Making Sense of AI. Anthony Elliott. Читать онлайн. Newlib. NEWLIB.NET

Автор: Anthony Elliott
Издательство: John Wiley & Sons Limited
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Жанр произведения: Математика
Год издания: 0
isbn: 9781509548910
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retention rates and help create new job opportunities.

      Let us turn now to contrast two transformationalist interventions which centre upon the problem of work and employment. The first is Klaus Schwab’s The Fourth Industrial Revolution, issued by the World Economic Forum (of which Schwab is executive chairman). The second is Bernard Stiegler’s Automatic Society, volume 1 of which is subtitled The Future of Work. There is a telling feature about the writing of Klaus Schwab that several critics have noted, and which pertains to the underlying ardour of his transformationalist stance. Schwab makes it abundantly clear that the AI transformation in manufacturing and services is already well under way. The digital revolution, he contends, is producing ‘exponential disruptive change’, and this can be discerned in the prevalence of advanced robotics, machine learning, big data and supercomputers in business and organizational life today. The scope and scale of the digital revolution for Schwab – what he terms the ‘fourth industrial revolution’ – are ‘unlike anything humankind has experienced before’.11 Yet if Schwab’s transformationalism is clearly evident in this diagnosis of our times, his critique of the consequences of AI appears (at least on an initial reading) as scrupulously non-judgemental. Employment is a signal example. Schwab contends that AI ushers in massive efficiency gains and cost reductions for businesses and industry, but also highlights the massive automation of jobs stemming from these very developments. On the one hand, he emphasizes that technological innovation today destroys jobs as never before, whilst on the other hand he underscores that AI unleashes a new era of prosperity through the creation of novel employment opportunities and future industries. He argues that AI disrupts labour markets and workplaces around the world, and yet emphasizes the ability of workers in the new economy to adapt continuously and fashion new skills through lifelong learning.

      The digital mindset, capable of institutionalizing cross-functional collaboration, flattening hierarchies, and building environments that encourage a generation of new ideas, is profoundly dependent on emotional intelligence . . . The world is fast changing, hyper-connected, even more complex and becoming more fragmented but we can still shape our future in a way that benefits all. The window of opportunity for doing so is now.12

      In the end, AI for Schwab is an exhilaratingly progressive affair. He argues that AI has the potential to be institutionalized as a global, cosmopolitan form of life, one to be celebrated rather than castigated.

      If for Stiegler Google Translate represents destructive linguistic entropy, the algorithmic automation of society signals massive economic entropy. AI makes it possible not just to economize upon labour, but to fully automate tasks and thus render employees redundant. This is a redundancy of the worker’s expertise, as advanced automation for Stiegler produces a generalized (economic as well as environmental) ‘disorder of hyper-standardization’ – where work, and the value of employees, are determined by calculating probabilities based upon averages. Today’s industrial capitalism, writes Stiegler, is ‘an era in which calculation prevails over every other criteria of decision-making, and where algorithmic and mechanical becoming is concretized and materialized as logical automation and automatism . . . as computational society becomes a society that is automated and remotely controlled’.14 We are at the beginning of a process of technological transformation that will have a massive impact upon the nature of work, expertise and knowledge – the algorithmic governmentality of 24/7 capitalism, according to Stiegler, will precipitate ‘entropic catastrophe’.