Making AI-Powered Devices Smart Using Neuromorphic Computing – Analytics Insight


AI-powered smart devices are getting their capabilities from neuromorphic computing

AI is powering smart products that are transforming industries with increased demand from customer-end and business-end. As per Accenture’s report, one smart home product division is projected to be worth US$135 billion by 2035. Soon, everything around us will be “smart” with devices that can be controlled by voice and gesture. From home entertainment to interiors and home improvement, devices will have increased autonomy freeing us of mundane activities. Robot vacuums have already started to take their place in homes, but imagine it replacing human cleaners at department stores? Like any other AI-powered device, smart solutions have the ability to collect data, analyze it, and monitor themselves to give maintenance updates.

Almost as Smart as the Human Brain

A branch of artificial intelligence, neuromorphic computing, leverages the functioning of smart devices. Neuromorphic computing architectures are inspired by the human brain, to give the system the ability to understand human commands and respond to them in the most human way.

The complete understanding of the human brain is an unraveled question for scientists. Ut the understanding that is known now is enough to comprehend the main principles of neural computation. Scientists and neurobiologists have collaborated to create algorithms and processes that mimic some of those main principles and functions of the brain.

The logic behind employing neuromorphic computing architectures for devices that require to run on high efficiency stems from the technology’s features. Neuromorphic systems are several times more energy-efficient than traditional computing architectures. These systems ace at processing humungous amounts of data continuously and deploying neuromorphic processes at the edge to reduce the lag in analysis. Because of intelligent technology, neuromorphic devices are highly adaptable to the environment and to the data from which they can learn and get deep insights.

Neuromorphic Computing and its Challenges

Neuromorphic hardware and software developments need a different approach because of the integration of two elements, memory, and processing. Programming languages will also need to be rewritten from the scratch. To cater to the new requirements, new generations of memory, storage, and sensor technology will be needed to leverage the full potential of neuromorphic systems.

Current generation AI is dependent on heavy rules and requires large datasets until it learns to generate outcomes. Neuromorphic devices are next generating artificial intelligence systems that can deal with problems that are at the human level like constraint satisfaction, a problem where the optimum solution is free of restrictions. Neuromorphic systems are better with probabilistic computing where traditional AI systems struggle with uncertain and noisy data.

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