Researchers in Japan have used artificial intelligence to recreate a stunningly accurate 3D model of a centuries-old stone relief based on a single, faded photograph taken 134 years ago. This technique could prove a game-changer for digital preservation, especially for artifacts that are hidden, too fragile to be excavated, or too delicate for conventional 3D modeling.
Source: Breakthrough A.I. Model Reveals Lost Ancient Relief from 134-Year-Old Photo
The late Grateful Dead guitarist Jerry Garcia’s estate has recreated his voice using AI in partnership with Eleven Labs. The singer-songwriter’s voice can now read to ElevenReader app users their choice of audiobooks, articles, poetry, PDFs, and more through what Eleven calls the Iconic Listening Experience, Billboard reported. Garcia’s voice is available in 32 different languages.
Source: Jerry Garcia’s AI-Created Voice Can Now Narrate Audiobooks, Articles and More
Dutch publisher Veen Bosch & Keuning’s announcement that it would use AI translation for commercial fiction has outraged both authors and translators – despite attempts to reassure them with promises that no books will be translated in this way without careful checking and that authors will have to give consent. “A translator translates more than just words, we build bridges between cultures, taking into account the target readership every step of the way,” says Michele Hutchison, winner of 2020’s International Booker prize.
Source: ‘It gets more and more confused’: can AI replace translators?
The platform, currently in beta testing, is set to officially launch in early 2025 and access will be by invite only. It is said to be “the first in the market to offer AI attribution via Sureel AI.” The tech will enable artists and labels to track how their music is used in AI training models and determine fair compensation for AI-generated outputs.
Source: Rostrum Pacific unveils AI-powered music distribution platform SpaceHeater
Though the band has been broken up for 50 years, Paul McCartney decided to use AI last year to create “the last Beatles record.” McCartney isn’t using this technology to resurrect his late bandmates, John Lennon and George Harrison, with deepfakes. Instead, McCartney took one of Lennon’s demos from 1978 and used AI to clean up the recording’s poor sound quality.
Source: The Beatles’ AI-assisted track ‘Now and Then’ is nominated for two Grammy awards
Streaming has reduced musicians’ income from record sales. This has made concerts the main source of income for many. “AI will not take over the concert market. People will always be interested in what other people do. And we will still want to experience live music,” Jeffs says. At the same time, he sees that many side jobs for musicians are already disappearing. These include jobs creating background music, jingles for radio and podcasts, and music for films, games and advertising.
Source: ‘Artificial intelligence is not a threat to music,’ says researcher
Google DeepMind, in collaboration with Hugging Face, open sourced its research titled ‘Scalable watermarking for identifying large language model outputs’ in a bid to distinguish between human and AI content on the internet, driven by LLMs. Launched exactly a year ago, SynthID, their watermarking tool, is now available for wider access.
Source: Can Google’s Watermark Tool Save the Internet from AI Slop?
Perplexity is finalizing a new funding round that would value it at $9 billion—triple its valuation from just a few months ago—the latest sign of continued investor excitement for artificial intelligence startups. The funding round would turn Perplexity into one of the most valuable young AI startups to emerge out of the generative AI boom. It was valued at just $520 million at the start of this year.
Source: AI Startup Perplexity to Triple Valuation to $9 Billion in New Funding Round
Leading AI companies rely more on content from premium publishers to train their large language models (LLMs) than they publicly admit, according to new research from executives at Ziff Davis. While AI firms generally do not say exactly what data they use for training, executives from Ziff Davis say their analysis of publicly available datasets makes it clear that AI firms rely disproportionately on commercial publishers of news and media websites to train their LLMs.
Source: Ziff Davis study says AI firms rely on publisher data to train models
Millions of stories published by sites including The New York Times and The New York Daily News have been found in three weeks of searching OpenAI’s training dataset. The news publishers are currently trawling through data to find instances of their copyrighted work being used to train OpenAI’s models – but they say the tech company should be forced to provide the information itself.
Source: ‘Millions’ of NYT and NY Daily News stories taken by OpenAI for training data