Whenever Meta claims their models are open source, you have to double-check.<p>SAM License (<a href="https://github.com/facebookresearch/sam3/blob/main/LICENSE" rel="nofollow">https://github.com/facebookresearch/sam3/blob/main/LICENSE</a>):<p>> iv. Your use of the SAM Materials will not involve or encourage others to reverse engineer, decompile or discover the underlying components of the SAM Materials.<p>> v. You are not the target of Trade Controls and your use of SAM Materials must comply with Trade Controls. You agree not to use, or permit others to use, SAM Materials for any activities subject to the International Traffic in Arms Regulations (ITAR) or end uses prohibited by Trade Controls, including those related to military or warfare purposes, nuclear industries or applications, espionage, or the development or use of guns or illegal weapons.<p>> b. If you institute litigation or other proceedings against Meta or any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the SAM Materials, outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Meta from and against any claim by any third party arising out of or related to your use or distribution of the SAM Materials.<p>The DINOv3 License (<a href="https://github.com/facebookresearch/dinov3/blob/main/LICENSE.md" rel="nofollow">https://github.com/facebookresearch/dinov3/blob/main/LICENSE...</a>) is similar but with the model names swapped.<p>It's always nice when a model's weights are released, but Meta's models are not open source because their weights always come with weird restrictions.
I'm no fan of Facebook or its social effects but I can't deny the wonderful downstream effect of their open source.<p>Popular microscopy models like Cellpose[0] have leaned heavily on the cornucopia of open and SOTA power. I have no doubt thousands of biologists have benefitted from the capabilities these models bring. I think it was unthinkable just 5 years ago that a single biologist with just a laptop could do mass-segmentation at this kind of fidelity.<p>Then there's Napari and it's plugin ecosystem[1] that wouldn't exist without the Chan Zuckerberg Initiative. Again, I'm not trying to glaze them but as someone in the biotech/microscopy space I can't understate how often I use and benefit from their open source.<p>[0] <a href="https://cellpose.readthedocs.io/en/latest/models.html" rel="nofollow">https://cellpose.readthedocs.io/en/latest/models.html</a>
[1] <a href="https://chanzuckerberg.com/rfa/napari-plugin-grants/" rel="nofollow">https://chanzuckerberg.com/rfa/napari-plugin-grants/</a>
The scoop: X-ray imaging of various strictures for scientific purposes produces colossal reams of data, previously hard to analyze. Meta provides machine analysis, both segmentation and classification, using unsupervised learning models.<p>> <i>a fully reconstructed, semantically labeled 3D volume delivered back to the scientist physically standing at the beamline [x-ray] instrument, ready for interpretation while the experiment is still running. Total turnaround: approximately 15 minutes.</i>
This fits with my impression of the 'personality' of various models:<p>Meta: Perceptive (strong vision)<p>Gemini: Fastest<p>Claude: Smartest<p>OpenAI: Prettiest
I love how consistently none of us even vaguely consider Grok an actual player
To Grok's credit I think it's fairly good as a creative writing tool because it can be very "spontaneous" and it naturally seems to use an informal style. It also lacks a lot of the words and phrasing Claude and OpenAI get hyper-fixated on.<p>IDK if this is emergent from being trained on an endless trough of Twitter shitposts but compared to how stiff the rest are, I consider it a feature. I wouldn't use it for anything important though, heh.
The models might be good but the product design, user story, and marketing is so terrible that it’s difficult to see it as more than an also-ran
I would replace Gemini with DeepSeek. I also find OpenAI smarter than Claude but Claude is better for API ergonomics and frontend
They aren't talking about vision LLMs though. SAM and DINO are vision models, no LLM involved.
Why do you think OpenAI is the prettiest?<p>Claude often makes better looking interfaces and designs. And I think OpenAI has solved more open math/ stats/ CS problems.
Their text output often includes more emojis and seems formatted better. Also their image models subjectively looks better than the rest.
> And I think OpenAI has solved more open math/ stats/ CS problems.<p>Still surprises me that OpenAI seems to lead in this one weird niche, I wonder what causes GPT to be able to routinely pull this off, there was one instance where some random 18 year old broke some mathematical question without knowing more than high school math if I remember correctly, all because of GPT.
> Claude often makes better looking interfaces and designs<p>How do you even qualify this? Either by "Well, when you're not specifying anything about it in the prompt" and then it almost doesn't matter at all, or by what actually goes into the prompt, then again it doesn't matter at all what model you use, more about the person driving it.
Qwen: Zestiest<p>Nemo: Straightest<p>DeepSeek: Craftiest
this page hijacks your tab's back button history :\
ELI5.
SAM 3 (Segment Anything Model 3) and DINOv3, projects released by Facebook, were used to do science and research.
AI model inspects hundreds of thousands of scientific images. A job that previously took an expert roughly a month can now be completed in around 15 minutes.
Gonna need to have a talk with LLNL. I'm sure they didn't choose the name, but seems a tad leaning in to use the name of tech from Star Trek meant to produce untold abundance that instead became an unintentional doomsday device.
So, not LLM models, right?<p>Also on this:<p>> The numbers are staggering: The DOE's light and neutron source facilities now produce tens of petabytes of data annually<p>Come on, petabytes are not staggering for entreprise software.
I used to work with images representing scans of brain tissue - for a full brain visualization at one horizontal slice terabytes was a common measure and the resolution of those images wasn't even particularly detailed - all the full resolution stuff was taken of tiny sub-sections of interest. This was also two decades ago - so I'm sure they've upped their game.
I'm not sure exactly which enterprises you have in mind, but sure: quantities which can be expressed as "a year's worth fits on my desk" should not be described as "staggering", and 10 PB of hard drives will (just about) fit on my desk.<p>The LHC, on the other hand, that generates a petabyte a second and has to throw most of it away for obvious reasons:<p><a href="https://www.itnews.com.au/news/computing-for-the-large-hadron-collider-310769" rel="nofollow">https://www.itnews.com.au/news/computing-for-the-large-hadro...</a>
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As a former proposal specialist (B2B, B2G, non DoD) I looked into the Genesis Mission procurement site and process.<p>Unless someone can correct me, the total amount of grant monies is $280,000,000 or so.<p>It became obvious that it’s not worth my time to engage in the “mission” as they call it, even if I could benefit some worthwhile causes.<p>That’s a pittance and pretty insulting to the purported benefit of funding scientific endeavors. I’m not even attempting to be political here. $280 Million versus $XX Billion for warfighting is a seriously gross misallocation of public monies, IMHO.<p>Total lackluster reporting on the scale and scope of the actual numbers, but not surprising.
This is a really weird comparison. The Chan Zuckerberg foundation is nowhere involved with any war. All they’re doing is making money available for research. In which universe is $280m not enough?
It's a weird world where $280M is not considered a lot of money.
That is — incredible
> Meta's open-source approach makes this possible<p>I'm sorry, was this article drafted in 2024 and never updated?
This sort of random out of date comment is a common LLM writing trope. Though it is true that the segment anything model is open source.<p><a href="https://github.com/facebookresearch/sam3" rel="nofollow">https://github.com/facebookresearch/sam3</a><p>They have another with calling A100s modern.<p>> A100 GPUs — the high-performance computing chips that power today's most advanced AI systems.<p>They might not have written it with AI, but the article has a lot of em dashs and colons and not this but that statements.
The models in question have source and weights available. AFAIK not fully FOSS because the weights require registration to access.
SAM3.1 was released and open sourced March 2026.<p>But what's the point of correcting you? People will continue to propagate their own lies.