content.raw: Artificial general intelligence through recursive data compression and grounded reasoning: a position paper
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content.value: Artificial general intelligence through recursive data compression and grounded reasoning: a position paper
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content.raw: This paper proposes a system to achieve AGI through general data compression and grounded reasoning.
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content.value: This paper proposes a system to achieve AGI through general data compression and grounded reasoning.
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content.raw: General Data Compression involves creating a flexible algorithm that adapts to input data to simplify and compress it recursively, identifying simple, orthogonal features to avoid redundancy. The algorithm measures AGI progress by solving problems based on increasing complexity, and it expands its search space according to the data itself. Compression is applied not only to data but also to model parameters, and sequences are segmented based on compressibility.
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content.value: General Data Compression involves creating a flexible algorithm that adapts to input data to simplify and compress it recursively, identifying simple, orthogonal features to avoid redundancy. The algorithm measures AGI progress by solving problems based on increasing complexity, and it expands its search space according to the data itself. Compression is applied not only to data but also to model parameters, and sequences are segmented based on compressibility.
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content.raw: Grounded Reasoning refers to forming representations with various granularities, crucial for commonsense reasoning and AGI. The system simulates the real world as its model, switching between representations and maximizing resourcefulness. Key ideas include the world as its own model for reasoning and actions aimed at maximizing entropy to test hypotheses.
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content.value: Grounded Reasoning refers to forming representations with various granularities, crucial for commonsense reasoning and AGI. The system simulates the real world as its model, switching between representations and maximizing resourcefulness. Key ideas include the world as its own model for reasoning and actions aimed at maximizing entropy to test hypotheses.
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content.raw: The paper emphasizes simplicity, data-dependent bias, recursion, orthogonality, resourcefulness, and grounding in real-world contexts as fundamental principles in building an AGI system.
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content.value: The paper emphasizes simplicity, data-dependent bias, recursion, orthogonality, resourcefulness, and grounding in real-world contexts as fundamental principles in building an AGI system.
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content.href: https://arxiv.org/abs/1506.04366
content.raw: https://arxiv.org/abs/1506.04366
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rawContent: Artificial general intelligence through recursive data compression and grounded reasoning: a position paper
  
  
  This paper proposes a system to achieve AGI through general data compression and grounded reasoning.
  
  General Data Compression involves creating a flexible algorithm that adapts to input data to simplify and compress it recursively, identifying simple, orthogonal features to avoid redundancy. The algorithm measures AGI progress by solving problems based on increasing complexity, and it expands its search space according to the data itself. Compression is applied not only to data but also to model parameters, and sequences are segmented based on compressibility.
  
  Grounded Reasoning refers to forming representations with various granularities, crucial for commonsense reasoning and AGI. The system simulates the real world as its model, switching between representations and maximizing resourcefulness. Key ideas include the world as its own model for reasoning and actions aimed at maximizing entropy to test hypotheses.
  
  The paper emphasizes simplicity, data-dependent bias, recursion, orthogonality, resourcefulness, and grounding in real-world contexts as fundamental principles in building an AGI system.
  
  https://arxiv.org/abs/1506.04366
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commentators._id: 6787f6033d342e4cc8d8c6cc
commentators.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/BapzHmY4xylemybaIjAEn.jpeg
commentators.fullname: Joseph Robert Turcotte
commentators.name: Fishtiks
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