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AI lab by information labs
information labs
35 episodes
5 months ago
AI lab podcast, "decrypting" expert analysis to understand Artificial Intelligence from a policy making point of view.
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Technology
Science
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All content for AI lab by information labs is the property of information labs and is served directly from their servers with no modification, redirects, or rehosting. The podcast is not affiliated with or endorsed by Podjoint in any way.
AI lab podcast, "decrypting" expert analysis to understand Artificial Intelligence from a policy making point of view.
Show more...
Technology
Science
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AI lab TL;DR | Emmie Hine - Can Europe Lead the Open-Source AI Race?
AI lab by information labs
11 minutes
6 months ago
AI lab TL;DR | Emmie Hine - Can Europe Lead the Open-Source AI Race?

🔍 In this TL;DR episode, Emmie Hine (Yale Digital Ethics Center) makes the case for Europe’s leadership in open-source AI—thanks to strong infrastructure, multilingual data, and regulatory clarity. With six key policy recommendations, the message is clear: trust and transparency can make EU models globally competitive.


📌 TL;DR Highlights

⏲️[00:00] Intro

⏲️[00:43] Q1-What advantages make the EU a strong contender in open-source AI compared to the US and China?

⏲️[03:08] Q2-How do EU regulations and initiatives enhance model trustworthiness and feasibility?

⏲️[05:43] Q3-What policy recommendations does the paper offer for EU AI governance and deployment?

⏲️[10:10] Wrap-up & Outro


💭 Q1 - What advantages make the EU a strong contender in open-source AI compared to the US and China?


🗣️ “The EU’s primary advantage is actually in regulatory leadership and trustworthiness.”

🗣️ “There’s access to a lot of multilingual data, which is really great and important for creating multilingual LLMs.”

🗣️ “As DeepSeek showed, big leaps are very possible.”


💭 Q2 - How do EU regulations and initiatives enhance model trustworthiness and feasibility?


🗣️ “Its framework of risk-based classification and how it encourages ethics by design—I think that’s really, really important for guiding responsible development and deployment of foundation models.”

🗣️ “The AI Office is really putting an emphasis on multi-stakeholder collaboration... bringing in civil society, academia, and industry.”

🗣️ “The Code of Practice will provide clarity on training data and copyright as well.”


💭 Q3 - Why focus on regulating specific AI apps instead of AI overall?


🗣️ “Establishing an EU-wide open-source foundation model governance framework... would combat open-washing, where companies say, ‘Oh yeah, this is open source,’ but it’s not really.”

🗣️ “A certification and benchmarking system to evaluate open models for security, reliability, ethics, and performance... would help boost user trust and also international competitiveness.”

🗣️ “Expanding funding programs—especially for SMEs and startups—can help them put out more competitive models while encouraging multilingual capabilities and ethical development.”

🗣️ “Ideally, this investment will ensure that these facilities are going to be energy efficient to help combat the climate impacts of models.”

🗣️ “There was definitely not a universal understanding of open-source technology in general, and specifically around open-source AI... So promoting digital literacy and responsible AI usage is going to be really, really crucial.”


📌 About Our Guest

🎙️ Emmie Hine | Yale Digital Ethics Center

🌐 Article | Open-Source Foundation Models Made in the EU: Why it is a Good Idea

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5191372 

🌐Newsletter | The Ethical Reckoner

https://ethicalreckoner.substack.com/ 

🌐 Emmie Hine

linkedin.com/in/emmiehine


Emmie Hine is a Research Associate at the Yale Digital Ethics Center and a PhD candidate in Law, Science, and Technology at the University of Bologna and KU Leuven. Her research focuses on the ethics and governance of emerging technologies in different geopolitical contexts. 

#AI #ArtificialIntelligence #GenerativeAI

AI lab by information labs
AI lab podcast, "decrypting" expert analysis to understand Artificial Intelligence from a policy making point of view.