Neuro-Symbolic AI: What do You Know About Next-Gen Artificial Intelligence?
Global Tech Outlook provides an overview on neuro-symbolic AI, the next-gen Artificial Intelligence
Being the major disruptive technology, artificial intelligence is disrupting the world and its functionalities with smart capabilities in recent years. The tech-driven world is set to experience the next-gen artificial intelligence known as neuro-symbolic AI that can help AI to reason through a knowledge-based question answering process. Neuro-symbolic AI is based on the basic foundations of deep learning and symbolic AI that can solve complicated questions with minimal training. It is well-known for its blend of neural network AI and symbolic AI. Neuro-symbolic AI is expected to enhance customer support, business intelligence, unusual discoveries, and many more. Symbolic AI system tends to achieve human-style comprehension with the combination of neural networks.
IBM has taken the Neuro-Symbolic AI (NS) initiative to conceive a fundamentally new methodology for the next-gen artificial intelligence. Researchers are aiming at augmenting the power of statistical AI with complementary capabilities of symbolic AI and create a revolution instead of evolution. The goals of neuro-symbolic AI include solving highly complicated problems, learning more tasks with less data, and offering understandable and controllable decisions for companies. There is a pursuit of understanding the natural language for efficiently managing the question-answer sessions, automatic data science as well as risk optimization.
Neuro-symbolic AI computing is a focused field of research for a few years to bring together robust learning in neural networks with sufficient reasoning through symbolic AI of network models. This next-gen artificial intelligence is still in a state-of-the-art stage in laboratories but it is expected to be successful in the nearby tech-driven future. This is known as the hybrid technology that can recognize the properties of objects and their explanations.
Symbolic AI tends to deal with symbols like different shapes, colours, and sizes of objects and stores these details in a knowledge base. There is a general rule that two objects are the same if any shape, colour or size is the same. All kinds of different symbols are encoded as a symbolic programme in a coding language that is easy for the computer system to have a clear understanding. But symbolic AI cannot work with minor and major problems in datasets. It fails to process when some data is missing from the knowledge base.
Neural network AI needs to be trained properly with different images to adjust the strengths of connections among its nodes to eradicate potential errors efficiently. The neural networks can translate between natural languages with image and speech recognitions.
The combination of symbolic AI and neural network AI has introduced neuro-symbolic AI to make smarter artificial intelligence. On one hand, symbolic AI provides common sense, reasoning, and know-how to deep learning and on the other hand, neural network AI acts as a guide in providing answers out of the real-time data to a symbolic representation through images.
That being said, there are multiple benefits of neuro-symbolic AI such as higher rate of accuracy in the decision-making process with less data, data efficiency, transparency and interpretability with policies of explainable AI.