Foundation models (FMs) are the defining paradigm of modern AI. Beginning with language models like GPT-3, the paradigm extends to images, videos, code, proteins, and much more. Foundation models are both the frontier of AI research and the subject of global discourse (NYT, Nature, The Economist, CNN). To exemplify this, UK Prime Minister Rishi Sunak announced a FM taskforce as a top-level national priority. Given their remarkable capabilities, these models are widely deployed: Google and Microsoft are integrating FMs into flagship products like Microsoft Word and Google Slides, while OpenAI’s GPT-4 is powering products and services from Morgan Stanley, Khan Academy, Duolingo, and Stripe. FMs have seen unprecedented adoption — Reuters recently reported that ChatGPT, launched in November 2022, is the fastest-growing consumer application in history.
With FMs on the rise, especially in light of billions of dollars of funding to FM developers (Adept, Anthropic, Character, OpenAI, Perplexity), transparency is on the decline. OpenAI’s GPT-4 report reads: “Given both the competitive landscape and the safety implications of large-scale models like GPT-4, this report contains no further details about the architecture (including model size), hardware, training compute, dataset construction, training method, or similar.” At the Center for Research on Foundation Models, we are committed to ensuring foundation models are transparent. To date, we have built HELM to standardize their evaluation, argued for community norms to govern their release, and advocated for policy to expand these efforts.
To clarify the societal impact of foundation models, we are releasing Ecosystem Graphs as a centralized knowledge graph for documenting the foundation model ecosystem. Ecosystem Graphs consolidates distributed knowledge to improve the ecosystem’s transparency. Ecosystem Graphs helps us answer many questions about the foundation model ecosystem:
Ecosystem Graphs currently documents the ecosystem of 200+ assets, including datasets (e.g. The Pile, LAION-5B), models (e.g. BLOOM, Make-a-Video), and applications (e.g. GitHub Copilot X, Notion AI), spanning 60+ organizations and 9 modalities (e.g. music, genome sequences). Ecosystem Graphs will be a community-maintained resource (akin to Wikipedia) that we encourage everyone to contribute to! We envision Ecosystem Graphs will provide value to stakeholders spanning AI researchers, industry professionals, social scientists, auditors and policymakers.
Like all technologies, foundation models are situated in a broader social context. In Figure 1, we depict the canonical pipeline for such development, whereby datasets, models, and applications mediate the relationship between the people on either side.
Concretely, consider Stable Diffusion:
While framed technically (e.g. curation, training, adaptation), the process bears broader societal ramifications: for example, ongoing litigation contends the use of LAION-5B and the subsequent image generation from StableDiffusion is unlawful, infringing on the rights and the styles of data creators.
The ecosystem graph is defined in terms of assets: each asset has a type (one of dataset, model, application). Examples include The Pile dataset, the Stable Diffusion model, and the Bing Search application. To define the graph structure, each asset X has a set of dependencies, which are the assets required to build X. For example, LAION-5B is a dependency for Stable Diffusion and Stable Diffusion is a dependency for Stable Diffusion Reimagine. To supplement the graph structure, we annotate metadata (e.g. who built the asset) for every asset in its ecosystem card. In Figure 2, we give several examples of primitive structures (i.e. subgraphs) that we observe in the full ecosystem graph:
Ecosystem Graphs will provide value to a range of stakeholders: developers, AI researchers, social scientists, investors, auditors, policymakers, end users, and more. To demonstrate this value, we zoom into the hubs in the ecosystem: hubs are nodes with unusually high connectivity, featuring prominently in the analysis of graphs and networks. Figure 3 identifies four such hubs.
For asset developers, hubs indicate their assets are high impact. For economists, hubs communicate emergent market structure and potential consolidation of power. For investors, hubs signal opportunities to further support or acquire. For policymakers and auditors, hubs identify targets to scrutinize to ensure their security and safety. Beyond hubs, we present more extensive arguments for why Ecosystem Graphs is useful in the paper.
The footprint of foundation models is rapidly expanding: this class of emerging technology is pervading society and is only in its infancy. Ecosystem Graphs aims to ensure that foundation models are transparent, establishing the basic facts of how they impact society. Concurrently, we encourage policy uptake to standardize reporting on all assets in Ecosystem Graphs: we will release a brief on Ecosystem Graphs as part of our series on foundation models.
Where to go from here:
Ecosystem Graphs was developed with Dilara Soylu (Stanford University) and Katie Creel (Northeastern University). Our design and implementation were informed by valuable feedback from folks at a range of organizations: we would like to thank Alex Tamkin, Ali Alvi, Amelia Hardy, Ansh Khurana, Ashwin Paranjape, Ayush Kanodia, Chris Manning, Dan Ho, Dan Jurafsky, Daniel Zhang, David Hall, Dean Carignan, Deep Ganguli, Dimitris Tsipras, Erik Brynjolfsson, Iason Gabriel, Irwing Kwong, Jack Clark, Jeremy Kaufmann, Laura Weidinger, Maggie Basta, Michael Zeng, Nazneen Rajani, Rob Reich, Rohan Taori, Tianyi Zhang, Vanessa Parli, Xia Song, Yann Dubois, and Yifan Mai for valuable feedback on this effort at various stages. We specifically thank Ashwin Ramaswami for extensive guidance on prior work in the open-source ecosystem and future directions for the foundation model ecosystem.