Human beings have always directed extensive research on creating a proper thinking machine and a lot of researchers are still continuing to do so. Their primary goal has always been to create an intelligent machine. Research in this particular field has enabled us to create neural networks in the form of artificial intelligence.
While the neural networks trained by deep learning have paved the way for multiple breathtaking developments, artificial intelligence researchers agree that in order to process it further, the technology should not only think about ‘what’ but also about ‘why’ to increase the efficiency of the cause-effect relationships.
Contemporary deep learning models are limited in their ability to interpret while the requirement of huge amounts of data for learning goes on increasing. Due to these limitations, researchers are trying to look for new avenues by uniting symbolic artificial intelligence techniques and neural networks.
About Neuro-Symbolic AI
Neuro-Symbolic artificial intelligence uses symbolic reasoning along with the deep learning neural network architecture that makes the entire system better than contemporary artificial intelligence technology.
For example, we use neural networks to recognize the color and shape of an object. When symbolic reasoning is applied in this system, it will now have the ability to identify furthermore properties of the object such as its volume, total area, etc.
Conquering the shortcomings of both symbolic artificial intelligence and neural network
If we are to observe the thought process and reasoning of human beings, we will be able to find out that human beings use symbols as a crucial part of the entire communication process (which also makes them intelligent). In order to make machine think and perform like human beings, researchers have tried to include symbols in them.
The symbolic artificial intelligence is entirely based on rules, requiring the straightforward installation of behavioral aspects and human knowledge into computer programs. This entire process was not only inconvenient but it also made the system inaccurate and overpriced (whenever more and more rules were added to the system).
In order to tackle these types of problems, the researchers looked for a more data-driven approach and because of the same reason, the popularity of neural networks reached its peak. While symbolic AI requires every single piece of information, the neural network has the ability to learn on its own if it has been given a large number of data sets.
Due to the drawbacks of both systems, researchers tried to unify both of them to create neuro-symbolic AI which is individually far better than both of these technologies. With the ability to learn and apply logic at the same time, the system automatically became smarter.
Advantages of using Neuro-symbolic AI
Through neural networks, you can receive correct answers 80 percent of the time. So, what about the remaining 20 percent you might wonder? Well, self-driving cars are powered by this particular technology to recognize accuracy in 80 percent of situations while the rest 20 percent is human common sense.
This is why a human can understand the urgency of an event during an accident or red lights, but a self-driving car won’t have the ability to do the same with only 80 percent capabilities. Neuro Symbolic AI will be able to manage these particular situations by training itself for higher accuracy with little data.
You will require a huge amount of data in order to train modern artificial intelligence systems. While the human brain has the capacity to learn using a limited number of examples, artificial intelligence engineers need to feed huge amounts of data (in GBs) into an artificial intelligence algorithm. You only need 1 percent of data from traditional methods to train the neuro-symbolic AI systems.
Knows what to use
To analyze the street scenes, SingularityNET and Cisco make use of the OpenCog AGI engine along with deep neural networks. To comprehend the entire thing every camera is modeled through a neural network and it also uses a symbolic layer. For example, during an emergency situation, it will be able to pave the way (with lesser traffic) for an ambulance.
The whole purpose of neuro-symbolic networks is to combine the efforts of neural networks and perform better and more quickly than the same (but in an effortless way).
Although with time the task of neural networks has become more and more complex, neuro-symbolic AI is here to address the same issue. With an amalgamation of both systems, it has been possible to create an artificial intelligence system which will require very little data but has the capability to exhibit common sense, which in turn makes it more efficient and appropriate to perform complex tasks.