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Bigger isn't always better: how to choose the most efficient model for context-specific tasks 🌱🧑🏼💻
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13Giada Pistilli
giadap
AI & ML interests
Principal Ethicist @ HF
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Ever notice how some AI assistants feel like tools while others feel like companions? Turns out, it's not always about fancy tech upgrades, because sometimes it's just clever design.
Our latest blog post at Hugging Face dives into how minimal design choices can completely transform how users experience AI. We've seen our community turn the same base models into everything from swimming coaches to interview prep specialists with surprisingly small tweaks.
The most fascinating part? When we tested identical models with different "personalities" in our Inference Playground, the results were mind-blowing.
Want to experiment yourself? Our Inference Playground lets anyone (yes, even non-coders!) test these differences in real-time. You can:
- Compare multiple models side-by-side
- Customize system prompts
- Adjust parameters like temperature
- Test multi-turn conversations
It's fascinating how a few lines of instruction text can transform the same AI from strictly professional to seemingly caring and personal, without changing a single line of code in the model itself.
Read more here: https://huggingface.co/blog/giadap/ai-personas
Our latest blog post at Hugging Face dives into how minimal design choices can completely transform how users experience AI. We've seen our community turn the same base models into everything from swimming coaches to interview prep specialists with surprisingly small tweaks.
The most fascinating part? When we tested identical models with different "personalities" in our Inference Playground, the results were mind-blowing.
Want to experiment yourself? Our Inference Playground lets anyone (yes, even non-coders!) test these differences in real-time. You can:
- Compare multiple models side-by-side
- Customize system prompts
- Adjust parameters like temperature
- Test multi-turn conversations
It's fascinating how a few lines of instruction text can transform the same AI from strictly professional to seemingly caring and personal, without changing a single line of code in the model itself.
Read more here: https://huggingface.co/blog/giadap/ai-personas
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AI Personas: The Impact of Design Choices
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Hi Andy, thank you so much for your thoughtful comment! I am glad my post help you framing those important questions.
Sure thing, send over by email your documentation and let's chat: [email protected]
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🤗 Just published: "Consent by Design" - exploring how we're building better consent mechanisms across the HF ecosystem!
Our research shows open AI development enables:
- Community-driven ethical standards
- Transparent accountability
- Context-specific implementations
- Privacy as core infrastructure
Check out our Space Privacy Analyzer tool that automatically generates privacy summaries of applications!
Effective consent isn't about perfect policies; it's about architectures that empower users while enabling innovation. 🚀
Read more: https://huggingface.co/blog/giadap/consent-by-design
Our research shows open AI development enables:
- Community-driven ethical standards
- Transparent accountability
- Context-specific implementations
- Privacy as core infrastructure
Check out our Space Privacy Analyzer tool that automatically generates privacy summaries of applications!
Effective consent isn't about perfect policies; it's about architectures that empower users while enabling innovation. 🚀
Read more: https://huggingface.co/blog/giadap/consent-by-design
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Consent by Design: Approaches to User Data in Open AI Ecosystems
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Why AI Companion Applications are a Lifeline, Not a Threat
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Empowering Public Organizations: Preparing Your Data for the AI Era
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We've all become experts at clicking "I agree" without a second thought. In my latest blog post, I explore why these traditional consent models are increasingly problematic in the age of generative AI.
I found three fundamental challenges:
- Scope problem: how can you know what you're agreeing to when AI could use your data in different ways?
- Temporality problem: once an AI system learns from your data, good luck trying to make it "unlearn" it.
- Autonomy trap: the data you share today could create systems that pigeonhole you tomorrow.
Individual users shouldn't bear all the responsibility, while big tech holds all the cards. We need better approaches to level the playing field, from collective advocacy and stronger technological safeguards to establishing "data fiduciaries" with a legal duty to protect our digital interests.
Available here: https://huggingface.co/blog/giadap/beyond-consent
I found three fundamental challenges:
- Scope problem: how can you know what you're agreeing to when AI could use your data in different ways?
- Temporality problem: once an AI system learns from your data, good luck trying to make it "unlearn" it.
- Autonomy trap: the data you share today could create systems that pigeonhole you tomorrow.
Individual users shouldn't bear all the responsibility, while big tech holds all the cards. We need better approaches to level the playing field, from collective advocacy and stronger technological safeguards to establishing "data fiduciaries" with a legal duty to protect our digital interests.
Available here: https://huggingface.co/blog/giadap/beyond-consent
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I Clicked “I Agree”, But What Am I Really Consenting To?
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Open R1: Update #3
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