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Neuro Symbolic Artificial Intelligence, also known as neurosymbolic AI, is an advanced version of artificial intelligence (AI) that improves how a neural network arrives at a decision by adding classical rules-based (symbolic) AI to the process. This hybrid approach requires less training data and makes it possible for humans to track how AI programming made a decision.
In image recognition, for example, Neuro Symbolic AI can use deep learning to identify a stand-alone object and then add a layer of information about the object’s properties and distinct parts by applying symbolic reasoning. This way, a Neuro Symbolic AI system is not only able to identify an object, for example, an apple, but also to explain why it detects an apple, by offering a list of the apple’s unique characteristics and properties as an explanation.
Neuro Symbolic AI is expected to help reduce machine bias by making the decision-making process a learning model goes through more transparent and explainable. Combining learning with rules-based logic is also expected to help data scientists and machine learning engineers train algorithms with less data by using neural networks to create the knowledge base that an expert system and symbolic AI requires.
Techopedia Explains Neuro Symbolic Artificial Intelligence
In many circles, Neuro Symbolic AI is considered to be the next AI evolution. Even in their early stages, Neuro Symbolic AI systems are expected to offer unprecedented advantages in areas such as computer vision, cybersecurity and robotics.
It's important to note that programmers can achieve similar results without including symbolic AI components. However, neural networks require massive volumes of labeled training data to achieve sufficiently accurate results — and the results cannot be explained easily.
The Strengths of Neuro Symbolic Artificial Intelligence
Neuro Symbolic AI not only combines highly-acclaimed AI and machine learning approaches, but it also manages to bypass the majority of weak points and disadvantages that come with using each system separately.
- Data Efficiency – The average Neuro Symbolic AI system can be trained with as little as one percent of the amount of data that would otherwise be required for traditional machine learning methods. This relieves data scientists from having to collect massive volumes of accurate data, and it also saves them the time and effort needed to organize and label the individual data points.
- High Accuracy – Both neural networks and symbolic AI have high degrees of accuracy. However, the percentages are not high enough for them to be used in high-risk scenarios that require accurate and swift responses (such as self-driving cars.)
For example, neural networks’ accuracy averages around 80 percent. In Neuro Symbolic AI, neural networks and symbolic AI overlay to bridge any gaps in accuracy and produce more reliable results. - Data Transparency – Self-learning AI systems make decisions using an underlying algorithm that they designed themselves, leaving the ones who created the system unaware of the methodology the program used to reach its conclusion. Neuro Symbolic AI, on the other hand, eliminates this issue by offering complete transparency, showing its users how it reached the final result.
Projects and Breakthroughs
Neuro Symbolic AI systems are far from a fringe idea with a niche audience. In fact, researchers at MIT, IBM, and Harvard are leading the way. Their most notable project is CLEVRER, a large video-reasoning database that can be used to help AI systems better recognize objects in videos, and track and analyze their movement with high accuracy.
Overall, Neuro Symbolic AI systems can be used to make smarter machines than before. They should be able to succeed where older technology failed, like in accurately identifying blurry images and even teach itself to identify skin cancer cells with a high degree of accuracy when compared to trained physicians.
