What if AI systems weren't chatbots?
Paper • 2605.07896 • Published • 8
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evijit
submitted a
paper to Daily Papers 8 days ago
Post
2848
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:
🚨 The "AI Scientist" myth delays progress: Waiting for AGI devalues human expertise and obscures science's real purpose: cultivating understanding, not just outputs.
📊 Wrong incentives: Datasets have 100x longer impact than models, yet data curation is undervalued.
⚠️ Broken collaboration: Domain scientists want understanding. ML researchers optimize performance. Without shared language, projects fail.
🔍 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
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:
🚨 The "AI Scientist" myth delays progress: Waiting for AGI devalues human expertise and obscures science's real purpose: cultivating understanding, not just outputs.
📊 Wrong incentives: Datasets have 100x longer impact than models, yet data curation is undervalued.
⚠️ Broken collaboration: Domain scientists want understanding. ML researchers optimize performance. Without shared language, projects fail.
🔍 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:
Discord: https://discord.com/invite/VYkdEVjJ5J
andrewelawrence
updated 2
Spaces 10 months ago
Post
411
New blog post alert! "What is the Hugging Face Community Building?", with @yjernite and @irenesolaiman
What 1.8 Million Models Reveal About Open Source Innovation: Our latest deep dive into the Hugging Face Hub reveals patterns that challenge conventional AI narratives:
🔗 Models become platforms for innovation Qwen, Llama, and Gemma models have spawned entire ecosystems of specialized variants. Looking at derivative works shows community adoption better than any single metric.
📊 Datasets reveal the foundation layer → Most downloaded datasets are evaluation benchmarks (MMLU, Squad, GLUE) → Universities and research institutions dominate foundational data → Domain-specific datasets thrive across finance, healthcare, robotics, and science → Open actors provide the datasets that power most AI development
🏛️ Research institutions lead the charge: AI2 (Allen Institute) emerges as one of the most active contributors, alongside significant activity from IBM, NVIDIA, and international organizations. The open source ecosystem spans far beyond Big Tech.
🔍 Interactive exploration tools: We've built several tools to help you discover patterns!
ModelVerse Explorer - organizational contributions
DataVerse Explorer - dataset patterns
Organization HeatMap - activity over time
Base Model Explorer - model family trees
Semantic Search - find models by capability
📚 Academic research is thriving: Researchers are already producing valuable insights, including recent work at FAccT 2025: "The Brief and Wondrous Life of Open Models." We've also made hub datasets, weekly snapshots, and other data available for your own analysis.
The bottom line: AI development is far more distributed, diverse, and collaborative than popular narratives suggest. Real innovation happens through community collaboration across specialized domains.
Read: https://huggingface.co/blog/evijit/hf-hub-ecosystem-overview
What 1.8 Million Models Reveal About Open Source Innovation: Our latest deep dive into the Hugging Face Hub reveals patterns that challenge conventional AI narratives:
🔗 Models become platforms for innovation Qwen, Llama, and Gemma models have spawned entire ecosystems of specialized variants. Looking at derivative works shows community adoption better than any single metric.
📊 Datasets reveal the foundation layer → Most downloaded datasets are evaluation benchmarks (MMLU, Squad, GLUE) → Universities and research institutions dominate foundational data → Domain-specific datasets thrive across finance, healthcare, robotics, and science → Open actors provide the datasets that power most AI development
🏛️ Research institutions lead the charge: AI2 (Allen Institute) emerges as one of the most active contributors, alongside significant activity from IBM, NVIDIA, and international organizations. The open source ecosystem spans far beyond Big Tech.
🔍 Interactive exploration tools: We've built several tools to help you discover patterns!
ModelVerse Explorer - organizational contributions
DataVerse Explorer - dataset patterns
Organization HeatMap - activity over time
Base Model Explorer - model family trees
Semantic Search - find models by capability
📚 Academic research is thriving: Researchers are already producing valuable insights, including recent work at FAccT 2025: "The Brief and Wondrous Life of Open Models." We've also made hub datasets, weekly snapshots, and other data available for your own analysis.
The bottom line: AI development is far more distributed, diverse, and collaborative than popular narratives suggest. Real innovation happens through community collaboration across specialized domains.
Read: https://huggingface.co/blog/evijit/hf-hub-ecosystem-overview
andrewelawrence
published 2
Spaces 10 months ago
Post
1754
The HF Policy Team submitted our response to the 2025 National Artificial Intelligence (AI) Research and Development (R&D) Strategic Plan.
Blog (with link to full pdf response):
https://huggingface.co/blog/evijit/us-ai-research-strategy-rfi
Blog (with link to full pdf response):
https://huggingface.co/blog/evijit/us-ai-research-strategy-rfi
evijit
authored 4
papers about 1 year ago
In-House Evaluation Is Not Enough: Towards Robust Third-Party Flaw Disclosure for General-Purpose AI
Paper • 2503.16861 • Published • 1
Stop treating `AGI' as the north-star goal of AI research
Paper • 2502.03689 • Published • 5
"It's not a representation of me": Examining Accent Bias and Digital Exclusion in Synthetic AI Voice Services
Paper • 2504.09346 • Published • 3
Fully Autonomous AI Agents Should Not be Developed
Paper • 2502.02649 • Published • 36
evijit
authored a
paper about 2 years ago