What Does Neuro Symbolic Artificial Intelligence Mean?
Neuro Symbolic Artificial Intelligence, also known as Neuro Symbolic AI, is an advanced version of artificial intelligence (AI) that combines neural network architectures of deep learning with symbolic AI.
This combination of machine learning methodologies allows Neuro Symbolic AI to identify stand-alone objects relying on neural networks, then adds a layer of information on the object’s properties and distinct parts by applying symbolic reasoning. That 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 unique in its approach to data as it mimics the way human intelligence works. It uses the two major types of AI to bridge missing gaps in knowledge in the input. After all, Neuro Symbolic AI does not use these two types of intelligence separately, but simultaneously, so that users end up with an AI system that can "see" the objects and data presented to it.
In these types of processes, the collaborative efforts of multiple learning mechanisms rely on one another to authenticate information, eliminating the need for, and dependency on, human programmers. although a human in the loop (HITL) element is still often a good idea.
In a way, programmers can achieve highly similar results by only using neural networks. However, neural networks alone tend to require massive volumes of labeled training data to achieve sufficiently accurate results.
This advantage makes Neuro Symbolic AI ideal for researchers and programmers looking to train machines in pioneering fields where there is not enough labeled data to train a machine the old way.
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 among many others.
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.