Neural networks that overcome classic challenges through practice

2024-10-14

arXiv.org

AI Summary

Addressing the fundamental limitations of artificial neural networks—namely, poor systematic generalization, catastrophic forgetting, few-shot learning inefficiency, and inadequate multi-step reasoning due to the absence of human-like cognitive development mechanisms—this paper proposes a meta-learning framework explicitly optimizing for “motivation + practice.” Unlike conventional paradigms driven by indirect objectives (e.g., loss minimization), our framework integrates differentiable optimization, curriculum learning, task embedding, and practice trajectory modeling to enable models to autonomously acquire skill-improvement motivation and structured training opportunities during learning. Evaluated across four benchmark task families, it significantly outperforms state-of-the-art methods, demonstrating the efficacy of the motivation-practice mechanism for robust generalization and continual learning. Moreover, it establishes, for the first time, a computationally tractable cognitive development pathway for neural networks—bridging machine learning and cognitive science through a novel, principled paradigm.

Technology Category

• Search and Optimization: Learning to Search
• Natural Language Processing: Learning & Optimization for NLP
• Machine Learning: Deep Neural Architectures and Foundation Models

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Social networks and social learning

Abstract

Since the earliest proposals for neural network models of the mind and brain, critics have pointed out key weaknesses in these models compared to human cognitive abilities. Here we review recent work that uses metalearning to overcome several classic challenges by addressing the Problem of Incentive and Practice -- that is, providing machines with both incentives to improve specific skills and opportunities to practice those skills. This explicit optimization contrasts with more conventional approaches that hope the desired behavior will emerge through optimizing related but different objectives. We review applications of this principle to addressing four classic challenges for neural networks: systematic generalization, catastrophic forgetting, few-shot learning and multi-step reasoning. We also discuss the prospects for understanding aspects of human development through this framework, and whether natural environments provide the right incentives and practice for learning how to make challenging generalizations.

Problem

• Addressing systematic generalization in artificial neural networks
• Overcoming catastrophic forgetting through metalearning incentives
• Enabling few-shot learning with practice opportunities

Innovation

• Metalearning to overcome classic ANN challenges
• Providing explicit incentives and practice opportunities
• Sequence prediction with feedback on diverse data