What is neuro symbolic artificial intelligence and how it is different from other approaches to AI, how it helps and what it consists of?

Neuro-symbolic AI is a hybrid approach to AI combining sub-symbolic and symbolic reasoning. Sub-symbolic reasoning is used in general NLP, deep learning and neural networks. It helps describe things which are hard to express in explicit rules. Symbolic reasoning is used in math software as it gives definitive correct answers. It’s rule based, uses formal logic, ontologies and knowledge graphs. Graphwise’s own taxonomy-based taggers are also performing symbolic NLP. Mixing both is an effort to overcome the fundamental limitations both approaches have.

Key takeaways: 

• Neuro symbolic ai is a mix of rule based and neural network-based ai and tries to overcome the limits of each technology. It lets the neuro part generate ideas and verifies them using the symbolic part. 
• It’s in a quite earlier phase and still more in the scientific research field and not so widely adopted yet but with huge potential to be used by enterprises and governments because it provides accountability while it can handle flexible context. 
• It is hard to implement because it requires manual or semi-manual work, maintaining ontologies and has huge architectural complexity. It has a scalability bottleneck when dealing with billions of dynamic enterprise data edges.

Why is neuro-symbolic AI being used in 2026?

Neuro Symbolic AI bridges the gap between statistical neural networks and symbolic logic based AI. It solves the individual problems each approach has by applying the other one. This results in a synergy.

What problems does it solve and how does it work? 

To understand how that works, let’s look at the individual problems of each approach and explain how the other one fixes it:

Purely statistical models, called sub-symbolic:

• Hallucinate
• Have the “black box” problem. It is difficult to understand why the model gave one or another answer. This makes those models unreliable because there is no accountability associated with using them.
• They are data hungry. In other words, in order to increase their accuracy, developers need to find a way to always feed them with new information. 

How Symbolic Logic Fixes Neural Networks:

Symbolic structures (like ontologies, rules, and knowledge graphs) provide a strict, rigid framework which guarantees that AI will act in a predictable way. With Graphwise you can change a fact in your knowledge, which will make the machines act accordingly without retraining. It embeds logical rules in the loss function of the neural network which penalizes violations during training and increases the chance of the system respecting physical, legal, or factual laws. It cannot output a statistical guess that violates a hard logical constraint. 

Symbolic AI downsides: 

• Rigidity – Traditional symbolic AI (GOFAI – Good Old-Fashioned AI) requires manual input by people who set the rules for each individual case – when X then Y.
  However real-world data is messy, noisy, and unstructured (speech, images, human handwriting). This breaks down traditional symbolic systems. 
• Difficulty in handling perception
• Inability to learn robustly from noisy perceptual data.

How Neural Networks Fix Symbolic Systems:

The Neural Solution: Neural networks act as the “perception engine.” They translate real world chaos into clean entities, concepts, and vectors that can then be processed by symbolic reasoning engines. The initial inputs can be text, audio, pixels on which the model performs pattern recognition and handle ambiguity to reach an ordered and structured inputs for the symbolic system to work with. 

Neuro symbolic AI examples

• Autonomous vehicles – it uses data from sensors – cameras, lidars, ultrasound sensors and radars to feed a sub-symbolic algorithm which then is transferred to logic based system to explain cause-and-effect scenarios and to explain the decisions the car made. 
• AlphaGo – a neural networks + classical search algorithms. Monte Carlo Tree Search which is used in it is a representation of statistical tree search, not deduction. So, not really what’s considered a “classic NeSy” but a system which is close. It is a system that uses a neural network to evaluate the positions in Go and then transfer the data to a Monte Carlo model which is a stochastic search algorithm which chooses the winning move.

 The most practical NeSy system is an LLM combined with a knowledge graph. The integration patterns for both are:

• The graph grounds the LLM (GraphRAG)
• The LLM helps to extend the graph (information extraction, possibly ontology generation)
• The graph can check the LLM (SHACL, reasoning)

Examples from our own experience:

• From the symbolic half we understand that very well because since 2000 we develop systems like and and we have several production systems for BBC, AstraZeneca, etc.
• How neural fixes symbolic: our own CEEL – NER, hybrid retrieval and a transformer disambiguator link text to 42M Wikidata concepts
• How symbolic fixes neural: For global banks and legal companies we use approaches to model laws as a graph

Who Came Up with It and When?

Neuro-symbolic AI is the modern evolution of decades-long efforts to bridge connectionism (neural networks) and symbolism (logic/rules).

The philosophical debate dates back to the mid-20th century, following the founding of AI at the Dartmouth Workshop in 1956. Researchers quickly split into “neats” (who favored logic and formal rules) and “scruffies” (who favored heuristic, statistical, and connectionist models).

Early formal attempts to combine neural networks with symbolic reasoning began taking shape in the late 1980s and 1990s. The foundational work by researchers like Ron Sun, Lokendra Shastri, and Ajit Narayanan lead to the concept of a merge between both technologies. 

 The term “Neuro-symbolic AI” gained significant popularity in the 2010s and 2020s. People like Josh Tenenbaum from MIT and major tech labs championed the idea of merging and solving the problems each technology has. 

FAQ

What are the main architectural patterns used to build a Neuro-symbolic AI system?

Neuro-symbolic AI combines neural networks, knowledge graphs and logic engines. This way it collects messy data from the world and applies structured rules and enforces facts over that messy data. These systems use GraphRAG, vector embeddings, and constraint checks to let AI think flexibly while remaining factually accurate and logically sound.

What are the main technical challenges in front of the neuro-symbolic AI? 

• The technology itself – finding robust mathematical and computational ways to integrate the messy real world data with the rule-based logic. That on itself leads to a bottleneck and latency when it comes to translating the data. 
• Knowledge Acquisition and Maintenance – usually building and maintaining such systems requires a lot of manual work from highly skilled engineers. On top of that, finding tools or creating tools that can do that with the same pace enterprise data grows is still challenging. 
• Costs – building systems like that is computationally and financially expensive.