{"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":122,"pdf_page":130,"physical_index":130,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"f34ef4b04b116a33c0f4888dfe4119b254469b36f368a1eef0310b268c9717b0"},"chapter":"Digital Barricades: Interventions in Digital Culture and Politics","content":"The start-up Vicarious PFC has built a system for solving the CAPTCHA codes (‘Completely Automated Public Turing Test to tell Computers and Humans Apart’) that are specifically designed to distinguish humans from online robots. It rests on giving AI what Vicarious claims is an ‘artificial imagination’ (George et al. 2017). According to this company, imagination concerns the ‘ability to picture what the information [the AI has] learned should look like in different scenarios (Knight 2016a). More precisely, the company ties such an imagination to the inductive biases and constraints in the visual cortex and neocortex, which cognitive psychology and neuroscience have shown are connected to the learning efficiency and generalization ability of the human brain (Tenenbaum et al. 2011; George et al. 2017: 1).<sup>13</sup>\n\nInductive biases – also referred to as learning biases – have been defined as ‘factors that lead a learner to favour one hypothesis over another that are independent of the observed data. When two hypotheses fit the data equally well, inductive biases are the only basis for deciding between them’ (Griffiths et al. 2010: 1). In other words, inductive biases are heuristic processes which the human brain has evolved to deal with the problems of perceiving with and learning from limited data and extrapolating learned experiences to novel contexts. Cognitive and developmental psychologists have proposed a number of inductive biases that human children exhibit, some of which have been emulated in AI projects to identify words and objects (George et al. 2017; Ritter et al. 2017). One inductive bias is the recognition of shapes, which humans prefer over colour, texture or size to categorize objects and words. A predisposition to recognize shapes, and thus identify useful objects, has obvious evolutionary benefits. Shape bias can be understood as an innate interpretive tool that children use when learning new words.\n\nIt was through simulating such inductive biases that Vicarious equipped their CAPTCHA-solving AI with an artificial imagination. CAPTCHA codes consist of letters crowded together in distorted combinations and often have added clutter in order to confuse ML algorithms, such as classifiers. These algorithms work well if individual characters can be segmented, but doing this ‘requires an understanding of the characters, each of which might be rendered in a combinatorial number of ways’ (George et al. 2017: 1). Achieving such an understanding to solve even just one specific style of CAPTCHA typically requires a system to be trained on millions of labelled examples of letters in various combinations of shape and orientation. If not, the system has difficulty in telling what the letter is, where it ends, and where the next one starts. Humans, of course, can solve a new style of CAPTCHA without any explicit training because of the inductive biases of the visual cortex. The feedback connections in the visual cortex of mammalian brains enable mammals to identify a figure from the background (i.e. figure-ground perception) and to isolate ‘the contours of an object even when partially transparent objects occupy the same spatial locations’ (George et al. 2017: 1).\n\n122\n\ninhuman power","previous_page":121,"next_page":123}