Farewell Ai2

2023-11-24 · Source: Interconnects AI · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation, Research Methodology & Innovation · Depth: Intermediate, long

Summary

Nathan Lambert is departing the Allen Institute for AI (Ai2) after contributing to the Olmo models, including Olmo 2 and Olmo 3, and projects like Tülu 2, Tülu 3, and RewardBench. He reflects on Ai2's significant impact, attributing it to the institute's open science culture and its unique position between academia and industry, fostering the safe diffusion of AI technology. Lambert details his career progression from a UC Berkeley EECS Ph.D. in MEMS to becoming an AI researcher, joining HuggingFace, and then Ai2 in October 2023. He highlights the importance of open research in providing clarity on frontier models, creating a diverse open ecosystem, and building institutions that advocate for a public-good-oriented AI future, especially as much AI research moves behind closed doors.

Key takeaway

For research scientists navigating AI's evolving landscape, prioritize open science and public engagement. Your work in open ecosystems, even with mid-sized models like Olmo, can significantly influence policy, train future generations, and mitigate power concentration. Actively share your research and build relationships to maximize impact, as explaining your work is often harder than building it. Consider contributing to open post-training recipes to address current gaps and foster a diverse AI ecosystem.

Key insights

Open science and public scientists are crucial for AI's safe diffusion, diverse ecosystem, and future talent development, counteracting the trend of closed industry research.

Principles

• Work's value is near zero if unknown.
• Vision and clear explanations are scarce.
• Open research defines AI understanding.

In practice

• Build interesting, fully-open models.
• Train medium models for specific tasks.
• Invest in open model diversity.

Topics

• Open Science
• AI Research Ecosystem
• Olmo Models
• Post-training Techniques
• Public Scientists
• Career Development

Best for: AI Scientist, Research Scientist, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by Interconnects AI.