The Free Puppy: How to Responsibly Leverage Open Source AI
Over the past few decades, the trajectory of AI and open source software has evolved in tandem, but with the recent explosion of LLMs and foundation models, “open source AI” has become a catch phrase meaning so many things to so many different people. Some definitions of open source AI are so specific that they leave out hugely popular projects; other definitions focus specifically on models, and many AI projects marketed as open source don’t actually fit within the industry definition of open source. With so many different definitions floating around, how can you know what you are getting when you engage with open source AI?
In this talk, we will review what makes something open source from an industry perspective, what to look for when evaluating the health, stability, and practicality of open source AI projects (or any open source project for that matter), and why it’s so important to ensure transparency and explainability when deploying open source AI applications. In addition, in this webinar, we will cover licensing considerations, how to evaluate the health of open source projects, how and when to leverage open source, and the importance of community.
Mo McElaney leads Open Source Developer Programs at IBM Research. For more than a decade, Mo has worked in open source, focusing on cloud data services, quantum computing, machine learning, and ethics in AI. They are on the content committee for PyCon US, a contributor to version 3 of the Contributor Covenant and Hippocratic License with the Organization for Ethical Source, and on the board of the Vermont Technology Alliance. Mo is a certified master gardener with the University of Vermont and lives in frosty Vermont with their family, cats, and many plants.