Policymakers must recognize the open source AI ecosystem is where influence is being negotiated: not just which models exist, but which are used; not just who can train a trillion-parameter network, but who can make it deployable, modifiable, and relevant, say Lucie-Aimée Kaffee and Shayne Longpre.
Neither in the United States nor in the European Union are regulations yet fully prepared for the mix of intimacy and monetization that AI chatbots can introduce, write Hugging Face's Lucie-Aimée Kaffee and Giada Pistilli. We need to learn the lessons from past failures on social media, they say.
🤖 Did you know your voice might be cloned without your consent from just *one sentence* of audio?
That's not great. So with @frimelle.bsky.social, we brainstormed a new idea for developers who want to curb malicious use: ✨The Voice Consent Gate.✨
Details, code, here: huggingface.co/blog/voice-c...
What if your most personal chat logs became the next source of ad data?
@frimelle.bsky.social and I wrote an op-ed for @techpolicypress.bsky.social
We look at what happens when generative AI conversations (the ones we treat as private) are turned into raw material for targeted advertising.
Wie kann KI offen & verantwortungsvoll gestaltet werden? Zwei neue Publikationen fassen Ergebnisse der Konferenz „Yes, we are open!?“ zusammen. Die Veröffentlichungen bieten Empfehlungen für Politik & Praxis – für eine faire, zukunftsfähige KI. 🌍🤖 www.weizenbaum-institut.de/news/detail/...
Instead of a checkbox, consent becomes something you actually say: the model only proceeds if you speak and match a randomly generated consent phrase.
It’s a small but concrete step toward consent by design and a way to start rethinking technical safeguards as part of AI policy.
What does it mean if anyone’s voice can be cloned and made to say whatever someone else wants?
Together with @mmitchell.bsky.social we built a first prototype, a Voice Consent Gate, to explore how consent could be built into AI voice cloning itself.
Excited to publish this piece!
Who is winning the open AI race?
Our new study Economies of Open Intelligence maps @hf.co 851k models' downloads 2020→2025.
1) Power rebalance: US tech ↓; China + community ↑
2) Models size & efficient ↑ (MoE, quant, multimodal)
3) Intermediary layers ↑ (adapters/quantizers)
4) Transparency ↓
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