NeuroSymbolic AI: Bridging the Gap Between Learning and Reasoning

My Google Home once told me it was 'raining' while I stood outside under clear skies. When I asked why, it replied, 'Weather data suggests 90% precipitation.' No apology, no recalibration, just blind certainty in flawed data.

This isn’t just a glitch. It’s proof that today’s AI excels at either rigid rule-following or statistical guesswork, but never both at once.

AI models can be grouped into two major categories: neural networks and symbolic AI. On one side, you’ve got neural networks which are great at recognizing patterns in data, but aren’t equipped when it comes to explaining logic. We saw this play out dramatically when IBM Watson for Oncology suggested unsafe treatments. Its black-box approach was unable to justify why it prioritized certain therapies over others. On the other side are symbolic AI systems which follow clear logical rules, but struggle when things become unpredictable. This rigidity backfired spectacularly at Zillow in 2021. Their home-buying algorithm, built on rigid formulas for pricing, kept overpaying for properties even as the housing market shifted unexpectedly during the pandemic. The system couldn't adapt when real-world conditions changed, ultimately losing $881 million. NeuroSymbolic AI is about bringing both neural networks and symbolic AI together, combining their strengths to create something that works better than either one of them alone. 

In this blog post, we will walk through how AI systems like neural networks and symbolic AI work, where they fall short, how NeuroSymbolic AI bridges this gap and what this means for the future of intelligent machines. Along the way, we will also look at examples of how this technology can make a difference in the real world.  

Overview of some Neural and Symbolic AI Techniques in Machine Learning and Knowledge Representation.

Understanding Neural Networks

Neural networks are inspired by the human brain and work by processing large amounts of data. Just like how our brains use networks of neurons to process information, neural networks use layers of artificial neurons to learn from large amounts of data. When I first learned about this biological inspiration, it felt like a lightbulb moment, AI wasn’t just cold code, but a mirror of how we think. Suddenly terms like ‘deep learning’ clicked. We’re essentially teaching machines to learn like humans do: by spotting patterns through repetition, trial, and error.

A comparison showcasing the similarities and differences between a neuron in the human brain and an artificial neuron.

But how does this abstract concept actually work in practice? Here’s that learning process broken down: 

The raw data enters through what is called the input layer, whether this be pixels from an image, words from a text or other readings. The data is converted to numerical values so it can later begin the process of recognizing patterns. Once the converted data is in the network, it gradually starts detecting patterns in the middle layers. These layers might detect patterns like trends in numerical data or other associations between information. After the data is processed through all of the layers, the neural network represents the decision in the form of numerical values that are called probabilities or confidence scores. The values indicate the network's confidence in which categories the different parts of the data belong to.

A diagram example of layers in a neural network.

Now imagine a training system with thousands of cat and dog photos. Neural networks work by looking for patterns in these images to eventually be able to distinguish between the two animals. Based on the learned patterns, the network then assigns the images to the appropriate categories. 

Neural networks power everything from facial recognition to language translation and can work pretty well when it's tested with data that resembles its training. What's great is that implementing them has also become much more accessible thanks to mature frameworks like TensorFlow and PyTorch, both Python libraries. 

As someone who’s used Python for other projects, I really appreciate how these tools leverage Python’s intuitive, almost human-like syntax. There’s something satisfying about being able to build complex systems with code that reads nearly like plain English, compared to more verbose programming languages. 

TensorFlow workflow: Feeding inputs, performing operations and fetching outputs.

However, these models can suffer from certain limitations. First, they need enormous amounts of datasets. Training an image recognition system to distinguish between different dog breeds might need hundreds of examples per breed. As the dataset grows, the model has to process more information, which can increase the complexity of learning. This can also lead to slower performance, since more data means more computation and longer response times. There’s also more room for error, the more steps the model has to go through, the greater the chance it has to make a mistake early on that could throw off the result. 

Neural networks are also what is known as Black Box models. When a neural network misidentifies an image or makes a prediction, it cannot determine why. There is no way to debug its reasoning process.

 

Neural networks in action: identifying and labeling objects in an image by recognizing patterns, without understanding the 'why' behind them.

This brings me to my key takeaway: neural networks are powerful but oddly “shallow”, they mimic understanding without the depth, which is why we need something more.

Which naturally leads us to ask: What happens when we approach AI from the completely opposite direction? To understand the other side of the equation, we need to look at a very different approach to artificial intelligence, one that doesn’t learn from examples.

Symbolic AI: Logical Reasoning

Symbolic AI works differently. Instead of learning from large datasets, it depends on formal logic and clearly defined rules to guide its reasoning. These systems represent knowledge using human-readable symbols that stand for concepts, objects, and the relationships between them. The system uses these symbols to explicitly define facts, such as "Soccer is a sport" or "a cat is an animal," storing them in a structured knowledge base.

To make connections, Symbolic AI relies on logical rules that operate on these symbols. A common approach is to use "if-then" statements, which outline how different entities relate or behave. For example, a rule might state, "if X is a cat, then X has fur," or "if Y is a bird, then Y can fly." These statements serve as the foundation for reasoning, allowing the system to link facts together and connect the dots.

When making decisions, the system scans the knowledge base and uses those deductive reasoning rules to infer new information. If it knows that "Tom is a cat" and has the rule "if X is a cat, then X has fur," it can logically conclude that "Tom has fur." It then uses this conclusion to arrive at an appropriate course of action or response. This process is deterministic, meaning the same input and rules will always produce the same output, and transparent, since every step of reasoning can be traced back to the original facts and rules.

This rule based approach has been used in several well known systems. One example is IBM’s DeepBlue chess system. This was a chess-playing computer that was developed by IBM and it gained fame when it defeated Garry Kasparov, a reigning champion. 

IBM DeepBlue: A chess computer that was able to defeat champion Garry Kasparov.

DeepBlue worked by analyzing millions of possible chess moves very quickly and using expert knowledge to choose the best one. For each move, it looked several steps ahead, considering how both players might respond. It used a method called minimax, which assumes each player will make the strongest possible move, and improved its efficiency with a technique called alpha-beta pruning that helped it ignore moves that weren’t worth exploring.

To decide which positions were better, DeepBlue used evaluation rules written by chess experts. These rules helped it judge things like piece safety, board control, and king protection.

While this kind of system can be useful, it comes with its own set of trade-offs. Let’s take a closer look at the strengths and limitations of this approach. 

One advantage is clear, since these systems are tied to formal rules, you can trace exactly how it arrived at a decision. This is the aspect that I find most valuable about symbolic AI, it mirrors how humans approach logical tasks. When we learn math, we don’t memorize every possible multiplication combination (what’s 189 x 37), we instead learn algorithms and rules that we can apply universally. Similarly, symbolic systems use structured reasoning rather than brute-force pattern matching.

What I equally appreciate about symbolic AI systems is that they are modular, meaning new rules or knowledge can be added without having to retrain the whole system. It reminds me of how humans build understanding.When we learn calculus, we don’t start over from basic counting, but extend our existing math knowledge. 

This modularity makes it especially good for tasks where constraints are important, like making sure a medical system never suggests dangerous drug combinations. And since it doesn’t rely on data in the same way as neural networks, it doesn’t need thousands of examples for every possible scenario. 

But there are challenges too. Symbolic AI isn’t as good as learning from raw data. While neural networks can pick up patterns from examples, symbolic systems usually need humans to write out the logic ahead of time. This is what frustrates me the most. Unlike humans who adapt creatively, symbolic AI fails when it encounters the unexpected. Imagine you’re hanging a picture and realize you don’t have a hammer, you might use a heavy book or a shoe to tap in the nail. But a symbolic AI instruction system would just say “hammer missing” and give up, because no one taught it a “use a different tool” rule. It can’t improvise like we do. 

This rigidity becomes a problem when dealing with something complex and unpredictable such as language or image recognition. There are also not as many frameworks built for symbolic systems, which can make them harder to implement in practice compared to neural networks. 

NeuroSymbolic AI: Combining the Best of Both Worlds

To overcome the limitations of neural networks and symbolic AI,  researchers are turning to a different approach, a blend called NeuroSymbolic AI. The idea is to let neural networks do what they’re good at, like recognizing patterns in images or language, while symbolic AI handles the logic, rules and reasoning. When they work together, they can catch each other’s mistakes and make smarter decisions overall. What I like most is that this blend is the most similar to the human brain. 

In fact, we use this same powerful combination in everyday thinking. When reading a sentence with typos like "I lvoe choclate ice crem," you instantly recognize the words (pattern matching from memory) while logically deducing the intended meaning based on context and grammar rules. Your brain doesn't just rely on memorized words or pure logic alone, it's the combination that makes understanding effortless. NeuroSymbolic AI strives to achieve this same balanced approach in machines.

A flow chart showcasing the benefits of NeuroSymbolic AI.

Let’s dive deeper into how this balanced approach works in practice. The neural component is responsible for handling raw inputs like images, sounds, or text and turning them into more structured, meaningful forms. These might include things like recognized objects in a photo, identified words in a sentence, or important patterns in audio. This process happens through layers in the neural network, as mentioned earlier. The data is first converted into numbers, then passed through several layers that pick up patterns and regularities, and finally output values that reflect how the system interprets the data. These values help turn the raw input into something more understandable and easier to work with.

Once the neural network finishes this step, the converted information is passed along to the symbolic part of the system. This part works differently, it uses symbols and follows clear rules to understand and reason about what the neural network has found. It makes sense of the output by comparing it to a set of known concepts and relationships stored in its knowledge base. This knowledge can include definitions of categories, descriptions of how different things are related, and logical rules about how they behave.

The symbolic system then uses reasoning to figure out what follows from the information it’s been given. It might look for conclusions that make sense based on the facts, check whether everything fits together without contradictions, or decide if certain actions are possible under specific conditions. Based on this reasoning, it selects the most logically consistent decision or response.

A workflow showing how Neurosymbolic AI processes information and manages logic. 

To see this in action, take self-driving cars for example. Neural networks can help the car see the world by spotting lane lines, reading traffic signs and recognizing pedestrians. But sometimes they can get it wrong. If a neural system mistakes a plastic bag floating across the road for a person, the symbolic part can step in and realize that what it’s seeing doesn’t make physical sense, since humans cannot fly. This can help the car be better aware of its surroundings, making a safer call. 

The same kind of teamwork can be used in healthcare. For example, with CT scans, neural networks can be used to flag possible tumors while symbolic AI double checks to make sure the results line up with the human anatomy. In cases like this, NeuroSymbolic AI can catch issues that a neural network or symbolic AI system alone would have missed. 

Conclusion: NeuroSymbolic AI, An Exciting Step Forward

While NueroSymbolic AI represents an exciting step forward, it's important to remember that it isn’t a magical solution. Like any new technology, it comes with its own set of challenges. 

The biggest hurdle lies in the mismatch with how neural networks and symbolic AI systems operate. Neural networks excel at finding patterns but struggle to explain their reasoning, while symbolic AI follows strict logical rules. Getting these two approaches to work together smoothly needs careful design, both technically and conceptually. Scaling the systems can also prove challenging. As problems grow more complex, maintaining the right balance of pattern recognition and rule following becomes tricky. 

So, is it the ultimate answer to AI’s limitations? Not yet. But I think it's a promising direction, one that moves us beyond the trade-offs of the past and toward systems that are more capable and ultimately more useful in the real world. The journey won’t be simple, but the destination, AI that truly understands what it’s doing, is worth the effort.  

• Miraj Yafi

References:

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“Deep Blue - Chess Engines.” Chess.Com, www.chess.com/terms/deep-blue-chess-computer. Accessed 23 May 2025. 

“Deep Blue.” IBM, www.ibm.com/history/deep-blue. Accessed 23 May 2025. 

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Cole, Alan. “How Zillow’s Homebuying Scheme Lost $881 Million.” How Zillow’s Homebuying Scheme Lost $881 Million, Full Stack Economics, 18 Feb. 2022, www.fullstackeconomics.com/p/why-zillow-is-like-my-bad-fantasy-football-team.