AI 101: What is Neuro-Symbolic AI?

Sometimes, when you can’t find a solution or when systems lack enough capabilities, it helps to build hybrids from what we already have. That’s also the idea behind neuro-symbolic AI (or neural-symbolic AI) – a concept that appeared long ago, evolved in waves, and is now seen by many as a strong path toward next-level AI.

What’s most remarkable about neuro-symbolic AI? It brings us even closer to human-like reasoning, mimicking how people use both logic and intuition in decision-making. Neuro-symbolic AI combines two worlds: 1) neural networks, which are great at learning patterns from data, and 2) symbolic systems, which excel at reasoning with structured knowledge and logic. This AI approach is researchers’ attempt to build systems that not only see and predict like neural nets but also understand and reason like humans.

Today, we’re going to look at its long and fascinating history – how this hybrid idea evolved over the years, the basics you need to know to build such systems, and what the future holds for neuro-symbolic AI to overcome.

In today’s episode, we will cover:

• How it all began: Symbolic and Connectionist AI

• The path of Neuro-Symbolic AI: Best of both traditions

• Why IBM sees neuro-symbolic AI as the way to AGI

• How to combine to parts?

• Symbolic representation in Neuro-Symbolic AI

• Advantages of Neuro-Symbolic AI

• Not without limitations

• Conclusion

• Sources and further reading

How it all began: Symbolic and Connectionist AI

To properly get the idea of neuro-symbolic AI, we need to look at its building blocks.

The first one is symbolic AI, which focuses on representing knowledge using symbols, logic, and rules. That’s actually a very logical way to look at AI, since computers operate based on logic and structured patterns. So it makes sense that, this way machines can naturally capture human knowledge.

Early AI systems (1950s–1980s) followed this symbolic approach. They were good at explaining their reasoning and could work with little data, but struggled with messy, real-world situations and couldn’t truly learn on their own. Among them are SHRDLU, ELIZA, DENDRAL, and MYCIN, which could follow rules or simulate simple conversations.

So to clearly sum it up, symbolic AI:

• Emphasizes logic, rules, and structured knowledge.

• For reasoning tasks, it uses human-readable symbols to represent objects, concepts, and relationships in the world.

• Provides clear and interpretable explanations for its decisions.

But they are slow and inflexible and can’t deal well with large amount of data.

The second part of neural-symbolic AI, in turn, was inspired by the human brain, and it’s called connectionist AI, or neural networks. Connectionist AI models intelligence as networks of simple connected units. These systems learn from large amounts of data by adjusting their internal weights. Since its beginnings in the 1940s, connectionism has gone through three major waves:

1. The first wave started in 1943 with early mathematical models of how neurons might work, such as the Perceptron.

2. The second wave began in the 1980s, when new techniques like hidden layers and sigmoid activation functions revived neural network research. These models were more powerful and flexible, and inspired debate about whether connectionism could replace traditional, rule-based symbolic AI.

3. And the third wave started in the 2010s when this approach led to today’s deep learning revolution, enabling image recognition, translation, and more.

So the strengths of neural networks are:

• They excel at identifying patterns and relationships in large amount of data.

• They are strong in perception tasks like image and speech recognition, NLP and others.

• Effective for generating predictions and “intuitive” ideas.

However, neural networks are often opaque (we don’t know why they make certain decisions), data-hungry, and weak at reasoning or combining ideas logically.

Here is what we have: two approaches with clear pros and cons. And usually when researchers want to overcome the limitations of two different methods, they turn to hybrid approach. And this is how neuro-symbolic AI also appeared.

The path of Neuro-Symbolic AI: Best of both traditions

Neuro-symbolic AI combines the strengths of two traditions: the learning power of neural networks with the reasoning ability and interpretability of symbolic logic. The goal is to build AI systems that can both learn from experience and reason logically about what they’ve learned.

In 1943, long before AI was formally established, Warren McCulloch and Walter Pitts in their work “A Logical Calculus of the Ideas Immanent in Nervous Activity” proposed one of the first mathematical models of neurons, creating the conceptual bridge between neural computation and symbolic logic. In this model, simple wired-up “neurons” could perform logical operations such as AND, OR, and NOT. To be more precise, it described how logic could emerge from neurons, not how neurons could learn logical structure.

Then, in 1957, came out Frank Rosenblatt’s research on Perceptron – a trainable model that links sensory inputs to symbolic responses through neuron-like units which learn by adjusting connection strengths.

The term “neuro-symbolic AI” came into use in the 1990s and early 2000s to describe neural networks that integrate or respect symbolic structure. This development was supported by several influential publications, including: