US (Fabian 2018). Six, however, are preeminent: Alphabet (Google’s parent company), Amazon, Apple, Facebook, IBM and Microsoft. These companies all exemplify what Tarleton Gillespie (2010) and Nick Srnicek (2016) respectively describe as ‘platforms’ or ‘platform capitalism’, a key feature of which is the digital gathering of big data generated by customers, be they users of search-engines, social media networks and video or music streaming services, or computer software or retail consumers. Access to such troves of data makes platform firms favourable sites for training ML systems.
IBM
Amongst these, IBM is in many ways an outlier, even though ‘Big Blue has a long record of interest in AI, stretching from its researchers’ involvement in the famous 1956 Dartmouth workshop to the triumph of its chess-playing Deep Blue over world champion Garry Kasparov in 1997 and its AI Watson, whose 2011 victory over human competitors in the television quiz show Jeopardy made it briefly the public face of the new generation of AI. Yet despite IBM’s \$15 million investment in the system, Watson has subsequently had only limited commercial success. While it has been described as ‘one of the most complete cognitive platforms available’ (Kisner, Wishnow and Ivannikov 2017: 1), and has been applied eclectically to commercial ventures in fields from fashion to telecommunications, IBM’s major emphasis was on potential uses in the highly profitable medical and health insurance sectors. In 2018, however, the company laid off many of the staff in this key division and announced it would be seeking new areas of focus. It is uncertain how far this setback was the result of technological failures and how much was due to the rigidities of IBM’s organizational practices (Strickland 2018). IBM is likely hampered in its AI efforts due to not possessing the large proprietary pools of big data necessary for training ML systems; instead IBM has to acquire it, expensively, by buying up smaller firms engaged in medical research and data collection (Kisner, Wishnow and Ivannikov 2017: 19–20). The other major US AI producers, however, do not suffer from this problem.
Alphabet (Google)
Alphabet has been harvesting user data and applying it to advance their AI projects for years, first by algorithmically improving search patterns and matching them with ad placements, and then using similar methods for categorizing, filtering and recommending video content on YouTube or predicting which apps users of its Android mobile phone operating system would purchase. Alphabet’s Google Brain unit is widely seen as the leading corporate ML research group. Between 2014 and 2018 Google bought up no less than 12 AI-related companies (Patrizio 2018), the most notable being DeepMind, which made the ML system AlphaGo that in 2016 scored an uncanny victory over the reigning human Go world champion, thus supplanting Watson as the poster-boy for AI. Such research connects not only with Google’s algorithmic online services and its Google Home devices but also with its Waymo autonomous vehicles unit and suite of robotics-related companies it acquired in the early 2000s. The development of AI is an endeavour fervently advocated by Google’s owners Sergey Brin and Larry Page as well as the transhumanist thinker Ray Kurzweil who is their ‘director of engineering’ (Simonite 2017); the combination of vast funds, deep expertise and ideological commitment places Google in an exceptional position in commercial AI research. Other US platform capitalists are, however, following similar paths.
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inhuman power