This chapter delves into the concept of AI democratization, discussing the different ways tech companies approach it and the potential dangers of conflating it with open source AI development. The speakers emphasize the importance of clarifying true democratization and avoiding 'democracy washing.'
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.
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
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
Supports governments, technology companies, and other key institutions by producing relevant research and guidance around how to respond to the challenges posed by AI