Ireland's data privacy regulator is investigating Elon Musk's X. The country's Data Protection Commission (DPC) said on Friday (via Reuters) that it's opening an inquiry into the social platform's use of European users' public posts to train its Grok AI chatbot. In this case, Ireland handles EU regulation enforcement because X's European headquarters are in Dublin. The DPC said it will probe "the processing of personal data comprised in publicly-accessible posts posted on the'X' social media platform by EU/EEA users." Under Europe's General Data Protection Regulation (GDPR) rules, Ireland has the legal muscle to fine X up to four percent of its global revenue.

X will permanently avoid training its AI chatbot Grok on the public posts of users in the European Union and European Economic Area following pressure from a regulator in the region. Last month, the company temporarily suspended the practice after Ireland's Data Protection Commission (DPC) opened High Court proceedings against it. X has now made that commitment a permanent one, which prompted the DPC to end its legal action. The DPC, which is the chief EU regulator for X, raised concerns that X may have been violating data protection rules and users' rights. Since May, X had offered users the option to opt-out of having their public posts being used to train Grok, implying that the company had enabled that setting for public accounts by default.

Kurt "Cyberguy" Knutsson talks about how to protect your social media posts; a toddler getting trapped in a Tesla after the battery died. Meta may have paused its plans to train artificial intelligence models for the lucky ones living in Europe, where laws protect people using Facebook and Instagram better than Americans. Here in the good ole USA, both Facebook and Instagram have already been combing through public posts from U.S. accounts to train and improve its AI capabilities, including its chatbot, since last year. The proposed privacy policy update for European Union and U.K. users, originally scheduled for June 26, would have allowed Meta to use publicly shared content for AI training. However, users and regulatory agencies opposed this plan, leading to its indefinite postponement in those regions.

In R. C. Sherriff's novel The Hopkins Manuscript, readers are transported to a world 800 years after a cataclysmic event ended Western civilization. In pursuit of clues about a blank spot in their planet's history, scientists belonging to a new world order discover diary entries in a swamp-infested wasteland formerly known as England. For the inhabitants of this new empire, it is only through this record of a retired school teacher's humdrum rural life, his petty vanities and attempts to breed prize-winning chickens, that they begin to learn about 20th-century Britain. If I were to teach futuristic beings about life on earth, I once believed I could produce a time capsule more profound than Sherriff's small-minded protagonist, Edgar Hopkins. But scrolling through my decade-old Facebook posts this week, I was presented with the possibility that my legacy may be even more drab.

In an interview, Meta's president of global affairs, Nick Clegg, said the tools "are really quite important" in that they provide, in a lot of ways, "the most comprehensive access to publicly available content across Facebook and Instagram of anything that we've built to date." The Content Library will also help the company meet new regulatory requirements and obligations on data sharing and transparency, as the company notes in a blog post Tuesday. The library and associated API were first released as a beta version several months ago and allow researchers to access near-real-time data about pages, posts, groups, and events on Facebook and creator and business accounts on Instagram, as well as the associated numbers of reactions, shares, comments, and post view counts. While all this data is publicly available--as in, anyone can see public posts, reactions, and comments on Facebook--the new library makes it easier for researchers to search and analyze this content at scale. Meta says that to protect user privacy, this data will be accessible only through a virtual "clean room" and not downloadable.

"CyberGuy" explains how Meta is admitting to using user data to train its AI. How would you feel if your social media posts were used to train a virtual assistant without your consent? That is exactly what is happening to millions of people who belong to Facebook and Instagram. Meta, the parent company of Facebook, admits that it is using public posts from both Instagram and Facebook members to train its new artificial intelligence assistant, Meta AI. CLICK TO GET KURT'S FREE CYBERGUY NEWSLETTER WITH SECURITY ALERTS, QUICK VIDEO TIPS, TECH REVIEWS, AND EASY HOW-TO'S TO MAKE YOU SMARTER Meta admits to using your posts to train its AI.

Our work aims to facilitate by Thrash and Elliot as possessing three core such encounters by providing tools for automatic identification characteristics: evocation (i.e., it is triggered rather than of text content likely to be judged inspiring. We focus on willed), transcendence (i.e., it orients towards things outside inspiration in everyday content as judged by lay people, similar of and greater than the self), and approach motivation (i.e., it in spirit to early work by Hart who attempted to capture the energizes approach rather than avoidance [1]-[3]). Inspiration experience of inspiration in ordinary life [5], rather than "as if has two distinct stages: one an activation state that is more akin it were reserved for the gifted artist, the breakthrough scientist, to feeling and emotion, the second an urge to act.

Facebook says it has deployed a feature in its Community Help hub to make it easier for users to assist each other during the pandemic. As of this week, AI will detect when a public post on News Feed is about needing or offering help and will surface a suggestion to share it on Community Help. Once a post is moved or published directly to the hub, an algorithm will recommend matches between people. For example, if someone posts an offer to deliver groceries, they'll see recommendations within Community Help to connect with people who recently posted about needing this type of assistance. Similarly, if someone requests masks, AI will surface suggested neighbors who recently posted an offer to make face coverings.

A social media platform designed for America's military community is now equipped with a custom machine learning model that insiders say can rapidly review public posts and pinpoint those that show signs and risks of potential self-harm. With support from the Veterans Affairs Department and Harvard University's Nock Lab, Amazon Web Services linked up with the existing RallyPoint military social media platform to target the production of a technological solution that can speedily surface sensitive public posts and boost online suicide intervention. "Historically, the heavy lifting of mental health support on RallyPoint has been shouldered by RallyPoint members stepping up to help each other when they come across people sharing their challenges on our site," RallyPoint CEO Dave Gowel recently told Nextgov. "Now, through our work with the VA, AWS and mental health experts from Harvard, we are more proactive in reinforcing our members' good work by offering helpful resources when we are alerted about public posts showing signs of risk." Launched in 2012, RallyPoint enables nearly 2 million service members, veterans, and their families to connect, share stories and information, ask questions and ultimately chat on topics that accompany military and veteran life.

It has already been established that digital traces can be used to predict various human attributes. In most cases, however, predictive models rely on features that are specific to a particular source of digital trace data. In contrast, short texts written by users $-$ tweets, posts, or comments $-$ are ubiquitous across multiple platforms. In this paper, we explore the predictive power of short texts with respect to the academic performance of their authors. We use data from a representative panel of Russian students that includes information about their educational outcomes and activity on a popular networking site, VK. We build a model to predict academic performance from users' posts on VK and then apply it to a different context. In particular, we show that the model could reproduce rankings of schools and universities from the posts of their students on social media. We also find that the same model could predict academic performance from tweets as well as from VK posts. The generalizability of a model trained on a relatively small data set could be explained by the use of continuous word representations trained on a much larger corpus of social media posts. This also allows for greater interpretability of model predictions.