Part of the Deep Dive: AI Webinar Series
The initial conception of Free and Open Source Software was developed during a time where software was bundled into discrete packages to be run on machines owned and operated by a single individual. The initial FOSS movement utilized licensing and copyright law to provide better autonomy and control over these software systems. Now, our software systems often operate as platforms, monopolizing access between networks and resources and profiting greatly through that monopoly.
In this talk, listeners will learn more about the ideological foundations of FOSS and the blindspots that have developed in our community as software has transitioned from individual discrete packages into deeply interconnected systems that gate access to critical resources for many. We will delve into what autonomy might mean in a world where the deployment of technology inherently affects so many. Finally, we will observe the flaws in conventional open source approaches to providing autonomy and what other tools we may have at our disposal to ensure better community governance of this increasingly pervasive technology.
In this webinar hosted by the Open Source Initiative as a part of the “Deep Dive: Defining Open Source AI” series, Mike Nolan discusses the history and evolution of free and open-source software, examining how the concept applies to emerging technologies like AI. Mike references a recent influential paper titled “Open for Business: Big Tech Concentrated Power in the Political Economy of Open AI.” The presentation is divided into four key sections: a historical overview of open source, the changing nature of software from discrete applications to complex platforms, the components and spectrum of openness in AI, and an analysis of the pros and cons of open AI. It also touches upon the claims made regarding open AI, its impact on innovation, and the changing landscape of software development. Ultimately, Mike emphasizes that open source does not guarantee democratization or meaningful competition in AI, highlighting the need for a critical evaluation of its value in the context of AI deployment and regulation.