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Lira Mirui

LiraMirui
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AGI waifu when

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reacted to KnutJaegersberg's post with πŸ‘€ 1 day ago
A Brief Survey of Associations Between Meta-Learning and General AI The paper titled "A Brief Survey of Associations Between Meta-Learning and General AI" explores how meta-learning techniques can contribute to the development of Artificial General Intelligence (AGI). Here are the key points summarized: 1. General AI (AGI) and Meta-Learning: - AGI aims to develop algorithms that can handle a wide variety of tasks, similar to human intelligence. Current AI systems excel at specific tasks but struggle with generalization to unseen tasks. - Meta-learning or "learning to learn" improves model adaptation and generalization, allowing AI systems to tackle new tasks efficiently using prior experiences. 2. Neural Network Design in Meta-Learning: - Techniques like Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks enable self-improvement and adaptability for deep models, supporting generalization across tasks. - Highway networks and ResNet-style models use shortcuts for efficient backpropagation, allowing deeper models that can be used in meta-learning frameworks. 3. Coevolution: - Coevolution involves the mutual evolution of multiple components, such as learners or task-solvers, to improve overall performance. - Coevolution between learners enhances collaboration and competition within AI systems, while coevolution between tasks and solvers (e.g., POWERPLAY and AI-GA frameworks) pushes solvers to adapt to increasingly complex tasks. 4. Curiosity in Meta-Learning: - Curiosity-based exploration encourages AI systems to discover new, diverse features of the environment, avoiding local optima. - Curiosity-based objectives can be combined with performance-based objectives to ensure efficient exploration and adaptation in complex tasks. 5. Forgetting Mechanisms: - Forgetting is crucial to avoid memory overload in AI systems https://arxiv.org/abs/2101.04283
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