With Elon Musk’s Twitter suffering yet another catastrophic outage (which has seen the Tesla and SpaceX founder implement temporary limits on how many tweets we can view in a day), users are once again looking for a rival platform to evacuate to. Previously it was the likes of Post and Mastodon - the latter of which Musk hated so much he restricted users from linking to their new accounts on Twitter - but now it’s the turn of Bluesky to get all the hype. Backed by Twitter founder Jack Dorsey, the platform claims to offer “an open and decentralised standard for social media” – “decentralised” meaning users don’t have an account under one ‘centralised’ company owned by one particular company, but can instead sign up to individual servers and communities owned and managed by other people. Sign up to our free Indy100 weekly newsletter It's already proven popular, with surreal social media poster Dril and New York congresswoman Alexandria Ocasio-Cortez already signed up. In fact, after Twitter’s latest blunder, invite codes to Bluesky became so lucrative and sign-ups became so intense that they had to temporarily pause new registrations (they’ve reopened them now, though, if you fancy a try). And while others are considering whether to jump ship to the rival site, or desperately hunting for the code which will let them in, some Twitter users were reminded of a legendary rock band thanks to the platform’s choice of name: Yep, for a handful of individuals, the hit track from Jeff Lynne and his Electric Light Orchestra (or ELO) came to mind – and it’s perhaps made all the more amusing considering one of “Mr Blue Sky’s” lyrics is the rhetorical question, “where did we go wrong”. If Musk were to ask that question, we’d probably say firing almost half of Twitter’s workforce and therefore making it almost impossible to deal with the demand is part of the problem. And if you were wondering, a few searches on Bluesky appear to suggest that neither Lynne or ELO are on the platform. Indy100 has approached Lynne’s representatives for a statement, but they said the musician was unavailable for comment. Have your say in our news democracy. Click the upvote icon at the top of the page to help raise this article through the indy100 rankings.

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Future generations of artificial intelligence chatbots trained using data from other AIs could lead to a downward spiral of gibberish on the internet, a new study has found. Large language models (LLMs) such as ChatGPT have taken off on the internet, with many users adopting the technology to produce a whole new ecosystem of AI-generated texts and images. But using the output data from such AI systems to further train subsequent generations of AI models could result in “irreversible defects” and junk content, according to a new, yet-to-be peer-reviewed study. AI models like ChatGPT are trained using vast amounts of data pulled across internet platforms that have mostly remained human generated until now. But AI-generated data using such models have a growing presence on the internet. Researchers, including those from the University of Oxford in the UK, attempted to understand what happened when several subsequent generations of AIs are trained off each other. They found the widespread use of LLMs to publish content on the internet on a large scale “will pollute the collection of data to train them” and lead to “model collapse”. “We discover that learning from data produced by other models causes model collapse – a degenerative process whereby, over time, models forget the true underlying data distribution,” scientists wrote in the study, posted as a preprint in arXiv. The new findings suggested there to be a “first mover advantage” when it comes to training LLMs. Scientists liken this change to what happens when AI models are trained on music created by human composers and played by human musicians. The subsequent AI output then trains other models, leading to a diminishing quality of music. With subsequent generations of AI models likely to encounter poorer quality data at their source, they may start misinterpreting information by inserting false information in a process scientists call “data poisoning”. They warned that the scale at which data poisoning can happen drastically changes after the advent of LLMs. Just a few iterations of data can lead to major degradation, even when the original data is preserved, scientists said. And over time, this could lead to mistakes compounding and forcing models that learn from generated data to misunderstand reality. “This in turn causes the model to misperceive the underlying learning task,” researchers said. Scientists cautioned that steps must be taken to label AI-generated content from human-generated ones, along with efforts to preserve original human-made data for future AI training. “To make sure that learning is sustained over a long time period, one needs to make sure that access to the original data source is preserved and that additional data not generated by LLMs remain available over time,” they wrote in the study. “Otherwise, it may become increasingly difficult to train newer versions of LLMs without access to data that was crawled from the Internet prior to the mass adoption of the technology, or direct access to data generated by humans at scale.” Read More ChatGPT ‘grandma exploit’ gives users free keys for Windows 11 Protect personal data when introducing AI, privacy watchdog warns businesses How Europe is leading the world in the push to regulate AI ‘Miracle material’ solar panels to finally enter production Meta reveals new AI that is too powerful to release Reddit user’s protests against the site’s rules have taken an even more bizarre turn

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