‘Dream a little before you think’ : an open invitation to reconsider human agency and the purpose of arts and humanities higher education through the labour of radical imagination
Following the long inertia of Enlightenment influence and decades of neoliberal propaganda, generative AI is hailed as the most recent revolutionary force that is prophesied to redefine not only what education and knowledge production mean, but also what human civilization could potentially look like. This thesis critically examines the ideological narratives and forces that propel the “inevitability” of deploying generative artificial intelligence in higher education. Building on Theodor Adorno and Max Horkheimer’s foundational critique of technological progress, Walter Benjamin’s examination of historical progress, and Hannah Arendt’s analysis of human agency, it challenges the linear, technologically solutionist vision of social and human progress. Inspired by Toni Morrison’s instruction on the purpose of humanities education and Ruha Benjamin’s elaboration on technological justice through the framework of radical imagination, this thesis argues that to reveal alternative educational futures – ones that aren’t grounded in massive ecological, economic, cognitive, social, and cultural dispossessions – will require a revolution of imagination.
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations of such models.
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.
Empirically, PRISM reduces the end-to-end time for data selection and model tuning to just 30% of conventional pipelines, and achieves this efficiency while simultaneously enhancing performance, surpassing models fine-tuned on the full dataset across eight multimodal and three language understanding benchmarks.
Jinhe Bi, Yifan Wang, Danqi Yan et al.· arXiv.org· 73 citations· ⚡4
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
This paper designs Markov decision processes (MDPs) for different combinatorial problems and proposes to train conditional GFlowNets to sample from the solution space and demonstrates that GFlowNet policies can efficiently find high-quality solutions.
Dinghuai Zhang, H. Dai, Esmeralda S. Whitammer et al.· Advances in Neural Informati...· 59 citations· ⚡8
The professor of physics and inaugural director of the NSF AI Institute for Artificial Intelligence and Fundamental Interactions will lead LNS and continue his research in particle physics.
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