Neuro-symbolic AI

Explanation

Imagine a child learning about animals. They see pictures of cats and dogs (data), but also learn rules like 'dogs bark' and 'cats meow' (symbols). Neuro-symbolic AI blends these two approaches.

It combines the pattern-recognition capabilities of neural networks (the 'neuro' part) with the reasoning and knowledge representation of symbolic AI (the 'symbolic' part).

Neural networks excel at learning from vast amounts of data, but struggle with abstract reasoning and explaining their decisions. Symbolic AI, on the other hand, uses explicit rules and logic to solve problems but requires extensive manual programming.

Neuro-symbolic AI seeks to leverage the strengths of both, creating systems that can learn from data, reason logically, and explain their actions in a human-understandable way.

Think of it as combining intuition with logic, allowing AI to not only identify patterns but also understand the underlying reasons behind them.

Examples

Consumer Example

Consider a smart home assistant that not only recognises your voice commands but also understands the context and reasons behind them.

Instead of simply turning on the lights when you say 'lights on,' it understands that it's nighttime, you're in the living room, and you're likely settling down to watch TV. It can then dim the lights, close the blinds, and turn on the TV, anticipating your needs based on learned patterns and logical reasoning.

Business Example

Imagine a bank using AI to detect fraudulent transactions.

A neuro-symbolic AI system can analyse transaction data to identify suspicious patterns (e.g., unusual amounts, locations) using neural networks. But it can also apply pre-defined rules (e.g., transactions exceeding a certain limit require additional verification) from symbolic AI.

If a suspicious transaction is detected, the system can provide a clear explanation of why it was flagged, based on both the learned patterns and the pre-defined rules, making it easier for human investigators to assess the risk.