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sashaย  authored a paper 25 days ago
Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model
sashaย  authored a paper 25 days ago
Hype, Sustainability, and the Price of the Bigger-is-Better Paradigm in AI
sashaย  authored a paper 25 days ago
From Efficiency Gains to Rebound Effects: The Problem of Jevons' Paradox in AI's Polarized Environmental Debate
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megย 
posted an update 4 days ago
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๐Ÿค– Did you know your voice might be cloned without your consent from just *one sentence* of audio?
That's not great. So with @frimelle , we brainstormed a new idea for developers who want to curb malicious use: โœจThe Voice Consent Gate.โœจ
Details, code, here: https://huggingface.co/blog/voice-consent-gate
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giadapย 
posted an update 24 days ago
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๐ŸŒŽ AI ethics and sustainability are two sides of the same coin.

In our new blog post with Dr. Sasha Luccioni, we argue that separating them (as is too often the case) means missing the bigger picture of how AI systems impact both people and the planet.

Ethical and sustainable AI development canโ€™t be pursued in isolation. The same choices that affect who benefits or is harmed by AI systems also determine how much energy and resources they consume.

We explore how two key concepts, evaluation and transparency, can serve as bridges between these domains:

๐Ÿ“Š Evaluation, by moving beyond accuracy or performance metrics to include environmental and social costs, as weโ€™ve done with tools like the AI Energy Score.

๐Ÿ” Transparency, by enabling reproducibility, accountability, and environmental reporting through open tools like the Environmental Transparency Space.

AI systems mirror our priorities. If we separate ethics from sustainability, we risk building technologies that are efficient but unjust, or fair but unsustainable.

Read our blog post here: https://huggingface.co/blog/sasha/ethics-sustainability

AIEnergyScore/Leaderboard
sasha/environmental-transparency
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sashaย 
authored 3 papers 25 days ago
evijitย 
posted an update 27 days ago
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AI for Scientific Discovery Won't Work Without Fixing How We Collaborate.

My co-author @cgeorgiaw and I just published a paper challenging a core assumption: that the main barriers to AI in science are technical. They're not. They're social.

Key findings:

๐Ÿšจ The "AI Scientist" myth delays progress: Waiting for AGI devalues human expertise and obscures science's real purpose: cultivating understanding, not just outputs.
๐Ÿ“Š Wrong incentives: Datasets have 100x longer impact than models, yet data curation is undervalued.
โš ๏ธ Broken collaboration: Domain scientists want understanding. ML researchers optimize performance. Without shared language, projects fail.
๐Ÿ” Fragmentation costs years: Harmonizing just 9 cancer files took 329 hours.

Why this matters: Upstream bottlenecks like efficient PDE solvers could accelerate discovery across multiple sciences. CASP mobilized a community around protein structure, enabling AlphaFold. We need this for dozens of challenges.

Thus, we're launching Hugging Science! A global community addressing these barriers through collaborative challenges, open toolkits, education, and community-owned infrastructure. Please find all the links below!

Paper: AI for Scientific Discovery is a Social Problem (2509.06580)
Join: hugging-science
Discord: https://discord.com/invite/VYkdEVjJ5J
giadapย 
posted an update about 1 month ago
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One of the hardest challenges in AI safety is finding the right balance: how do we protect people from harm without undermining their agency? This tension is especially visible in conversational systems, where safeguards can sometimes feel more paternalistic than supportive.

In my latest piece for Hugging Face, I argue that open source and community-driven approaches offer a promising (though not exclusive) way forward.

โœจ Transparency can make safety mechanisms into learning opportunities.
โœจ Collaboration with diverse communities makes safeguards more relevant across contexts.
โœจ Iteration in the open lets protections evolve rather than freeze into rigid, one-size-fits-all rules.

Of course, this isnโ€™t a silver bullet. Top-down safety measures will still be necessary in some cases. But if we only rely on corporate control, we risk building systems that are safe at the expense of trust and autonomy.

Read the blog post here: https://huggingface.co/blog/giadap/preserving-agency
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megย 
posted an update about 2 months ago
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๐Ÿค– As AI-generated content is shared in movies/TV/across the web, there's one simple low-hanging fruit ๐Ÿ‡ to help know what's real: Visible watermarks. With the Gradio team, I've made sure it's trivially easy to add this disclosure to images, video, chatbot text. See how: https://huggingface.co/blog/watermarking-with-gradio
Thanks to the code collab in particular from @abidlabs and Yuvraj Sharma.
yjerniteย 
posted an update about 2 months ago
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Tremendous quality of life upgrade on the Hugging Face Hub - we now have auto-complete emojis ๐Ÿค— ๐Ÿฅณ ๐Ÿ‘ ๐Ÿ™Œ ๐ŸŽ‰

Get ready for lots more very serious analysis on a whole range of topics from yours truly now that we have unlocked this full range of expression ๐Ÿ˜„ ๐Ÿค” ๐Ÿ—ฃ ๐Ÿ™Š
giadapย 
posted an update 2 months ago
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I've noticed something. While we're careful about what we post on social media, we're sharing our deepest and most intimate thoughts with AI chatbots -- health concerns, financial worries, relationship issues, business ideas...

With OpenAI hinting at ChatGPT advertising, this matters more than ever. Unlike banner ads, AI advertising happens within the conversation itself. Sponsors could subtly influence that relationship advice or financial guidance.

The good news? We have options.
๐Ÿค Open source AI models let us keep conversations private, avoid surveillance-based business models, and build systems that actually serve users first.

Read more about it in our latest blog post, co-written with
@frimelle
https://huggingface.co/blog/giadap/privacy-conversational-ai
giadapย 
posted an update 2 months ago
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๐Ÿ“Š We benchmark models for coding, reasoning, or safetyโ€ฆ but what about companionship?

At Hugging Face, weโ€™ve been digging into this question because many of you know how deeply I care about how people build emotional bonds with AI.

Thatโ€™s why, building on our ongoing research, my amazing co-author and colleague @frimelle created the AI Companionship Leaderboard ๐Ÿฆพ
frimelle/companionship-leaderboard

Grounded in our INTIMA benchmark, the leaderboard evaluates models across four dimensions of companionship:
๐Ÿค– Assistant Traits: the โ€œvoiceโ€ and role the model projects
๐ŸŒท Relationship & Intimacy: whether it signals closeness or bonding
๐Ÿ’˜ Emotional Investment: the depth of its emotional engagement
๐Ÿคฒ User Vulnerabilities: how it responds to sensitive disclosures

This work builds on our paper with @frimelle and @yjernite .

๐Ÿ“ข Now weโ€™d love your perspective: which open models should we test next for the leaderboard? Drop your suggestions in the comments or reach out! Together we can expand the leaderboard and build a clearer picture of what companionship in AI really looks like.

Paper: INTIMA: A Benchmark for Human-AI Companionship Behavior (2508.09998)
INTIMA Benchmark: AI-companionship/INTIMA
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giadapย 
authored a paper 2 months ago
megย 
posted an update 3 months ago
yjerniteย 
authored 3 papers 3 months ago
megย 
posted an update 3 months ago
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๐Ÿค– ICYMI: Yesterday, Hugging Face and OpenAI partnered to bring open source GPT to the public. This is a Big Deal in "AI world".

0. Common ground setting: OpenAI is the ChatGPT people. An โ€œopen sourceโ€ model is one whose weights are available โ€” that means the model can be โ€œyoursโ€.
1. You donโ€™t have to interact with the company directly, nor give them your interactions, to use the system. The company can't "surveil" you.
2. You can evaluate the unique contributions of their SOTA model much more rigorously than you can when there are collections of models+code behind a closed API. You can find out specifically what the model can and can't do.
3. And you can directly customize it for whatever you'd like. Fine-tuning, wherein you give the model data that's tailored to your use cases and train it some more on that data, is trivial* when you have the model weights.
*Provided you have the compute.
4. You can directly benchmark whatever you'd like. Biases? Energy usage? Strengths/weaknesses? Go for it. You wants it you gots it--this transparency helps people understand SOTA *in general*, not just for this model, but points to, e.g., what's going on with closed Google models as well.
5. One of the most powerful things about "openness" that I've learned is that it cultivates ecosystems of collaborators building on top of one another's brilliance to make systems that are significantly better than they would be if created in isolation.
But, caveat wrt my own philosophy...
6. I do not take it as a given that advancing LLMs is good, and have a lot more to say wrt where I think innovation should focus more. For example, a focus on *data* -- curation, measurement, consent, credit, compensation, safety -- would deeply improve technology for everyone.
7. The transparency this release provides is massive for people who want to *learn* about LLMs. For the next generation of technologists to advance over the current, they MUST be able to learn about what's happening now. (cont...)
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megย 
posted an update 3 months ago
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๐Ÿค– ๐Ÿ‘พ Thanks so much to BBC News and the stellar Suranjana Tewari for having me on to talk about US <โ€”> China relationship in AI, and what it means for AI ethics.
giadapย 
posted an update 3 months ago
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๐Ÿ’ฌ From Replika to everyday chatbots, millions of people are forming emotional bonds with AI, sometimes seeking comfort, sometimes seeking intimacy. But what happens when an AI tells you "I understand how you feel" and you actually believe it?

At Hugging Face, together with @frimelle and @yjernite , we dug into something we felt wasn't getting enough attention: the need to evaluate AI companionship behaviors. These are the subtle ways AI systems validate us, engage with us, and sometimes manipulate our emotional lives.

Here's what we found:
๐Ÿ‘‰ Existing benchmarks (accuracy, helpfulness, safety) completely miss this emotional dimension.
๐Ÿ‘‰ We mapped how leading AI systems actually respond to vulnerable prompts. ๐Ÿ‘‰ We built the Interactions and Machine Attachment Benchmark (INTIMA): a first attempt at evaluating how models handle emotional dependency, boundaries, and attachment (with a full paper coming soon).

Check out the blog post: https://huggingface.co/blog/giadap/evaluating-companionship

๐Ÿšข We also shipped two visualization tools with Gradio to see how different models behave when things get emotionally intense:
- AI-companionship/intima-responses-2D
- giadap/INTIMA-responses
yjerniteย 
posted an update 3 months ago
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๐—™๐—ถ๐—ฟ๐˜€๐˜ ๐—š๐—ฃ๐—”๐—œ ๐— ๐—ผ๐—ฑ๐—ฒ๐—น ๐˜„๐—ถ๐˜๐—ต ๐—˜๐—จ ๐——๐—ฎ๐˜๐—ฎ ๐—ง๐—ฟ๐—ฎ๐—ป๐˜€๐—ฝ๐—ฎ๐—ฟ๐—ฒ๐—ป๐—ฐ๐˜† ๐—ง๐—ฒ๐—บ๐—ฝ๐—น๐—ฎ๐˜๐—ฒ? ๐Ÿ‡ช๐Ÿ‡บ

With the release of the EU data transparency template this week, we finally got to see one of the most meaningful artifacts to come out of the AI Act implementation so far (haven't you heard? AI's all about the data! ๐Ÿ“Š๐Ÿ“š)

The impact of the template will depend on how effectively it establishes a minimum meaningful transparency standard for companies that don't otherwise offer any transparency into their handling of e.g. personal data or (anti?-)competitive practices in commercial licensing - we'll see how those play out as new models are released after August 2nd ๐Ÿ‘€


In the meantime, I wanted to see how the template works for a fully open-source + commercially viable model, so I filled it out for the SmolLM3 - which my colleagues at Hugging Face earlier this month ๐Ÿค— ICYMI, it's fully open-source with 3B parameters and performance matching the best similar-size models (I've switched all my local apps from Qwen3 to it, you should too ๐Ÿ’ก)

Verdict: congrats to the European Commission AI Office for making it so straightforward! Fully open and transparent models remain a cornerstone of informed regulation and governance, but the different organizational needs of their developers aren't always properly accounted for in new regulation. In this case, it took me all of two hours to fill out and publish the template (including reading the guidelines) - so kudos for making it feasible for smaller and distributed organizations ๐Ÿ™Œ Definitely a step forward for transparency ๐Ÿ”

To learn more have a look at:

- The SmolLM3 model: HuggingFaceTB/SmolLM3-3B
- Its filled out Public Summary of Training Content: hfmlsoc/smollm3-eu-data-transparency
- And if you're interested, some previous remarks on regulatory minimum meaningful standards for data disclosure: https://huggingface.co/blog/yjernite/naiac-data-transparency