Despite this victory, we note that ‘Go’ in Katja Grace et al.’s (2018) list of AGI milestones has not been passed. This milestone is specifically listed as ‘Go (same training as human)’ (Grace et al. 2018: 747). While these shifting goalposts can be explained in terms of the AI effect, AlphaGo’s victory, while impressive, is less so when compared to how a human learns how to play the game. As Brenden M. Lake et al. (2017) point out, a human being can learn Go after playing just a few times, but for a ML system to do the same, it requires data from hundreds of thousands to millions of games. AlphaGo was trained on over 30 million games of Go, whereas Lee Sedol, has likely played only 50,000 games in his lifetime (Lake et al. 2017: 23). This difference emphasizes the major drawback of machine learning: the excessive amount of data it needs to do just one thing. On the other hand, human beings are able to ‘build rich causal models, make strong generalizations, and construct powerful abstractions’, all based on extremely limited data (Tenenbaum et al. 2011: 1279). Indeed, the capacity to generalize based on just a few examples may be at the core of general intelligence.
Some cognitive psychologists suggest that human beings generalize from limited data by using their imagination; a path towards AGI may thus lie in overcoming the problem of data gluttony by equipping systems with artificial imagination (Lapovsky 2014; Johnston 2008: 408; Ritter et al. 2017). Imagination is yet another factor that Marx argued marked labour as uniquely human: the human architect builds something in the mind before construction begins in the real world.
perfect machines, inhuman labour
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