{"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":123,"pdf_page":131,"physical_index":131,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"dba8a107e8b7e2eb47729251f5f0d93fd38ff3cd384925e1450d0c108b4db3dd"},"chapter":"Digital Barricades: Interventions in Digital Culture and Politics","content":"What Vicarious has referred to (or slyly marketed) as imagination is more generally referred to in the AI community as ‘one-shot learning’, which is a human capability enabled, at least in part, by inductive biases. One-shot learning refers to a human child or ML system being shown a single labelled example and from this being able to learn how to identify other examples of the same category of objects, be it cats, letters or whole words, by relying on various innate inductive biases. For example, the typical human two year old can learn and start to use a new word like ‘cat’, and, after seeing just one example, can recognize a cat from various angles, cats of different colours and sizes and in various poses, and so on. For a ML system to recognize cats in photos, it has to be shown millions of examples of cats from different angles, of different shapes, sizes, materials, colours, and so on (Tenenbaum et al. 2011: 1279; Lake et al. 2017: 22). Similarly, after playing just one or a couple of games of Go, almost any human would have learned how to play the game and could also generalize this knowledge to play one of the many variants of Go with little difficulty, but for AlphaGo to do the same would ‘require significant reprogramming and retraining’ (Lake et al. 2017: 23). Researchers from DeepMind have found that the one-shot learning models of matching neural networks trained on ImageNet also exhibit biases towards shape, although the magnitude of the bias depends on the particular neural architecture (Ritter et al. 2017).<sup>14</sup> Through this inductive bias the neural network learns how to learn categorization or how to be a classifier, i.e. an algorithm that implements classification.\n\nVicarious simulated inductive biases with a ‘structured probabilistic generative model framework’ on what they call a Recursive Cortical Network (RCN). Generative models produce representations or abstractions from observed or learned phenomena based on a probabilistic model. To explain using Marx’s architect, if she wanted to build a honeycomb-like structure, this architect would have observed honeycombs in a bee’s nest; the memory of these honeycombs would be the architect’s generative model in her mind, from which she can generate (i.e. imagine) many different honeycomb architectural drawings. In AI, such generative models are first created based on the patterns it has recognized in the data and are then used to predict probabilities from this model, such as producing new variations of the data it has been trained on. In the case of Vicarious’ RNC, its inductive bias enabled it to learn the contours and surfaces of alphabetic letters based on little training data and then, in turn, break CAPTCHAs by generatively segmenting characters (George et al. 2017). This generation can be understood as an artificial imagination because segmenting letters in this way is akin to imagining what they would look like in different shapes, orientations and positions or without a particular background or distortion. In this case, imagination appears to be connected not only to an act of creation, but also to adapting to different situations or noisy contexts. For Vicarious, the end goal of equipping AI with an imaginative capacity is to enable it to transfer learning to a completely different domain. Importantly, this transfer can be understood as adaptability, and this word is what AI researchers use to explain how AI can become generally intelligent and/ or more human-like (Alpaydin 2016: ix, xii, 17, 24; Kaplan 2016: 49–50).\n\nperfect machines, inhuman labour\n\n123","previous_page":122,"next_page":124}