AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence (1905.10985v2)

Abstract: Perhaps the most ambitious scientific quest in human history is the creation of general artificial intelligence, which roughly means AI that is as smart or smarter than humans. The dominant approach in the machine learning community is to attempt to discover each of the pieces required for intelligence, with the implicit assumption that some future group will complete the Herculean task of figuring out how to combine all of those pieces into a complex thinking machine. I call this the "manual AI approach". This paper describes another exciting path that ultimately may be more successful at producing general AI. It is based on the clear trend in machine learning that hand-designed solutions eventually are replaced by more effective, learned solutions. The idea is to create an AI-generating algorithm (AI-GA), which automatically learns how to produce general AI. Three Pillars are essential for the approach: (1) meta-learning architectures, (2) meta-learning the learning algorithms themselves, and (3) generating effective learning environments. I argue that either approach could produce general AI first, and both are scientifically worthwhile irrespective of which is the fastest path. Because both are promising, yet the ML community is currently committed to the manual approach, I argue that our community should increase its research investment in the AI-GA approach. To encourage such research, I describe promising work in each of the Three Pillars. I also discuss AI-GA-specific safety and ethical considerations. Because it it may be the fastest path to general AI and because it is inherently scientifically interesting to understand the conditions in which a simple algorithm can produce general AI (as happened on Earth where Darwinian evolution produced human intelligence), I argue that the pursuit of AI-GAs should be considered a new grand challenge of computer science research.

• The paper introduces AI-GAs as a novel approach that combines meta-learning architectures, algorithms, and generated learning environments to achieve general AI.
• It contrasts traditional modular AI development with automated, scalable processes that optimize neural network structures across tasks.
• The research highlights that adaptive, Darwinian-like environments can accelerate agents' learning and rapid task generalization.

Overview of AI-GAs: AI-Generating Algorithms as a Pathway to General AI

The concept of generating general AI is a highly ambitious enterprise, demanding a comprehensive understanding of the components and systems that contribute to human-like cognition. The paper "AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence" by Jeff Clune proposes a paradigm shift in the approach towards achieving general AI. The author suggests a novel method combining three research pillars: meta-learning architectures, meta-learning the learning algorithms themselves, and generating effective learning environments. This overview discusses the paper's main claims, numerical analysis, and implications for future AI research.

Manual AI Approach vs. AI-Generating Algorithms

In the traditional "manual AI approach," the development of general AI is segmented into discovering individual components that contribute to intelligence and then assembling these into a coherent whole. This methodological framework, while inherently optimistic about modular learning and engineering, faces significant challenges given the monumental task of identifying and synergizing potentially countless building blocks into a single intelligent agent.

Conversely, Clune's proposal of AI-generating algorithms (AI-GAs) postulates that intelligence can be derived through algorithmic learning processes that automatically produce the foundational components of AI. This approach is grounded in the realization that many machine learning successes have come from replacing hand-engineered solutions with learned alternatives, as seen in the evolution of computer vision and natural language processing.

Research Pillars of AI-GAs

1. Meta-learning Architectures: The first pillar focuses on the automatic search and optimization of neural network architectures, a domain already active in producing state-of-the-art results in benchmarks like CIFAR and ImageNet. The idea is to transcend human-designed models by identifying architectures suited to various tasks automatically.
2. Meta-learning Learning Algorithms: The second pillar seeks to optimize the learning algorithms themselves, moving beyond predefined strategies like stochastic gradient descent to more adaptable methodologies that better generalize across task distributions. Recent meta-learning advancements indicate potential improvements in sample efficiency and adaptiveness.
3. Automatically Generating Learning Environments: The third and crucially underexplored pillar relates to creating environments that foster learning, akin to evolutionary processes. These environments constitute challenges that adapt to agent performance and learning progress, ushering an open-ended horizon for intelligent behavior development echoing Darwinian evolution.

Implications and Future Directions

Clune’s advocacy for AI-GAs emphasizes their potential to seamlessly scale with impending computational advancements, potentially outpacing the manual method's reliance on assembling predefined components. This paper’s foresight into AI-GA suggests a new research trajectory that may offer efficient pathways to handling both immediate and long-range challenges in AI development.

From a theoretical standpoint, AI-GAs posit a leap in understanding how automated environments can dynamically cater to learning and adaptability, leading to agents capable of rapid task generalization. Practically, this translates to potential improvements in AI applications across domains requiring adaptive models, from autonomous systems to complex problem-solving tasks.

Conclusion

AI-GAs present a compelling alternative to traditional AI research, promising streamlined pathways to achieving general intelligence. While the paradigm offers intriguing possibilities, it also necessitates meticulous research in environments and regulatory parameters to ensure safety and ethical considerations are addressed. As AI technologies continue to evolve, AI-GAs could catalyze a transformative phase in artificial intelligence, necessitating a collective research endeavor to explore this promising frontier.