TOWARDS AGI
Ethem Alpaydin states that he would not be surprised if a system ‘reaches the level of human intelligence’ through machine learning (2016: xii). Such a view is shared by, among others, Andrew Ng, who advocates the view that ‘human intelligence boils down to a single algorithm’ and believes that deep learning could, if not solve the problem of emulating human intelligence, at least come closer to achieving this goal than anything that has been tried before (Domingos 2015: 117). Similarly, Domingos identifies five main algorithmic ‘tribes’ in machine learning, but asserts that the goal is to unify ‘the key features of all of them’ into ‘the ultimate master algorithm’ (2015: xvii), and declares that AI exceeding human intelligence will be reached soon after this goal is reached (2015: 286).
Such narrow AI approaches to general intelligence can be understood as viewing intelligence as a ‘toolbox’ containing mostly unconnected tools. But connecting them may be difficult due to each tool having been built ‘according to very different theoretical considerations’, thus to ‘implement them as modules in a big system will not necessarily make them work together, correctly and efficiently’ (Wang and Goertzel 2007: 6). While recognizing the possibility of an integrative approach, the AGI community disputes the possibility of general intelligence emerging out of narrow approaches or that AGI could even bridge the gap between various narrow AIs (Wang and Goertzel 2007: 6; Goertzel 2014). As Goertzel argues, the ‘AGI approach takes “general intelligence” as a fundamentally distinct property from task or problem specific capability, and focuses directly on understanding this property and creating systems that display [it]’ (Goertzel 2014: 2). Recognizing that both the narrow AI and AGI fields of research agree that learning is a key aspect of intelligence, Wang and Goertzel point out that
perfect machines, inhuman labour
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