{"resolved":true,"match_type":"uuid","source_type":"original_book","book_id":"a2eab101-adfc-4a0d-8142-63a4d11cee02","title":"人工智能和资本主义的未来","author":null,"edition":null,"publisher":null,"year":null,"isbn":"9780745338613","requested_by":"printed_page","book":{"source_type":"original_book","book_id":"a2eab101-adfc-4a0d-8142-63a4d11cee02","id":"a2eab101-adfc-4a0d-8142-63a4d11cee02","uuid":"a2eab101-adfc-4a0d-8142-63a4d11cee02","title":"人工智能和资本主义的未来","normalized_title":"人工智能和资本主义的未来","author":null,"edition":null,"publisher":null,"year":null,"isbn":"9780745338613","isbns":["9780745338613","9780745338606","9781786803955","9781786803979","9781786803962"],"parsed":true,"ai_readable":true,"document_type":"book","page_count":225,"navigation_url":"https://workshop.closeclaw.top/ai/book/a2eab101-adfc-4a0d-8142-63a4d11cee02","title_url":"https://workshop.closeclaw.top/ai/title/%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD%E5%92%8C%E8%B5%84%E6%9C%AC%E4%B8%BB%E4%B9%89%E7%9A%84%E6%9C%AA%E6%9D%A5","fixed_page_url_template":"https://workshop.closeclaw.top/ai/book/a2eab101-adfc-4a0d-8142-63a4d11cee02/page/{printed_page}","path_api_url_template":"https://workshop.closeclaw.top/api/ai/book/a2eab101-adfc-4a0d-8142-63a4d11cee02/page/{printed_page}","read_url":"https://workshop.closeclaw.top/api/ai/read/a2eab101-adfc-4a0d-8142-63a4d11cee02","page_url":"https://workshop.closeclaw.top/api/ai/books/a2eab101-adfc-4a0d-8142-63a4d11cee02/page","search_url":"https://workshop.closeclaw.top/api/ai/books/a2eab101-adfc-4a0d-8142-63a4d11cee02/search"},"page":{"printed_page":53,"pdf_page":61,"physical_index":61,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"2994bd650e20242fd07e9ea300e25b65984583fe28067baf2d15e198cbf0a5ae"},"chapter":"Digital Barricades: Interventions in Digital Culture and Politics","content":"We’re going to infuse every application that we interact with, on any device, at any point in time, with intelligence.\n\nWe’ll make these same intelligent capabilities that are infused in our own apps – the cognitive capabilities – available to every application developer in the world.\n\nWe’re building the world’s most powerful AI supercomputer and making it available to anyone, via the cloud, to enable all to harness its power and tackle AI challenges, large and small. (Microsoft n.d.)\n\nMicrosoft imagines a world submerged in AI that is nothing short of techno-animistic: ‘As we infuse intelligence into everything, whether it’s your keyboard, your camera, or business applications, we are essentially teaching applications to see, to hear, to predict, to learn and take action.’ Other tech giants have used the same terminology. Guy Ernest from Amazon describes Amazon Web Services as ‘democratizing AI’ by making their AI tools available for ‘any team size and skill, and for every use case’. The very same phrase has been uttered by Fei Fei Li, Google’s Chief Scientist of the Cloud and ML, and Michael Marin, a senior executive at IBM Internet of Things (Greene 2018; Simpson 2018). ‘Democratizing’ AI thus means generalizing both its deployment and the tools for creating it, making it increasingly available to end-users and allowing anyone, working in any field, even those without any AI training, to develop AI.\n\nOne of the most significant axes of the ‘democratization’ programme might seem to contradict the AI industry’s profit orientation. The AI industry is characterized by a large and vigorous open-source community, in which tools and templates for making AI are freely distributed, projects are undertaken by cooperative online programming collectives, and products are released gratis for general use. Nearly all of the tech giants have open-sourced some of their AI-related materials (Simonite 2015; Crosby 2018). In 2015, Google released TensorFlow, a library of tools for deep learning programming, under the Apache 2.0 open-source licence, and it is now widely used. Other tech giants, seeing Google’s success, followed suit. In 2017, Facebook opened several of its libraries, pre-trained models and data sets including its Caffe2 and PyTorch frameworks (Arakelyan 2017). Microsoft’s CNTK, Baidu’s Warp-CTC, and Amazon’s DSSTNE (the AI framework that powers its product recommendation system) are now all freely available and can be used to produce industry-grade AI (Stone 2016; Finley 2016). In addition, there are many other open-source AI projects not derived from the tech giants (Harvey 2017). Almost all AI projects today rely on such open-source toolkits. This is a significant milestone, we suggest, for AI becoming part of the general conditions of production.\n\nmeans of cognition\n\n53","previous_page":52,"next_page":54}