Neuromorphic Computing

Machine learning and deep learning has never been more advanced than it is today, with computing power evolving tremendously and becoming capable of processing information and learn from larger data sets than ever before. An emerging type of computing, known as neuromorphic computing, is making breakthroughs and becoming popular with AI and neuroscience researchers.

Before you understand neuromorphic computing, it is important to understand what artificial intelligence or AI is. AI is a system of computing that is not reliant on explicit programs in order to perform particular tasks. An AI system is capable of analyzing the input data and creating the perfect response based on that data. This is the basis for machine learning and deep learning using neural networks.

Neuromorphic computing is very similar to how the human brain functions. It makes use of physical linkages similar to neurons.

One of the latest trends in the field involves SNN, also known as spiking neural networks. The properties that SNN’s have make them perfect for being used in a silicon circuit. Therefore, software is being replaced with silicon, whereby the cognitive and computing power of neural nests is being drastically enhanced, along with additional benefits of portability and energy efficiency. All of these are hugely beneficial for operations that need to be done offsite, on the field.

One potential application of artificial intelligence is that it can be used to replicate algorithms and thereby you can use it to create clones of the most efficient workers. Cyber-attacks in the modern world have become very advanced, with automated forces being used to target companies and even countries. There is no room for even a little miscalculation while defending against such attacks. Therefore, AI and neuromorphic computing could become a popular trend in the computing world, able to thwart automated attacks.