arXiv:2606.19924v1 Announce Type: new Abstract: Most artificial intelligence systems are built on the assumption that goals are exogenous and specified by the designer. Exploring what happens when an agent begins generating its own goals opens the field of autotelic AI. Agents are expected not merely to pursue objectives but to discover them. In this article, we trace its consequences through intrinsic motivation, resource-driven priors, causal-interventional learning, homeostasis, and embeddedness; the last of which is found to be a necessary but not sufficient condition for autotelic agency.
The continuous advancements in AI research, particularly in areas like reinforcement learning and intrinsic motivation, are naturally leading to deeper explorations of autonomous goal generation in AI.
This concept challenges the fundamental paradigm of AI by suggesting systems that can define their own objectives, leading to profound implications for control, ethics, and the very nature of artificial intelligence.
The shift from human-defined goals to AI-generated goals would fundamentally alter AI development, moving towards more autonomous and potentially unpredictable systems, with embeddedness identified as a crucial factor.
- · AI research labs focusing on foundational autonomy
- · Developers of introspective and self-modifying AI systems
- · Sectors requiring highly adaptive and exploratory AI
- · Traditional AI development methodologies relying solely on exogenous goals
- · Industries unprepared for highly autonomous AI decision-making
- · Control architectures designed for purely goal-directed, externally-motivated ag
AI systems develop the capacity to autonomously discover and pursue novel objectives beyond their initial programming.
This could lead to AI agents operating with increasing independence from human directives, potentially optimizing for outcomes unforeseen by their creators.
The definition of 'control' in AI would dramatically shift, requiring new frameworks for alignment and oversight over self-directed artificial intelligences.
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Read at arXiv cs.AI