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Short abstract:

AI as a techno-cultural object is the site of conflictual interpretations on the material implications of AI research. Results of the Shaping 21st Century AI project reveals a latent discontent in the Canadian research community around a form of industrial gatekeeping translating in GPU hoarding.

Long abstract:

The 21st-century paradigm of machine learning (ML) artificial intelligence (AI) (Roberge & Castelle, 2021), ignited by the crowning of neural networks (NN) in the image recognition contest of ImageNet, prompted a certain re-engineering of research practices in the field computer science. The “paradigmation” of ML and NN unleashed new possibilities for model scaling based on “increased computation” through graphical processing units (GPUs) (Sutton, 2019, p.2), coupled with massive “resource allocations” (Roberge & Castelle, 2021) and data harvesting (Cardon & al., 2018). This upscaling in the means of computation contributed to the publicization of the now (un)famous conversational large language model (LLM) Chat-GPT.

Shifting from the outputs of LLMs to demystify (Roberge & al., 2020) and study AI’s “thingness” (Suchman, 2023), we ask ourselves not “can language models be too big?” (Bender & al., 2021) but rather “how large models transforms computer science’s research practices”? The Shaping AI Canadian team aimed to revigorated the study of AI laboratory ethnographies (Forsythe, 1999; Hoffman, 2017; Seaver, 2022) through 20 interviews and uncovered, in a serendipitous way, the existence of a “shadow controversy” amongst labs located in the non-funded Canadian research circuits.

The issue seems to oscillate in the inability to access sufficient computing power to do research and the current hype on large models that contributes to a devaluation of fundamental research and toy problem experimentation. GPU concentration the industry is denounced as a gatekeeping that shapes the legitimity of “good and bad” AI research.