Artificial Intelligence is morphing itself to the requirements of our enterprise applications and our consumer interests at the same time. As the number and type of AI services continue to grow, data engineering gurus are urging us to consider ever-more esoteric forms of automation intelligence. One of these strands is neuro-symbolic AI, an approach which aims to dovetail the human brain-like capacity of neural models with the human-readable intelligence represented by symbolic models.
What Is Neuro-symbolic AI?
To explain these technologies in the simplest terms possible, neural AI (often referred to as neural network technology) applies pattern recognition on large datasets based on the complex reasoning capabilities of the brain itself. As such, neural AI is great for working out smart city transport logistics based on an amassed set of sensor information, but it’s not so good at predicting when the next pop music phenomenon will hit, why someone is suffering from a rare and poorly documented disease, or when some other comparatively unique or deeply subjective event might happen.
A purely data-driven neural network might capture historical music preferences, but without an explicit understanding of generational shifts, socio-cultural trends and other symbolic or rule-based relationships, it could struggle to extrapolate far into the future.
By contrast, a neuro-symbolic approach could combine: data-driven patterns (how music genres have risen or fallen over time in different countries); logical/symbolic reasoning about demographic changes and tastes (ageing populations, birth-rate shifts, migration); and also contextual knowledge such as emerging cultural events and economic factors.
Let’s Play Ball
Let’s use American football as wider illustrative example to explore what neural AI and neuro-symbolic AI can achieve. While neural AI would be able to classify and distinguish among hundreds of different jerseys, team logos and player uniforms, it wouldn’t necessarily know which teams have the strongest rivalries or which players are considered the greatest of all time. Nor would it recognize (at an even more symbolic level) whether a particular jersey is traditionally worn during playoff games or brought out for a historic team celebration.
Symbolic AI is founded on rule-based reasoning based upon natural language that provides us with more transparency to see why decisions are being made. In contrast, neural AI is more of a black box as its pattern recognition engines churn away at a highly granular detail level.
The Application Of Neural-Symbolic AI
What all that brings us to is the point where we can say that neural-symbolic AI addresses previous limitations by blending the pattern-recognition strengths of neural networks with the contextual intelligence of symbolic systems. Rather than merely noting that a jersey is, say, blue and gold, it understands the jersey’s deeper meaning and whether it’s connected to a defining playoff moment, tied to a legendary player, or symbolizes a historic team rivalry. This richer understanding demonstrates how neural-symbolic AI can move beyond surface-level classification to offer context-driven insights about the sport.
“The term ‘symbolic’ relates to approaches based on the explicit representation of knowledge, logics and rules, often using formal languages and the processing of those language items (symbols) via algorithms,” writes Massimo Attoresi, on the European Data Protection Supervisor pages. “While neural networks have demonstrated their ability to learn from unstructured datasets and their efficiency and scalability in processing large amounts of data in dynamic environments, these ‘non-symbolic’ approaches, have shown their weaknesses, particularly in identifying new patterns from complex datasets.”
The Truth Behind LLMs
These truths thus far provide us with a realization that large language models are not, despite their appearances, conscious critical-thinking entities. For all of the nuance and intelligence that they can produce, LLMs are language pattern machines – an approximation of what the most likely thing to say next might be, when clear instructions (prompts) are provided.
Aiming to carve a name for itself in this sector of the AI universe is PebblesAi a company known for its AI-native go-to-market platform for marketing and sales. According to Emin Can Turan, CEO & lead researcher at Pebbles AI, “Neuro-symbolic AI requires rigorous research and domain-centric focus, unlike large language models, which can generate text on nearly any topic but rely on massive, often contradictory datasets. While this breadth might suffice for simple tasks, it becomes a liability in specialized fields like B2B marketing & sales, corporate law, or healthcare.”
It’s like picking a high-school athlete and throwing them straight into the NFL without any knowledge of professional playbooks, field strategies, or official rules he says. By contrast, neuro-symbolic AI is meticulously developed by researchers and technologists that also have deep expertize in the relevant domain, ensuring accurate results and ethical guardrails.
“Another key advantage is that neuro-symbolic AI can perform deterministic calculations alongside contextual reasoning, a complexity that generic LLMs struggle to handle,” said Turan. “Although LLMs seem convenient for all purposes at first glance, the precision and accuracy offered by neuro-symbolic AI make it far better suited for tasks where mistakes, subpar outputs, or dangerous recommendations simply aren’t acceptable.”
Specialized Specific Solutions
For example clarifies Turan, a neuro-symbolic AI could be used for archaic workflow process in the legal industry in big law firms, or solve problems in the go-to-market related workflows of B2B companies across different departments. Neuro-symbolic AI is argued to represent a step forward in the quest to build AI systems that can think and learn like humans, especially when we combine them with AI agents.
According to Oleksandr Knyga, AI director and Dmytro Antoniuk, AI lead, who head the Pebbles AI engineering team, modern reasoning systems implement agentic architectures that bridge neural and symbolic processing through structured task decomposition.
“The agent layer serves as a computational orchestration mechanism, managing the interplay between neural pattern extraction and symbolic rule application, thereby creating a robust framework for neuro-symbolic integration at the system level,” notes Knyga and Antoniuk.
Building such a neuro-symbolic AI is an exceptionally complex endeavor and it is one that essentially mirrors the multifaceted nature of the human mind by bringing together domain-specific skills, expertise and wisdom. This development could form a key part of the way we next build AI services from start to finish.
