Source-linked AI summary
Open Questions in Creating Safe Open-ended AI: Tensions Between Control and Creativity
Adrien Ecoffet, Jeff Clune, Joel Lehman
TL;DR
As AI research increasingly explores open-ended algorithms, whether their creativity can be safely constrained remains an important question. The paper connects open-ended search with AI-safety concerns, traces how indirect incentives can complicate control, and proposes research directions to study the problem. It cautions that confident recommendations remain limited because the topic is understudied and complex.
Problem
As machine learning explores open-ended algorithms, how to control their creativity safely remains unresolved amid complex search dynamics.
Source: Introduction · pp. 1–2, Approach: Safety in Open-ended Search · p. 3Method
The paper connects open-ended search to AI-safety questions and proposes studying natural and computational examples and safety benchmarks.
Source: Introduction · p. 2, Learning from Biological Evolution and… · p. 5, Computational Study of Open-Ended Search · p. 5Results
Open-ended search often uses speculative incentives as proxies for ideal objectives, making it harder to control than directed search.
Source: Explicit Incentives · p. 3Takeaways & Limitations
Studying natural evolution, computational open-ended search, and safety benchmarks could help clarify how its safety and controllability might be assessed.
Source: Learning from Biological Evolution and… · p. 5, Computational Study of Open-Ended Search · p. 5, Selective Discovery · p. 6The paper offers few confident recommendations because open-ended search safety is understudied and complex, with effects of initial changes difficult to predict.
Source: Approach: Safety in Open-ended Search · p. 3, Discussion and Conclusion · p. 7
Abstract
from arXiv · showhide
Artificial life originated and has long studied the topic of open-ended evolution, which seeks the principles underlying artificial systems that innovate continually, inspired by biological evolution. Recently, interest has grown within the broader field of AI in a generalization of open-ended evolution, here called open-ended search, wherein such questions of open-endedness are explored for advancing AI, whatever the nature of the underlying search algorithm (e.g. evolutionary or gradient-based). For example, open-ended search might design new architectures for neural networks, new reinforcement learning algorithms, or most ambitiously, aim at designing artificial general intelligence. This paper proposes that open-ended evolution and artificial life have much to contribute towards the understanding of open-ended AI, focusing here in particular on the safety of open-ended search. The idea is that AI systems are increasingly applied in the real world, often producing unintended harms in the process, which motivates the growing field of AI safety. This paper argues that open-ended AI has its own safety challenges, in particular, whether the creativity of open-ended systems can be productively and predictably controlled. This paper explains how unique safety problems manifest in open-ended search, and suggests concrete contributions and research questions to explore them. The hope is to inspire progress towards creative, useful, and safe open-ended search algorithms.
Introduction
Open-ended search extends artificial-life ideas about continual innovation to AI research, where algorithms may design new architectures, learning methods, or even pursue AI research programs. The paper argues that this creativity raises safety and control questions as AI is increasingly applied in the real world.
Introduction
- Directed search optimizes a defined objective, whereas open-ended search investigates conditions under which creative systems can generate diverse products.The latter is often population-based and evolutionary, with complexity and intelligence potentially emerging from the search.Source: Introduction · p. 1
- ML is increasingly exploring algorithms that design architectures, learn how to learn, and potentially pursue AI research programs.This calls for a more creative, open-minded search because the potential of new algorithms can be difficult to predict.Source: Introduction · p. 1
- Open-ended evolution studies continual evolutionary innovation, and its ideas are beginning to influence machine learning research.The paper describes this growing overlap as a reason to examine the safety of open-ended search.Source: Introduction · pp. 1–2
- AI safety concerns grow as real-world applications can affect human well-being and produce costly, unanticipated failures.The paper connects these concerns to whether open-ended systems' creativity can be controlled predictably.Source: Introduction · p. 2
- Artificial life and open-ended evolution offer experience with creative algorithms, while open-ended search remains relatively new in machine learning.The paper lays out connections to active AI-safety questions and suggests research contributions.Source: Introduction · p. 2
Background
Open-ended search spans evolutionary systems inspired by biology and machine-learning algorithms that learn to innovate. AI safety, by contrast, addresses divergences between intended goals and behavior through specification, robustness, and assurance concerns.
Open-ended Search
- Open-ended evolution studies continual innovation, while open-ended search also covers machine-learning algorithms that learn to innovate.Digital evolutionary systems can produce ecological interactions and novelty; the shared scope includes systems that produce agents.Source: Open-ended Search · p. 2
AI Safety
- AI safety problems include mismatches between intended goals and optimization, unsafe behavior after perturbations, and difficulty understanding or controlling systems.These are described as specification, robustness, and assurance problems, respectively.Source: AI Safety · pp. 2–3
Approach: Safety in Open-ended Search
The paper frames open-ended search safety as a tension between creative, bottom-up emergence and top-down control. It traces safety risks through ideal objectives, explicit search incentives, and the incentives of agents produced, using biological evolution and innovation systems to examine the trade-offs.
Approach: Safety in Open-ended Search
- Open-ended search may be difficult to constrain because complex dynamics make the effects of small initial changes hard to predict, though higher-level regularities may be exploitable.The paper proposes further research to explore whether such regularities can support safety.Source: Approach: Safety in Open-ended Search · p. 3
How Safety Issues Emerge in Open-ended Search
- Open-ended search involves multiple optimization levels that can diverge from designer intent and create potential safety hazards.The paper adapts this categorization from prior AI-safety frameworks.Source: How Safety Issues Emerge in Open-ended… · p. 3
Ideal Objective
- An experimenter's ideal objective reflects their aims, with safety implicitly included when seeking real-world solutions or AGI without harm.Examples include solving concrete problems, generating diversity to study open-endedness, or creating AGI.Source: Ideal Objective · p. 3
Explicit Incentives
- Open-ended search incentives often serve as speculative proxies for ideal objectives, unlike directed search's more direct objective translations, making them harder to control.Evolution illustrates the gap: survival and reproduction are explicit incentives, while producing intelligence is the ideal objective in this example.Source: Explicit Incentives · p. 3
Agent Incentives
- Agents produced by open-ended search may develop emergent incentives that diverge from the search process's ideal objective or explicit incentives.Human desires are offered as proxies shaped by ancestral conditions; environmental change can make such proxies poorly matched.Source: Agent Incentives · p. 3
Case Study: Biological Evolution and AI Safety
- Biological evolution produced human intelligence despite an explicit incentive of survival and reproduction, while humans can pursue ends beyond that incentive.The paper contrasts this bottom-up emergence with top-down safety approaches that seek to align AI incentives with human interests.Source: Case Study: Biological Evolution and… · p. 4
Controllability of Innovative Systems
- Innovation systems face a trade-off: strong central control may impair progress, while unrestricted discovery can produce harmful side effects.The paper presents this as an unresolved question about designing systems that maximize benefits while minimizing risk.Source: Controllability of Innovative Systems · p. 4
Research Directions for Safe Open-Ended AI
The paper proposes research directions for learning from natural and human innovation, testing computational open-ended search, improving interpretability, and developing safety practices for discovery and transfer. These directions address how to steer creative systems while recognizing uncertainty about predictability, control, and generalization.
Learning from Biological Evolution and Human Systems of Innovation
- Natural evolution and human innovation offer evidence and intervention models for safety, but predictability remains unresolved and lessons may not generalize to computational search.Questions about how moral values evolved could inform whether open-ended search might produce agents with similar values.Source: Learning from Biological Evolution and… · p. 5
Computational Study of Open-Ended Search
- Study computational systems in low-stakes settings through forked-run divergence, learned incentive controllers, and sweeps of search conditions.A learned controller could map a fully specified ideal objective to incentives, but would not solve translating implicit objectives into code.Source: Computational Study of Open-Ended Search · pp. 5–6
Automatic Interpretability
- Current interpretability methods require manual analysis and are architecture-specific, motivating tests on open-ended agents and eventual automatic understanding of novel architectures.Even moderately complex architectures could take inordinate human effort to understand with current approaches.Source: Automatic Interpretability · p. 6
Benchmarks for Safe Open-Ended Search
- Benchmarks can focus research and ease comparison, though overfitting and score fixation can undermine their value.The paper proposes standardized safety problems, including discovering novel policies without evaluating ones that crash into a wall.Source: Benchmarks for Safe Open-Ended Search · p. 6, Selective Discovery · p. 7
Scalable Interactive Open-ended Search
- Human input could shape incentives, select or stop agents, and help make search safer, but dependence on people may make interaction costly at scale.The paper suggests testing whether learned human-preference models used in directed reinforcement learning can guide open-ended evolution.Source: Scalable Interactive Open-ended Search · p. 6
Selective Discovery
- Safety benchmarks should test whether search can optimize desirable niches without ever discovering high-scoring solutions in designated undesirable niches.A simpler novelty-search benchmark is to maximize novel policies while never evaluating one that crashes into a wall.Source: Selective Discovery · pp. 6–7
Open-Ended Reality Gap
- Because practical open-ended agents may be trained in simulation and transferred to reality, the paper proposes reproducible robot-transfer workflows and two-simulation proxies.The proxy can speed research but is not wholly representative of real-world transfer.Source: Open-Ended Reality Gap · p. 7
Discussion and Conclusion
Designers influence open-ended systems indirectly by setting incentives and conditions rather than specifying each product. The paper maps this distinctive safety challenge onto AI safety, but offers few confident recommendations because the area is understudied and evidence is inconclusive.
Discussion and Conclusion
- Open-ended search makes safety indirect: designers set system incentives and conditions instead of specifying the qualities of a single product.The paper compares this role to regulating an economy, allocating research funds, or designing a virtual universe.Source: Discussion and Conclusion · p. 7
- Few confident recommendations are offered because open-ended search safety is complex and understudied, with evidence that does not support straightforward conclusions.The paper instead highlights research directions and concrete projects that could catalyze further understanding.Source: Discussion and Conclusion · p. 7
- The paper is an initial exploration of how open-ended search uniquely intersects with AI-safety problems, agendas, and concepts.It highlights that some safety challenges may require novel approaches.Source: Discussion and Conclusion · p. 7