Let us reiterate: as of 2019, AGI does not exist. Some doubt that such machine intelligence is even possible (Dreyfus 1972; Braga and Logan 2017; Nadin 2018). Within the AI community, however, many consider AGI to be possible at least in theory and some argue that it ‘is merely a very difficult engineering problem’ (Pennachin and Goertzel 2007: 1). At the very least, AGI is considered sufficiently plausible to attract investments for research and business ventures.
Within the larger AI community, AGI researchers form a very small minority. The narrow AI industry dwarfs the AGI one in terms of the number and size of corporations and research institutions, and projects and funding. Baum’s 2017 survey of active AGI projects gives an overview of the political economy of AGI as an industry. This survey identifies a total of 45 active AGI projects<sup>5</sup> in 30 countries worldwide, with most of them being ‘based in major corporations and academic institutions’ and a smaller number based in public corporations, nonprofits or governmental institutions.<sup>6</sup> While ‘some ... are large and heavily funded’, most of them are small to medium in scale (Baum 2017: 2). AGI research is characterized by having a large number of open-source projects, with a total of 25 projects having made their source code available online. While only nine of the AGI projects have identifiable military connections, only four were identified as clearly having no military connections with the rest being unclear. Most AGI projects are based in the US or within its sphere of influence, and the only ones that are not are based in Russia and China (Baum 2017: 2).
The largest AGI projects are Alphabet-Google’s DeepMind, the Elon Musk-backed Open AI, and the Human Brain Project, while other notable projects include Vicarious FPC, the Microsoft acquisition Maluuba, Open Cog, Uber AI, and Nnaisense. According to Baum, there are two trends among the for-profit corporations: either to support ‘long-term AGI R&D in a quasi-academic fashion, with limited regard for short-term profit or even any profit at all’, or to undertake ‘AGI projects delivering short-term profits for corporations while working towards long-term AGI goals’ (2017: 19) The latter trend is apparent in corporations like Microsoft, Google and Uber; Baum argues that if ‘this synergy between short-term profits and long-term AGI R&D proves robust, it could fuel an explosion of AGI R&D similar to what is already seen for deep learning’ (2017: 19). Based on Baum’s survey, it appears that the current state of AGI research is still primarily, as Pei Wang and Goertzel argued a decade prior, ‘producing publications and preliminary results’ (2007: 3).
perfect machines, inhuman labour
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