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Facebook is joining Google in open-sourcing its AI hardware

Facebook is the latest in a line of tech giants that are giving away the designs of their computers servers dedicated to AI.

Built with the aid of Nvidia, the social network’s state-of-the-art server, codenamed Big Sur, can run the latest AI algorithms, and comes wielding a huge number of GPUs — built with the aid of Nvidia. At Facebook, the “deep learning” technology helps identify faces in user photos, curates the news feed, and even powers parts of the company’s upcoming “M” personal assistant, reports Wired.

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But why are the likes of Google, Microsoft, and now Facebook so keen for the tech industry to get their hands on their hardware? It’s mainly due to the fact that all three companies currently don’t have the manpower to grow the technology at the pace they would like. It also doesn’t help that the entire field of deep learning researchers is still fairly small.

Facebook is hoping that by submitting its AI hardware to the Open Compute Project — a group through which it shares the designs of its computer infrastructure with other tech giants including Microsoft and Apple — it can attract more industry talent into its fold.

Yann LeCun, the man in charge of Facebook’s AI research group, believes that opening up Big Sur can help unlock design ideas for the newly created server, reports MIT Technology Review. “Companies like us actually thrive on fast progress; the faster the progress can be made, the better it is for us,” says LeCun.

In the increasingly competitive field of AI research, the recruitment process is the new battlefront for companies such as Facebook and Google. Although Google beat it to the punch by sharing its AI hardware last month, Facebook’s inception of the Open Commute Project established it as the frontrunner in open-sourcing technology. Facebook claims that this approach helps drive down costs and accelerate the growth of technology.

Saqib Shah
Former Digital Trends Contributor
Saqib Shah is a Twitter addict and film fan with an obsessive interest in pop culture trends. In his spare time he can be…
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Apple's Craig Federighi presents the Image Playground app running on macOS Sequoia at the company's Worldwide Developers Conference (WWDC) in June 2024.

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Gemini Advanced on the Google Pixel 9 Pro Fold.

Update: Google has responded to Digital Trends’ queries. The story has been updated with company’s statement below.
The rise of generative AI has been a fairly messy process, especially from fair usage ethics and copyright perspective. AI giants are inking deals with publishers to avoid legal hassles, while at the same time, they are embroiled in copyright tussles in courts in multiple countries.
As the ravenous appetite for training AI on user data grows, we might be in for another ethical conundrum. Multiple users on X and Reddit have shared demonstrations of how Google’s latest Gemini 2.0 series AI model can remove watermarks from copyright-protected images.
Going by the before/after samples of images, it seems Gemini is doing a fairly good job of removing the watermarks. Notably, it’s not only erasing those banner-style watermarks, but also fairly complex overlays with design and stylized text elements.
https://x.com/ajiteshleo/status/1901484196972945872?s=61
The model in question is the Gemini 2.0 Flash, which was released earlier this year and got a reasoning upgrade, as well. It is worth noting here that you can not remove the watermark if you are using the mobile or desktop version of the Gemini 2.0 Flash model. Trying to do so returns a message like this:
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You can, however, try and remove the watermark from images in the Google AI Studio. Digital Trends successfully removed watermarks from a variety of images using the Gemini 2.0 Flash (Image Generation) Experimental model.
 
It is a violation of local copyright laws and any usage of AI-modified material without due consent could land you in legal trouble. Moreover, it is a deeply unethical act, which is also why artists and authors are fighting in court over companies using their work to train AI models without duly compensating them or seeking their explicit nod.

How are the results?
A notable aspect is that the images produced by the AI are fairly high quality. Not only is it removing the watermark artifacts, but also fills the gap with intelligent pixel-level reconstruction. In its current iteration, it works somewhat like the Magic Eraser feature available in the Google Photos app for smartphones.
Furthermore, if the input image is low quality, Gemini is not only wiping off the watermark details but also upscaling the overall picture. .
https://x.com/kaiju_ya/status/1901099096930496720?s=61
The output image, however, has its own Gemini watermark, although this itself can be removed with a simple crop. There are a few minor differences in the final image produced by Gemini after its watermark removal process, such as slightly different color temperatures and fuzzy surface details in photorealistic shots.

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