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Item type: Publication , Access status: Metadata only , Response to the inquiry into the use of generative artificial intelligence in the Australian Education system(Commonwealth Department of the Senate, 2023) Daniell, Katherine; Bell, Genevieve; Gould, Maia; Holt, Matthew; Meares, Andrew; Feldman, Hannah R.The emergence and adoption of new technologies and systems, such as Generative Artificial Intelligence (AI), will likely have a profound impact on education in Australia, transforming the way we learn, teach, engage and do research. It is important to be clear by what we mean by Generative AI. Here we want to build on the definition provided in the recent Rapid Response Information Forum report from the Office of the Chief Scientist of Australia. That report defined Generative AI as follows; “Whereas conventional AI has been largely analytic, generative AI takes its name from its capacity to generate novel content, as varied as text, image, music and computing code, in response to a user prompt.’’ 1 This definition suggests it is impossible to think about Generative AI without also thinking about AI more broadly. In defining AI, we would look to the 2019 Australian Council of Learned Academics (ACOLA) Report on The Effective and Ethical Development of AI for an appropriate definition. In the report, AI is defined as follows: ‘’Artificial intelligence can be understood as a collection of interrelated technologies used to solve problems that would otherwise require human cognition. Artificial intelligence encompasses a number of methods, including machine learning (ML), natural language processing (NLP), speech recognition, computer vision and automated reasoning.”’2 Taken together, these two definitions make clear that AI is not a singular thing, and as a result neither is Generative AI. Instead, we need to understand AI and Generative AI as complex dynamic systems that rely on a collection of technologies. What neither of these definitions illuminate, however, is that AI and Generative AI, also require data, human labour and a surprising collection of processes, regulations, and rules. As a result, the question of how Generative AI might be used in the Australian Education Sector is not really a question about the deployment and use of a single technology, but rather a question about a collection of technologies and the systems that might animate them. This makes answering the question a more nuanced and more subtle affair. In this submission, we recount our experiences as one of the newest academic units at the Australian National University (ANU). We have been delivering innovative and transformational educational experiences that engage critically and openly with AI, as both a subject of inquiry and a tool for learning. We believe our experiences are relevant and extensible to how we might engage with Generative AI. Based on our experiences, we believe there are optimistic scenarios in which emergent technologies more broadly, and AI systems more specifically, can be effectively used in a range of school and university settings.Item type: Publication , Access status: Metadata only , Agricultural Productivity in Indonesian Provinces(IGI Global, 2019-01-01) Tan, Khee Giap; Merdikawati, Nurina; Rajan, Ramkishen S.Indonesia has been recognized as a country with significant potential in agriculture, not only to be self-sufficient in terms of food, but also to be the "food basket" for the world. However, given limited and competing use of resources, raising agricultural productivity is of paramount importance. To date, most of the existing work on Indonesia's agricultural sector is at the national level. Considering the extent of Indonesia's regional diversity, a provincial-level analysis of the country's agricultural sector would be more useful from a policy perspective. In this light, this paper examines agricultural productivity growth in Indonesian provinces during 2000-2011 and draws policy implications from such empirical analysis. The paper uses two methodologies, namely growth accounting and Malmquist index data envelopment analysis. Results suggest that technological change has been improving for most provinces, though there is wide variation in technical efficiency change which in turn is driving differences in total factor productivity growth across provinces.Item type: Item , Access status: Open Access , Item type: Publication , Access status: Metadata only , A sign that spells: machinic concepts and the racial politics of generative AI(2024-12-31) Offert, Fabian; Phan, ThaoIn this paper, we examine how generative artificial intelligence produces a new politics of visual culture. We focus on DALL· E and related machine learning models as an emergent approach to image-making that operates through the cultural technique of semantic compression. Semantic compression, we argue, is an inhuman and invisual technique, yet it is still caught in a paradox that is ironically all too human: the consistent reproduction of whiteness as a latent feature of dominant visual culture. We use Open AI’s failed efforts to “debias” their system as a critical opening to interrogate how DALL· E dissolves and reconstitutes politically and economically salient human concepts like race. This example vividly illustrates the stakes of the current moment of transformation, when so-called foundation models reconfigure the boundaries of visual culture and when “doing” anti-racism means deploying quick technical fixes to mitigate personal discomfort, or more importantly, potential commercial loss. We conclude by arguing that it simply does not suffice anymore to point out a lack–of data, of representation, of subjectivity–in machine learning systems when these systems are designed and understood to be complete representations of reality. The current shift towards foundation models, then, at the very least presents an opportunity to reflect on what is next, even if it is just a “new and better” kind of complicity.Item type: Publication , Access status: Metadata only , From secrecy to dignity: trust and policy implications of shifting attitudes to privacy(ANU National Security College, 2019) Henschke, Adam; Young, Ryan; Gould, Maia; Woodford-Smith, Hannah