What is Neural Symbolic Integration?

What is Neural Symbolic Integration?

A technique that combines neural networks and symbolic systems, such as logic or rules, to enable more explainable and robust AI systems.

Artificial intelligence (AI) is the field of computer science that aims to create machines and systems that can perform tasks that normally require human intelligence, such as perception, reasoning, learning, decision making, and natural language processing. AI systems can be broadly classified into two categories: neural and symbolic.

Neural AI systems are based on artificial neural networks, which are computational models inspired by the structure and function of biological neurons. Neural networks can learn from data by adjusting their weights and biases through a process called backpropagation. Neural networks are good at handling complex and noisy data, such as images, speech, and text, and can achieve high performance on various tasks, such as object recognition, natural language understanding, and machine translation. However, neural networks also have some limitations, such as:

• They are often black boxes, meaning that their internal workings are not transparent or interpretable to humans. This makes it hard to explain how and why they make certain decisions or predictions, which can raise ethical and social issues, especially in domains where accountability and trust are important, such as healthcare, finance, and law.
• They are often data-hungry, meaning that they require large amounts of labeled data to train and generalize well. This can be costly and time-consuming to obtain, and may not be available for some domains or tasks. Moreover, the quality and quantity of data can affect the performance and reliability of neural networks, which can lead to biases or errors.
• They are often brittle, meaning that they can fail or degrade significantly when faced with novel or unexpected situations that differ from their training data. This can limit their adaptability and robustness to changing environments or user needs.

Symbolic AI systems are based on symbolic representations and manipulations of knowledge, such as logic, rules, ontologies, and graphs. Symbolic systems can reason from explicit and structured knowledge using formal methods, such as deduction, induction, abduction, and analogy. Symbolic systems are good at handling abstract and high-level concepts, such as causality, common sense, and morality, and can achieve human-like performance on various tasks, such as planning, problem-solving, and natural language generation. However, symbolic systems also have some limitations, such as:

• They are often knowledge-poor, meaning that they rely on manually encoded knowledge that may not be complete or accurate for all domains or tasks. This can be labor-intensive and error-prone to construct and maintain, and may not capture the richness and diversity of human knowledge.
• They are often perception-poor, meaning that they have difficulty in processing and interpreting sensory input, such as images, speech, and text. This can limit their ability to interact with the real world or natural language.
• They are often inflexible, meaning that they have difficulty in learning from data or feedback, adapting to new situations or goals, or dealing with uncertainty or inconsistency. This can limit their scalability and efficiency.

Neural symbolic integration is a technique that combines neural networks and symbolic systems in a hybrid architecture to address the weaknesses of each while preserving their strengths. Neural symbolic integration aims to achieve the following benefits:

• Explainability: By integrating symbolic reasoning with neural learning, neural symbolic systems can provide more transparent and interpretable explanations for their decisions or predictions, which can enhance trust and accountability.
• Robustness: By integrating symbolic knowledge with neural data, neural symbolic systems can improve their generalization and reliability across different domains or tasks, and cope better with novel or unexpected situations.
• Efficiency: By integrating symbolic abstraction with neural perception, neural symbolic systems can reduce their data requirements and computational complexity, and increase their speed and accuracy.

Approaches

There are different approaches for integrating neural networks and symbolic systems, depending on how they interact with each other. According to Henry Kautz’s taxonomy , these approaches can be classified into four categories:

Symbolic Neural Symbolic

This approach is the current approach of many neural models in natural language processing, where words or subword tokens are the ultimate input and output of large language models. Examples include BERT , RoBERTa , GPT-3 , etc. These models use neural networks to learn from large amounts of text data, and generate natural language outputs based on probabilistic distributions. However, these models do not explicitly represent or manipulate symbolic knowledge, such as logic or rules, and thus may lack explainability or robustness.

Symbolic [Neural]

This approach is exemplified by AlphaGo , where symbolic techniques are used to invoke neural techniques. In this case, the symbolic technique is Monte Carlo tree search, and the neural technique is a deep neural network that learns how to evaluate game positions. This approach uses symbolic methods to guide the search process, and neural methods to learn from data and improve the evaluation function. However, this approach does not explicitly integrate symbolic and neural representations, and thus may not fully exploit their complementarity.

Neural | Symbolic

This approach uses a neural architecture to interpret perceptual data as symbols and relationships that are reasoned about symbolically. An example is Neural-Concept Learner , which uses a convolutional neural network to extract concepts and relations from images, and a logic tensor network to reason about them using first-order logic. This approach uses neural methods to handle complex and noisy data, and symbolic methods to handle abstract and high-level concepts. However, this approach may not allow bidirectional communication between the neural and symbolic components, and thus may not fully leverage their synergy.

Neural: Symbolic → Neural

This approach relies on symbolic reasoning to generate or label training data that is subsequently learned by a deep learning model. An example is NeuroSAT , which uses a recurrent neural network to learn how to solve Boolean satisfiability problems, by training on examples generated by a symbolic solver. This approach uses symbolic methods to provide supervision or guidance for neural learning, and neural methods to achieve scalability or efficiency. However, this approach may not allow feedback or adaptation between the symbolic and neural components, and thus may not fully capture their dynamics.

Conclusion

Neural symbolic integration is a technique that combines neural networks and symbolic systems in a hybrid architecture to enable more explainable and robust AI systems. Neural symbolic integration can provide various benefits, such as explainability, robustness, and efficiency, by exploiting the strengths and addressing the weaknesses of each component. There are different approaches for integrating neural networks and symbolic systems, depending on how they interact with each other. Each approach has its own advantages and disadvantages, and may be suitable for different domains or tasks. Neural symbolic integration is an active and promising research area that can advance the state-of-the-art of AI and enhance its impact on society.