Neurosymbolic AI -- Why, What, and How (2305.00813v1)

Published 1 May 2023 in cs.AI

Abstract: Humans interact with the environment using a combination of perception - transforming sensory inputs from their environment into symbols, and cognition - mapping symbols to knowledge about the environment for supporting abstraction, reasoning by analogy, and long-term planning. Human perception-inspired machine perception, in the context of AI, refers to large-scale pattern recognition from raw data using neural networks trained using self-supervised learning objectives such as next-word prediction or object recognition. On the other hand, machine cognition encompasses more complex computations, such as using knowledge of the environment to guide reasoning, analogy, and long-term planning. Humans can also control and explain their cognitive functions. This seems to require the retention of symbolic mappings from perception outputs to knowledge about their environment. For example, humans can follow and explain the guidelines and safety constraints driving their decision-making in safety-critical applications such as healthcare, criminal justice, and autonomous driving. This article introduces the rapidly emerging paradigm of Neurosymbolic AI combines neural networks and knowledge-guided symbolic approaches to create more capable and flexible AI systems. These systems have immense potential to advance both algorithm-level (e.g., abstraction, analogy, reasoning) and application-level (e.g., explainable and safety-constrained decision-making) capabilities of AI systems.

Authors (3)

Amit Sheth (127 papers)
Kaushik Roy (265 papers)
Manas Gaur (59 papers)

Citations (18)

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Summary

Overview of Neurosymbolic AI: Why, What, and How

The paper entitled "Neurosymbolic AI - Why, What, and How" addresses the fusion of neural network methods with symbolic knowledge-based approaches in AI, delineating the necessity and framework for such an integration. The authors, Amit Sheth, Kaushik Roy, and Manas Gaur, from recognized institutions, explore the substantial promise of Neurosymbolic AI in enhancing both algorithmic and application-level outcomes.

In essence, the paper motivates the combination of two distinct paradigms in AI: neural networks that excel in data-driven perception tasks and symbolic AI that facilitates cognition with explicit knowledge structures. By intertwining these paradigms, AI systems can achieve cognitive capabilities mirrored in human intelligence, such as abstraction, analogy, and long-term planning, while ensuring attributes like explainability and safety, which are essential for applications in critical domains such as healthcare and autonomous driving.

Algorithm-Level and Application-Level Considerations

The authors highlight that human intelligence leverages integrated perception and cognition systems, termed Systems 1 and 2 by Kahneman's framework. In parallel, AI must blend data-driven strengths of neural networks (System 1) with the explicit cognitive strengths offered by symbolic models (System 2).

Algorithm-Level Perspective: The distinction and integration of perceptive and cognitive processes are pivotal for realizing AI systems capable of human-like reasoning. Despite the capabilities of neural networks in large-scale pattern recognition tasks, they are inadequate for cognitive processes that depend heavily on symbolic knowledge structures, which offer better interpretability and reasoning capabilities. Furthermore, systems strictly relying on neural networks face challenges of transparency and elucidation due to their "black-box" nature.

Application-Level Perspective: The practical implications of Neurosymbolic AI underscore the advancements in explainability, interpretability, and safety of AI systems. By integrating symbolic structures, such as knowledge graphs, within AI architectures, it is feasible to assert explicit reasoning trails and enforce domain constraints, thereby enhancing user trust in the systems.

Methods in Neurosymbolic AI

The paper delineates the classification of Neurosymbolic AI methods into two significant categories based on the manipulation of knowledge structures:

Lowering: This pertains to compressing symbolic knowledge into forms amenable to neural pattern integration, such as embeddings of knowledge graphs or logic. The primary challenge here is the lossy transformation which can affect the cognitive capabilities of AI systems.
Lifting: In contrast, this involves extracting structured symbolic knowledge from neural representations to enable symbolic reasoning. Techniques in this category may involve federated pipelines that orchestrate interaction between the LLMs and symbolic solvers, further enhanced by end-to-end differentiable pipeline designs that allow coherent integration across perception and cognition stages.

Implications and Future Directions

While the existing implementations of Neurosymbolic AI demonstrate its effectiveness, particularly in balancing machine perception with symbolic cognition, significant potential remains for future advancement. For instance, evolving knowledge graphs to model diverse application-specific constraints and workflows, supplemented by high-capacity neural frameworks, could make substantial strides in AI utility both algorithmically and practically.

The paper also alludes to the critical issue of safety in deploying advanced AI systems. It suggests that structured knowledge models, such as enhanced knowledge graphs, can encode necessary policy and safety regulations, aiding in explainability and accountability.

In conclusion, while Neurosymbolic AI offers a promising pathway to achieving intelligent systems closer to human cognitive functionality, ongoing research is required to address the intricacies of integrating neural and symbolic components efficiently. This journey towards realizing robust AI architectures through Neurosymbolic AI is likely to witness significant development in the coming years, backed by empirical evidence and theoretical advancements recounted in this foundational paper.