The development of artificial intelligence is challenging our traditional notions of intelligence and cognitive abilities. This paper explores how we can reconceptualise our understanding of intelligence and superintelligence in the light of these developments and what implications this has for society, business and technology.
What is intelligence?
The conceptualisation of intelligence has a long and complex history. From Spearman’s g-factor (1904), which viewed intelligence as a single, measurable variable, to more modern theories such as Howard Gardner’s multiple intelligences (1983) and Robert Sternberg’s triarchic theory (1985), our understanding of intelligence has constantly expanded and differentiated.
More recently, two influential theories, the Integrated Information Theory (IIT) by Giulio Tononi (2004) and the Global Workspace Theory (GWT) by Bernard Baars and Stan Franklin (1988), have opened up new perspectives on the understanding of consciousness and intelligence. IIT postulates that consciousness is an intrinsic property of systems with high levels of integrated information (see also manuscript chapter 6.6.3 Hyperflux: Information frenzy as a catalyser; currently only available in German), which has implications for our understanding of artificial and biological intelligence (Tononi et al., 2016). GWT, on the other hand, proposes that consciousness emerges from a ‘global workspace’ (cf. resonance space) in which information becomes widely available and accessible, offering insights into the cognitive processes that enable intelligent behaviour (Dehaene et al., 2017). Both theories have the potential to expand our understanding of intelligence beyond traditional cognitive models and could have important implications for the development of artificial intelligence.
Henry Shevlin (2018) extends the traditional discussion of intelligence by establishing a close link between intelligence and consciousness. He argues that general intelligence in particular – as opposed to specialised intelligence – could be an important indicator of consciousness. Shevlin proposes a three-part framework to assess general intelligence: robustness (the ability to cope with tasks despite perturbations), flexibility (the ability to transfer knowledge to new tasks) and system-wide integration (the ability to integrate and balance inputs from different systems). This approach offers a new perspective on the assessment of intelligence, particularly in the context of consciousness research. Shevlin argues that many animals perform well in this framework, while current artificial systems show significant deficits in all three dimensions. This leads him to the conclusion that conscious artificial intelligence is probably still a long way off and that the way to get there is through the development of AI systems that are comparable to higher animals in terms of general intelligence.
Current concepts of superintelligence, as formulated by thinkers such as Nick Bostrom (2014), tend to view intelligence as a one-dimensional, scalable quantity. However, this view may be too simplistic and does not take into account the multiple forms and aspects of intelligence that can emerge in complex systems.
In many societies, intelligence is often equated with success, prosperity and wisdom. However, this association is culturally conditioned and possibly misleading. A study by Sternberg et al. (1981) showed that conceptualisations of intelligence can vary considerably between different cultures. It is therefore important to develop a broader, cross-cultural understanding of intelligence.
An expanded concept of intelligence is therefore proposed, which includes the following aspects:
a) Collective intelligence: The emergent cognitive abilities of groups and systems (Malone, 2018).
b) Augmented intelligence: The expansion of human cognitive abilities through technology.
c) Ecological intelligence: The ability to understand complex systems and interact with them sustainably.
d) Emotional and social intelligence: The ability to understand emotions and navigate effectively in social contexts.
This new conception of intelligence has the following implications:
a) Education: Education systems would need to be reformed to promote a broader range of cognitive skills.
b) Economy: Economic models could evolve from an approach focussed on individual performance to more collaborative forms.
c) Society: Social structures and values could shift to recognise collective and augmented forms of intelligence.
The implementation of collective intelligence models presents a number of challenges, as evidenced by the Chinese social credit system. This system, which may be regarded as a form of enforced ‘collective intelligence’, has resulted in considerable social pressure and negative psychological effects (Zhang & Norvilitis, 2002). It is therefore essential to give due consideration to ethical considerations and individual freedoms when developing collective intelligence models.
Therefore, a further differentiation of collective intelligence is proposed, which emphasises the following aspects:
a) Local autonomy and diversity of approaches
b) Voluntary participation and transparency
c) Adaptive systems and continuous learning
d) Education and empowerment for critical reflection
This differentiation is based on concepts such as Jim Rough’s ‘Wise Democracy’ (2002) and aims to promote collective wisdom through structured but open dialogue processes.
This new conceptualisation of intelligence has a direct impact on AI research and development:
a) Focus on collaborative AI systems that complement rather than replace human intelligence
b) Development of AI systems that integrate different forms of intelligence
c) Consideration of ethical and social aspects in AI development
d) Promotion of interdisciplinary research to expand our understanding of intelligence
The redesign of intelligence and superintelligence in the context of AI development offers new avenues for the conceptualisation of future technologies and societies. This necessitates a comprehensive approach that integrates technological advancements with ethical considerations and social requirements. Future research should concentrate on the ways in which diverse forms of intelligence—human, artificial, and collective—can be harnessed synergistically to address complex global challenges.
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