{"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":76,"pdf_page":84,"physical_index":84,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"fd245e4868da732006b9aade268db51fb1102eebe36840abdc58538100deca93"},"chapter":"Digital Barricades: Interventions in Digital Culture and Politics","content":"dependence on highly trained software engineers and programmers) each has its particularities. Here we focus on ML, the leading form of AI in the second decade of the twenty-first century.\n\nCatherine Dong (2017) explains that ‘Machine learning engineering happens in three stages: data processing, model building and deployment and monitoring.’ The ‘meat’ in this ‘sandwich’, she says, is the building of the model. This is where software engineers develop the algorithms that recognize given input data, be it digital images of cats, or of pedestrians crossing streets, or configurations of search terms, or purchase records, or social media topics. This learning process is tested, tweaked and improved over thousands or millions of training examples, and then further tested on raw data, where the pattern signal is mixed with noise (images of cats with those of dogs, antelopes and elephants; pedestrians alongside wheelbarrows, letter boxes and unicyclists, and so on). At the end of this process the AI should be capable of identifying its target object with an acceptable level of confidence and of establishing statistical correlations between different patterns (more pedestrians crossing streets in daytime than at night; cat images posted more frequently by women than men, etc.). It is these capacities that give ML its uncanny, inhuman ability to predict associations and outcomes.\n\nThere are many techniques to construct algorithmic models – ‘linear/ logistic regression, random forests and boosted decision trees’ – familiar to computer scientists for some time (Dong 2017). These are used to create the friend-suggestions, ad-targeting, recommendations and search rankings that are part of everyday internet use. However, the most sophisticated ML modelling involves ‘deep neural networks’ that are ‘hard to train ... require[ing] more time and computational power’ and ‘a combination of intuition and trial and error’ (Dong 2017). While courses and do-it-yourself instructions in ML are proliferating, and libraries of open-source tools widely available, deep expertise with neural networks is rare; capable computer scientists may be numbered only in the thousands worldwide (Economist 2017b). This accounts for the intense corporate bidding wars for such talent and the high salaries and benefits it can command. ML thus intensifies the basic software development labour process in which core activities are conducted by small groups of highly skilled techno-scientific workers.\n\nWhile the ‘meat’ of ML is the making of the algorithmic model, it is ‘the bread of the sandwich process’ – what happens before and after training the algorithm – ‘that holds everything together’ (Dong 2017). These two layers of the ML process involve, at one end, ‘cleaning and formatting vast amounts of data to be fed into the model’ and, at the other, ‘careful deployment and monitoring of the model’. As Dong observes: ‘Any incompatibility from any stage of the ML development process – from data processing to training to deployment to production infrastructure – can introduce error’ (2017). In practice, ML software engineers often spend more time dealing with ‘preparing and monitoring’ than in actual model making. Later in this chapter we will address the sources of such data sets, from unknowing mobile phone users to closely monitored distribution centre workers and the sleepless ‘safety drivers’ in driverless vehicles. But regardless of how data is collected, it nearly always has to be cleaned for use in AI training: standardizing its format, eliminating errors, detecting outliers, supplying additional information, adding features used in processing and above all labelling objects that ML will learn to recognize. Amongst data scientists, jokes such as ‘80% of the job is cleaning data and the other 20% is complaining about how the data has been cleaned’ abound (Lohr 2014).\n\n76\n\ninhuman power","previous_page":75,"next_page":77}