1. First stage: Specialised AI agents (analogous to neuron groups/people groups)
At this stage, we can consider specialised AI agents, such as Large Language Models (LLMs), as analogous to groups of neurons or specialised groups of people.
Characteristics:
a) Specialisation: Each agent is specialised in certain tasks (e.g. speech processing, image recognition).
b) Basic interactions: Agents can exchange simple information or collaborate.
c) Limited scope: The capabilities are restricted to specific domains.
Examples:
- GPT-3 for text generation
- DALL-E for image generation
- AlphaFold for protein folding prediction
Analogy to the first stage of the brain-social analogy:
Similar to neuron groups or specialised teams in companies, these AI agents work together in limited areas without having a comprehensive ‘understanding’ of the overall system.
2. Second stage: Integrated AI systems (analogue to cortical columns/companies)
At this level, we are looking at larger, integrated AI systems that combine several specialised agents.
Characteristics:
a) Functional integration: Various AI capabilities are brought together in one system.
b) Multimodal processing: Ability to process and integrate different types of input (text, image, audio).
c) Emergent capabilities: Integration creates new capabilities that go beyond the sum of the individual components.
Examples:
- OpenAI’s GPT-4, which can process text and images
- Google’s PaLM, which integrates different modalities
- DeepMind’s Gato, a generalist AI system
Analogy to the second stage of the brain-social analogy:
These integrated systems are similar to the function of cortical pillars or divisions, which combine different functions under one roof and can therefore handle more complex tasks.
3. Third stage: Interaction of large AI systems (analogue to brain regions/countries)
At this stage, we are looking at the interaction of various large AI systems, possibly as a precursor to a form of artificial superintelligence.
Characteristics:
a) Cross-system communication: Different AI systems exchange information and findings.
b) Global optimisation: Ability to solve complex problems through collaboration between different systems.
c) Emergent intelligence: Potential for the emergence of a superordinate, emergent intelligence.
Examples:
- Hypothetical networking of AI systems from various tech giants
- Global AI networks to solve complex problems (e.g. climate modelling, pandemic control)
- Concepts such as the ‘global brain’ in the AI version
- Vision of a singularity
Analogy to the third stage of the brain-social analogy:
This stage is similar to the synchronisation of different brain regions or the global cooperation of countries, whereby a form of ‘collective AI consciousness’ could possibly develop.
Discussion:
1. Strengths of the analogy:
a) Scalability: The analogy can be easily applied to different levels of complexity of AI.
b) Emergence: In all three levels, we can observe emergent properties that arise from the interaction of simpler components.
c) Integration potential: The analogy shows ways in which AI systems could be increasingly integrated and networked.
2. Challenges and limits:
a) Consciousness and intentionality: It remains unclear whether AI systems will ever develop a consciousness comparable to that of humans.
b) Ethical implications: The development of highly integrated AI systems raises significant ethical issues, especially at the third stage.
c) Control and governance: As AI systems become more complex and autonomous, issues of control and governance become increasingly important.
3. Implications and research questions:
a) How can we develop mechanisms to promote and control the ‘synchronisation’ between different AI systems?
b) What role do human developers and users play in this process of increasing AI integration?
c) How can we ensure that the development of integrated AI systems is ethical and for the benefit of humanity?
d) What new opportunities arise from the synchronisation of different AI systems for solving complex global problems?
4. Theoretical embedding:
This analogy resonates with various concepts from AI research and futurology:
a) Artificial General Intelligence (AGI): The idea of an AI that achieves human-like generality could be seen as an intermediate stage between the second and third stages of our analogy.
b) Singularity: The concept of technological singularity, as described by Kurzweil (2005), could be seen as an extreme manifestation of the third stage [1].
c) Swarm intelligence: Concepts of swarm intelligence, as discussed by Bonabeau et al. (1999), could provide insights into the synchronisation of AI systems [2].
Summary:
Extending our analogy to the field of artificial intelligence offers insights into possible development paths for AI. It shows parallels between biological, social and artificial systems and opens up new perspectives for the design and integration of AI systems. At the same time, it makes it clear that with the increasing complexity and integration of AI systems, new ethical, governance-related and philosophical questions arise that need to be carefully addressed.
This approach is particularly valuable for developing strategies for the responsible development and integration of AI systems and for anticipating potential future challenges and opportunities in the field of artificial intelligence.
[1] Kurzweil, R. (2005). The singularity is near: When humans transcend biology. Penguin.
[2] Bonabeau, E., Dorigo, M., & Theraulaz, G. (1999). Swarm intelligence: from natural to artificial systems. Oxford university press.