The Promise and Peril of Open Source AI with Elizabeth Seger and Jeffrey Ladish

As AI development races forward, a fierce debate has emerged over open source AI models. So what does it mean to open-source AI? Are we opening Pandora’s box of catastrophic risks? Or is open-sourcing AI the only way we can democratize its benefits and dilute the power of big tech?
Correction: When discussing the large language model Bloom, Elizabeth said it functions in 26 different languages. Bloom is actually able to generate text in 46 natural languages and 13 programming languages - and more are in the works.
RECOMMENDED MEDIA
Open-Sourcing Highly Capable Foundation Models (elizabethseger.com/open-sourc...)
This report, co-authored by Elizabeth Seger, attempts to clarify open-source terminology and to offer a thorough analysis of risks and benefits from open-sourcing AI
BadLlama: cheaply removing safety fine-tuning from Llama 2-Chat 13B (arxiv.org/abs/2311.00117)
This paper, co-authored by Jeffrey Ladish, demonstrates that it’s possible to effectively undo the safety fine-tuning from Llama 2-Chat 13B with less than $200 while retaining its general capabilities
Centre for the Governance of AI (www.governance.ai/)
Supports governments, technology companies, and other key institutions by producing relevant research and guidance around how to respond to the challenges posed by AI
AI: Futures and Responsibility (AI:FAR) (www.ai-far.org/)
Aims to shape the long-term impacts of AI in ways that are safe and beneficial for humanity
Palisade Research (palisaderesearch.org/)
Studies the offensive capabilities of AI systems today to better understand the risk of losing control to AI systems forever
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