What Is Neuro-Symbolic AI?

Key takeaways

Neuro-symbolic artificial intelligence (AI) combines neural networks with symbolic AI to enable systems to mimic human decision-making and logic.

• As of 2024, the major research areas within neuro-symbolic AI include learning and inference, logic and reasoning, and knowledge representation [1].

• Neuro-symbolic AI benefits regulated, high-impact fields where trust and accountability are essential, such as law, medicine, and safety-focused industries.

• You can use neuro-symbolic AI to support efficient, transparent decision-making in tasks such as legal document review, personalized patient care, and crisis management planning.

Learn more about neuro-symbolic AI and how using this type of algorithm can help you understand the “why” behind AI decisions and design more intelligent, transparent systems. If you’re ready to start building your AI expertise, enroll in the IBM AI Engineering with Python, PyTorch & TensorFlow Professional Certificate. You’ll have the opportunity to explore machine learning, deep learning, neural networks, and ML algorithms like classification, regression, clustering, and dimensional reduction in as little as four months.

What is a neuro-symbolic AI?

Neuro-symbolic AI is a combination of two artificial intelligence paradigms, neural networks and symbolic AI, to enable human-like reasoning capabilities. Neural networks excel at pattern recognition, while symbolic AI excels at logic, language, and rule-based reasoning, much like human comprehension. By combining these methods, AI can learn from data while applying logical reasoning to interpret and explain conclusions. 

For example, neuro-symbolic AI might identify objects in an image using pattern recognition from neural network algorithm components, then use symbolic logic to infer relationships between them. This enables deeper understanding and reasoning for applications, such as natural language processing, complex problem-solving, and decision-making. 

Learn more: How Do Neural Networks Work?

What is an example of symbolic AI?

A popular example of symbolic AI is MYCIN, which was an early-stage AI medical diagnostic system. This system used symbolic rule-based reasoning to evaluate patient symptoms. It applied an “If X, then Y” approach, using logical reasoning to arrive at its diagnosis. While transparent and interpretable, this type of system struggled to adapt to new information and changing demands. More recent systems have overcome this limitation by combining neural network algorithms to create neuro-symbolic AI.

What is the difference between neuro-symbolic AI and generative AI?

Where generative AI focuses on creating new content, like text or images, neuro-symbolic AI focuses on human-like reasoning and contextual understanding. Generative AI systems, like large language models, learn patterns from large datasets to recognize correlations and reproduce them in novel ways. This can lead to novel and informative outputs, but the underlying AI algorithms can’t necessarily understand the context behind the content they produce, making them more prone to hallucinations and biased information.

Conversely, neuro-symbolic developers design AI to understand logic and reason. By integrating neural networks and symbolic reasoning systems, it can go beyond recognizing complex patterns, then apply structured reasoning to interpret these patterns. 

For example, imagine designing an autonomous vehicle using different types of AI. Generative AI systems could simulate driving environments and identify objects from visual sensors, but decision-wise, these models lack the capacity to inform decision-making or explain the reasoning behind their actions. Conversely, neuro-symbolic AI could utilize symbolic reasoning capabilities to make informed choices and enable vehicles to provide explanations for actions, making underlying decisions more transparent. 

What industries are most likely to benefit from neuro-symbolic AI?

Organizations in regulated, high-impact fields where trust and accountability are essential, like health care and law, are most likely to benefit from neuro-symbolic AI. When relying on neural networks alone, algorithms can interpret patterns and make predictions based on data. However, the algorithms often can’t explain the decision-making process or guarantee that it follows specific rules. This lack of transparency created a problem for organizations that need to clearly trace decision paths to ensure compliance and safety. For example, if you provide a patient with a diagnosis based on medical images, you must be able to trace the diagnostic process and validate the decision-making steps. 

By combining neural networks with symbolic AI, which uses explicit relationships and logical structures to guide decisions, neuro-symbolic systems establish a transparent audit trail of decision-making. Because of this, neuro-symbolic AI is likely to benefit several areas, including medical, legal, aerospace, customer service, human resources, and technical industries.

Neuro-symbolic AI examples

Industries are just scratching the surface of potential neuro-symbolic applications. Thanks to the combined capabilities of neuro-symbolic systems, several industries can use this algorithm type to support efficient, transparent decision-making. Some examples of use cases include:

Legal document analysis 

Neuro-symbolic systems can interpret contracts and use rule-based reasoning to identify relevant clauses, assess compliance, and provide legal justifications with traceable decision paths. 

Crisis management planning

These systems can create strategic responses by integrating current data with symbolic models of crisis scenarios to make a formal decision. Neural networks can make sense of chaotic data, while symbolic AI can explain the reasoning behind recommended actions. 

Job candidate profiling

Whereas traditional AI systems have shown bias toward particular candidate profiles, neuro-symbolic systems can build a profile for candidates using thoughtful selection criteria and reasoning, providing transparent reasoning for hiring decisions. This increases the trust and fairness of the hiring process across organizations.

Personalized medicine

Neuro-symbolic systems can use pre-established guidelines and deep learning to accurately analyze medical images, provide diagnoses, recommend optimal treatments for patients, and improve preventative care measures. 

Neuro-symbolic AI research areas

According to a 2024 systematic review, major research areas in neuro-symbolic AI fall within the following overlapping categories [1]:

- Learning and inference (63 percent)

- Logic and reasoning (35 percent)

- Knowledge representation (44 percent)

- Explainability and trustworthiness are less represented (28 percent)

- Meta-Cognition is the least explored area (5 percent)

Getting started in neuro-symbolic AI

Because it’s a hybrid approach, building a foundation in both neural networks and symbolic AI can help you understand the concept of neuro-symbolic AI. For neural networks, consider concepts related to designing and structuring the input layer, hidden layers, and output layers. These layers work together to import raw data, transform the information, and produce the final prediction. You can expand your understanding to more nuanced concepts within neural network architectures, such as weights and biases, transformers, non-linear activations, and backpropagation. 

For symbolic AI, you can explore its use in technologies, such as expert systems and natural language processing. This can help you grasp the underlying rule-based system and how it applies to the transparent decision-making process seen with neuro-symbolic AI. Once you understand each approach, you can continue to explore the combined neuro-symbolic AI model and discover how it capitalized on the strengths of each system.

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