How Neurosymbolic AI Can Fix Generative AI’s Reliability Issues

Generative AI has made impressive progress in recent years. It can write essays, create art and even compose music. But when it comes to getting the facts right, things often fall short. It could tell you with confidence that zebras live underwater or that the Eiffel Tower is in Rome. While these mistakes seem harmless, they point to a bigger problem: trust. In areas such as healthcare, law or finance, we cannot afford AI to make such mistakes.

This is where neurosymbolic AI can help. By combining the power of neural networks with the logic of symbolic AI, it could solve some of the reliability issues that generative AI faces. With neurosymbolic AI we can build systems that not only generate answers, but also generate answers we can trust.

Why generative AI is unreliable

Generative AI works by analyzing patterns in massive amounts of data. This way it predicts which word or image will come next. It’s like an advanced autocomplete tool that’s incredibly versatile, but doesn’t actually “know” anything. It’s just playing the odds. This dependence on probabilities can make it unpredictable. Generative AI doesn’t always choose the most likely option. Instead, it selects from a range of possibilities based on patterns it has learned. This randomness can make it creative, but it also means that the same input can lead to different outputs. That inconsistency becomes a problem in serious situations where we need reliable answers.

Generative AI doesn’t understand the facts. It mimics patterns and so it sometimes makes things up and presents them as real. This tendency of AI is often known as hallucination. For example, AI can make up a quote from a famous person or create a quote that does not exist. This is useful when we need to create new content, but can be a serious problem, especially when AI is used to provide advice on medical, legal or financial matters. It can mislead people into trusting information that is simply not true.

To make matters worse, when AI makes mistakes, it doesn’t explain itself. There is no way to check why it gave a certain response or how to fix it. It is essentially a black box, hiding its reasoning in a tangle of mathematical weights and probabilities. This may be fine if you’re asking for a simple recommendation or informal help, but it’s much more worrying if AI decisions start to impact things like healthcare, jobs, or finances. When an AI suggests a treatment or makes a hiring decision, it’s hard to trust if it doesn’t know why it chose that answer.

At its core, generative AI is a pattern matcher. It does not reason or think. It generates responses by mimicking the data it was trained on. That makes it human, but it also makes it vulnerable. A small change in input can lead to major errors. The statistical basis of AI is based on patterns and probabilities, making AI inherently random. This can result in very reliable predictions, even if those predictions are wrong. In high-stakes areas, such as legal advice or medical recommendations, this unpredictability and lack of reliability pose serious risks.

How neurosymbolic AI improves reliability

Neurosymbolic AI could solve some of these reliability issues of generative AI. It combines two strengths: neural networks that recognize patterns and symbolic AI that uses logic to reason. Neural networks are excellent at processing complex data, such as text or images. Symbolic AI checks and organizes this information using rules. This combination can create systems that are not only smarter but also more reliable.

By using symbolic AIwe can add reasoning to generative AI, where we verify the generated information against trusted sources or rules. This reduces the risk of AI hallucinations. For example, when an AI provides historical facts. Neural networks analyze the data to find patterns, while symbolic AI ensures that the output is accurate and logically consistent. The same principle can also be applied in healthcare. An AI tool may use neural networks to process patient data, but symbolic AI ensures that its recommendations align with established medical guidelines. This extra step ensures that the results remain accurate and well-founded.

Neurosymbolic AI can also bring transparency to generative AI. When the system reasons through data, it shows exactly how it arrived at an answer. For example, in the legal or financial industry, an AI might reference specific laws or principles it used to generate its suggestions. This transparency builds trust because users can see the logic behind the decision and have more confidence in the reliability of the AI.

It also brings consistency. By using rules to guide decisions, neurosymbolic AI ensures that responses remain stable even when the input is similar. This is important in areas like financial planning, where consistency is crucial. The logical reasoning layer keeps the AI’s output stable and based on solid principles, reducing unpredictability.

Combining creativity with logical thinking makes neurosymbolic generative AI smarter and safer. It’s not just about generating responses; it’s about generating responses you can count on. As AI becomes increasingly involved in healthcare, law, and other critical areas, tools like neurosymbolic AI offer a path forward. They provide the reliability and trust that really matter when decisions have real consequences.

Case study: GraphRAG

ChartRAG (Graph Retrieval Augmented Generation) shows how we can combine the powers of generative AI and neurosymbolic AI. Generative AI, like large language models (LLMs), can create impressive content, but often struggles with accuracy or logical consistency.

GraphRAG addresses this by combining knowledge graphs (a symbolic AI approach) with LLMs. Knowledge graphs organize information into nodes, making it easier to track connections between different facts. This structured approach ensures that the AI ​​remains rooted in reliable data while generating creative responses.

When you ask GraphRAG a question, it doesn’t just rely on patterns. It refers to the answers with trusted information in the graph. This extra step ensures logical and accurate responses, reducing errors or ‘hallucinations’ common with traditional generative AI.

The challenge of integrating neurosymbolic and generative AI

However, combining neurosymbolic AI with generative AI is not easy. These two approaches work in different ways. Neural networks are good at processing complex, unstructured data, such as images or text. Symbolic AI, on the other hand, focuses on applying rules and logic. Merging the two requires a balance between creativity and accuracy, which is not always easy to achieve. Generative AI is all about producing new, diverse results, but symbolic AI keeps everything based on logic. Finding a way to make the two work together without compromising performance is a difficult task.

Future directions to follow

Looking ahead, there is a lot of potential to improve the way neurosymbolic AI works with generative models. An exciting possibility is creating hybrid systems that can switch between the two methods depending on what is needed. For tasks that require accuracy and reliability, such as in healthcare or law, the system may rely more on symbolic reasoning. When creativity is needed, you can switch to generative AI. Work is also being done to make these systems more understandable. By improving how we follow their reasoning, we can build trust. As AI continues to evolve, neurosymbolic AI can make systems smarter and more reliable, making them both creative and reliable.

The bottom line

Generative AI is powerful, but its unpredictability and lack of understanding make it unreliable in high-stakes areas such as healthcare, law and finance. Neurosymbolic AI could be the solution. By combining neural networks with symbolic logic, it adds reasoning, consistency and transparency, reducing errors and increasing trust. This approach not only makes AI smarter, but also ensures that the decisions are reliable. As AI plays a greater role in critical areas, neurosymbolic AI offers a path forward – one where we can count on the answers AI provides, especially when lives and livelihoods are at stake.