The development of Artificial General Intelligence (AGI) represents a milestone in the history of technology. This paper explores the many aspects of AGI research, from its theoretical foundations to current research landscapes and ethical challenges. (Summary of the manuscript chapter 3, General Artificial Intelligence – an insight)
Definition and concept of AGI
AGI is defined as ‘a machine intelligence that is capable of understanding and learning any intellectual task that can be performed by a human being’ (manuscript, p. 264). In contrast to weak AI, AGI strives for a ‘comprehensive, flexible intelligence that enables it to think, learn and be creative independently’ (p. 264).
Historical development
The idea of artificial intelligence dates back to antiquity, as the myth of Talos shows. However, the modern era of AI research only began in the middle of the 20th century. A decisive moment was the Dartmouth Conference in 1956, which ‘is often described as the birth of AI research’ (p. 266).
Technological foundations and challenges
Machine learning and deep learning are at the heart of modern AI development. Despite impressive progress in some areas, fundamental limitations remain. The author emphasises: ‘The lack of generalisation ability contrasts with the human ability to seamlessly apply what has been learned to novel contexts’ (p. 268).
New research approaches
Innovative approaches are being researched to overcome these challenges. These include ‘differentiable neural computers’ and ‘probabilistic programming’ (p. 268). These could lead to a ‘more causal and robust modelling of knowledge than the purely correlation-based approach of current deep learning systems’ (p. 268).
Global research landscape
AGI research is a global endeavour. Leading players such as OpenAI, DeepMind and Meta are driving development. OpenAI, for example, is ‘working to develop guidelines and standards to ensure that AGI technologies are used in the best interests of humanity’ (p. 277).
Ethical challenges and governance
The development of AGI raises important ethical questions. A key concern is ‘that AGI systems that outperform human intelligence could get out of control and make decisions that are not in line with human values and interests’ (p. 279). To address this, various governance models are discussed, including the development of a ‘constitutional AI’ and international regulatory frameworks such as the EU AI Act.
Social impact
The potential impact of AGI on society is far-reaching. The author predicts: ‘AGI will lead to a massive restructuring of the labour market’ and ‘personalised learning by AGI tutors will revolutionise the educational landscape’ (p. 280). AGI will also ‘fundamentally question our understanding of intelligence, consciousness and the nature of being human’ (p. 281).
Conclusion
The development of AGI is not only a technological challenge, but also a social one. As the author points out, ‘it will be crucial to engage in a broad societal dialogue to shape the development and implementation of AGI in a way that serves the well-being of all humanity’ (p. 281). The way in which we develop, deploy and regulate AGI will shape our future.
This article illustrates the complexity and multi-layered nature of AGI development. It emphasises the need for interdisciplinary research and international cooperation in order to overcome the technological challenges and at the same time take appropriate account of ethical and social implications.