Relation-Oriented: Toward Causal Knowledge-Aligned AGI
Observation-Oriented paradigm currently dominates relationship learning models, including AI-based ones, which inherently do not account for relationships with temporally nonlinear effects. Instead, this paradigm simplifies the "temporal dimension" to be a linear observational timeline, necessitating the prior identification of effects with specific timestamps. Such constraints lead to identifiability difficulties for dynamical effects, thereby overlooking the potentially crucial temporal nonlinearity of the modeled relationship. Moreover, the multi-dimensional nature of Temporal Feature Space is largely disregarded, introducing inherent biases that seriously compromise the robustness and generalizability of relationship models. This limitation is particularly pronounced in large AI-based causal applications. Examining these issues through the lens of a dimensionality framework, a fundamental misalignment is identified between our relation-indexing comprehension of knowledge and the current modeling paradigm. To address this, a new Relation-Oriented} paradigm is raised, aimed at facilitating the development of causal knowledge-aligned Artificial General Intelligence (AGI). As its methodological counterpart, the proposed Relation-Indexed Representation Learning (RIRL) is validated through efficacy experiments.
Code (0)
등록된 구현이 없습니다.
Tasks
RelationRepresentation LearningSimilar Papers 제목 키워드 기반
Structure Mapping for Transferability of Causal Models
Human beings learn causal models and constantly use them to transfer knowledge between similar environments. We use this intuition to design a transfer-learning framework using object-oriented representations to learn th…
reinforcement-learningReinforcement Learning (RL)Transfer LearningHow Rules Represent Causal Knowledge: Causal Modeling with Probabilistic Logic Programming
Pearl famously argues that causal knowledge enables the prediction of intervention effects. By contrast, purely descriptive knowledge supports only conclusions drawn from observations. His theory of causality, however, i…
Generalized Deep Multi-view Clustering via Causal Learning with Partially Aligned Cross-view Correspondence
Multi-view clustering (MVC) aims to explore the common clustering structure across multiple views. Many existing MVC methods heavily rely on the assumption of view consistency, where alignments for corresponding samples …
ExMAG: Learning of Maximally Ancestral Graphs
As one transitions from statistical to causal learning, one is seeking the most appropriate causal model. Dynamic Bayesian networks are a popular model, where a weighted directed acyclic graph represents the causal relat…
COPO: Causal-Oriented Policy Optimization for Hallucinations of MLLMs
Despite Multimodal Large Language Models (MLLMs) having shown impressive capabilities, they may suffer from hallucinations. Empirically, we find that MLLMs attend disproportionately to task-irrelevant background regions …