RE: https://mastodon.social/@nixCraft/116188939207308679
“It [Oracle] is also exploring an arrangement called “bring your own chip” (BYOC), where new customers will be required to supply their own hardware, shifting capital requirements off Oracle’s books.”
The #CriticalAI Guest Forum welcomes op-ed from Ricky D. Crano, a Film and Media Studies Lecturer at UC Irving, on disturbing “AI”-related institutional changes in higher ed
Critical AI’s Guest Forum welcomes writers on topics of potential interest to our readers inside and outside of the academy. Below, an op-ed from Ricky D. Crano (Film & Media Studies, UC Irvin…
Wow, we are very excited about this.
Karen Hao's work in this area CANNOT be missed: https://x.com/_KarenHao/status/1834419437707162068
#ClimateCrisis #AIhype #CriticalAIstudies #criticalAIiteracies
Today the real JANE ROSENZWEIG talked about the future of undergraduate research in the era of Generative AI! Thread below about the event.
Many thanks to Duke University Press over on Twitter, who have published this Q&A with our editor Lauren M. E. Goodlad on their blog: https://dukeupress.wordpress.com/2024/02/22/qa-with-lauren-m-e-goodlad-editor-of-critical-ai/
Another important article today by @billyperrigo. Check it out here: https://time.com/6684266/openai-democracy-artificial-intelligence/ Some commentary follows
A thread 🧵 /1
All of these tests in the medical domain have a striking similarity about them: an LLM does better on a standardized test than first-year med students; or a bot does better on a texting situation than real doctors who don't actually practice medicine that way.
More grist for the industry's mill as it makes the case that this flawed, exploitative, and environmentally irresponsible technology will benefit humanity. /3
A really great piece about women and erasure in the AI ethics space that gives them the recognition they deserve. A great read!
This convergence of events: the week at OpenAI and the dreaded New York Times article shed light on a disconcerting reality—the increasing marginalization of women in artificial intelligence, a glaring lack of recognition and lack of respect for their work that is emblematic within both industry and media.
From @Mer__edith, posted initially on X/Twitter:
"This paper is really important, presenting empirical evidence of the imbrication bet. AI & the surveillance biz model. This is notable particularly given that most production surveillance tech is proprietary, its existence and use hidden from the public."
Read it here: https://arxiv.org/abs/2309.15084?ref=404media.co
A rapidly growing number of voices argue that AI research, and computer vision in particular, is powering mass surveillance. Yet the direct path from computer vision research to surveillance has remained obscured and difficult to assess. Here, we reveal the Surveillance AI pipeline by analyzing three decades of computer vision research papers and downstream patents, more than 40,000 documents. We find the large majority of annotated computer vision papers and patents self-report their technology enables extracting data about humans. Moreover, the majority of these technologies specifically enable extracting data about human bodies and body parts. We present both quantitative and rich qualitative analysis illuminating these practices of human data extraction. Studying the roots of this pipeline, we find that institutions that prolifically produce computer vision research, namely elite universities and "big tech" corporations, are subsequently cited in thousands of surveillance patents. Further, we find consistent evidence against the narrative that only these few rogue entities are contributing to surveillance. Rather, we expose the fieldwide norm that when an institution, nation, or subfield authors computer vision papers with downstream patents, the majority of these papers are used in surveillance patents. In total, we find the number of papers with downstream surveillance patents increased more than five-fold between the 1990s and the 2010s, with computer vision research now having been used in more than 11,000 surveillance patents. Finally, in addition to the high levels of surveillance we find documented in computer vision papers and patents, we unearth pervasive patterns of documents using language that obfuscates the extent of surveillance. Our analysis reveals the pipeline by which computer vision research has powered the ongoing expansion of surveillance.
Louis-Olivier Brassard
Anna 👩🏻💻
CriticalAI
