USA Today Joins the OpenAI Copyright Queue

USA Today Co. filed a lawsuit against OpenAI on Thursday, according to The Verge, claiming the company copied "hundreds of thousands" of articles to train its AI models without ever asking for permission. The filing seeks damages exceeding $250 million.

The suit is one of dozens now stacking up against OpenAI. The New York Times, The Intercept, Ziff Davis, CBC/Radio-Canada, Encyclopaedia Britannica, Merriam-Webster, The Seattle Times, and a coalition of nearly 400 local newspapers have all filed similar claims. USA Today's filing adds The Tennessean, Indy Star, The Columbus Dispatch, and The Oklahoman to the list.

The lawsuit's central argument is blunt: OpenAI's commercial success was built on copyrighted material it had no right to use, turning other people's journalism into a product worth hundreds of billions of dollars. OpenAI had not responded to a request for comment at the time of reporting.

Nothing has been decided in court. These are allegations. But the pattern is hard to ignore — the list of publishers willing to absorb the legal cost of suing one of the world's most valuable companies keeps growing.

What to watch: whether any of these cases reaches a verdict or forces a licensing settlement that changes how AI companies acquire training data. That outcome would affect every AI product built on text.

Google has released an experimental note-taking app called Google AI Edge Foresight, according to The Verge. It transcribes meetings and audio files entirely on your device using Google's on-device EmbeddingGemma 2 model, meaning your audio never touches a cloud server.

It is free. During a meeting, you can jot shorthand bullet points and the app will turn them into polished notes using the transcript. A built-in assistant lets you ask questions about what was discussed, and you can attach local files for it to reference.

The privacy angle is the main differentiator here. Apps like Granola and Wispr Flow do similar things, but both send audio to remote servers to process it.

Currently only available for Macs with Apple Silicon (M-series chips). Windows is not supported.

Honest read: Worth trying if you're on a compatible Mac and privacy is why you've avoided AI note-taking apps until now — this closes the main objection.

What it is: A lightweight AI model that converts text, images, audio, video, and code into embeddings (numerical representations that capture meaning), so a search system can find related content across all those formats at once, entirely on a local device.

What's new: The original EmbeddingGemma handled text only. This version processes five data types in a single shared space, meaning one query can pull results from audio recordings, video clips, images, and documents simultaneously.

How it works: When you feed in a voice memo, the model maps it into the same numerical space as your text notes and image files. A search query — itself converted into that same space — finds whatever is closest in meaning, regardless of format. The model has 740 million parameters (a measure of model size and capacity), runs with as little as 567MB of active RAM on a phone, and supports an 8,000-token context window (context window: the maximum amount of text or data the model can consider at one time), four times larger than its predecessor.

What it can't do: Google's benchmarks show top scores among models under one billion parameters, but those evaluations were run by Google. Independent results at frontier scale — larger, general-purpose models — remain untested.

Who should care: Developers building offline search tools or privacy-focused apps that need to search across mixed media without sending data to a server.

Try it / read it: EmbeddingGemma 2 model card and weights on Hugging Face

Falcon ASR is a 1.6 billion parameter (a measure of model size) speech-to-text model built specifically for Arabic, with strong support for the Emirati dialect. It also handles English, French, Spanish, and Portuguese using the same model weights, with no language flag needed — it detects what is spoken and transcribes accordingly.

Developed by the Technology Innovation Institute in Abu Dhabi, it achieved a word error rate (WER — the percentage of words transcribed incorrectly) of 20.92% across six Arabic test sets, beating the previous best published result by around 2.25 percentage points. On an internal Emirati evaluation, it outperformed all compared systems at both word and character level.

You can try it now via the Hugging Face Demo Space — paste or upload audio and see the transcript. Model weights are available to download if you want to run it locally.

Worth knowing: The Emirati evaluation used internal recordings and human-validated transcripts rather than fully public benchmarks, so treat those specific numbers as the company's own testing rather than independently verified results.

NIST, the U.S. agency that sets measurement standards and technical benchmarks, has formally endorsed a White House initiative called "A New Golden Age of Science," according to NIST's announcement. The programme aims to accelerate AI-driven scientific discovery across materials science, biotechnology, and quantum research. For ordinary users, the immediate effect is that NIST's open-access data repositories — used to evaluate whether AI systems are trustworthy — will be directed toward these priority areas. No new rules or compliance requirements were announced.