, funding to security- and privacy-focused startups dipped slightly on a sequential basis, but remained well above year-ago levels. Overall, investors put $4.9 billion into global companies in the space in Q1, per Crunchbase data, a comparatively solid performance relative to recent quarters.

How does HEVC implementation really work these days?

Mark Zuckerberg and Jack Dorsey have different visions for how to use AI for management purposes, but both imagine a system of heightened control.

Nduka Orjinmo / Bloomberg: Nigeria-based Terra, which makes pilotless drones and defense systems, raised $34M from Joe Lonsdale, Lux Capital, and others, as jihadist groups drive demand  —  Terrahaptix Inc., a Nigerian drone-making startup, will open its first factory abroad in Ghana, where it will build mid-range pilotless aircraft …