It looks like Neuromorphic computing is one of the most used buzzwords, connected with AI and machine learning. In this Artificial Intelligence Insight Series - Neuromorphic Chipsets, we’ll show how NC chipsets could improve on ML and possibly make artificial intelligence better than it already is.
As you might already know, most of the machine learning activities that we have today could be carried out on a prefabricated circuitry. In this sense, it requires prior connections, information or data to build its decisions on. That’s very unlike our brains that are able to create new connections or decisions without any of the prior data.
Neuromorphic chips are termed as analog data processors that have been designed just like our brains. In fact, ‘neuro’ translates to nerves, or nervous system and ‘morphic’ refers to having an identical structure or form.
Certain aspects of the design structure in the chips allow it to interpret sensory data so that it can respond in ways that aren’t specifically programmed into it. To make this possible, the neuromorphic engineering that goes into their making involves the addition of physics and biology to computer science, electronic engineering and mathematics create ANN or artificial neural network artificial neural network.
The chips are now advanced enough to be used with AI/ML in a human-like method. When used in technology we have now, it adds capabilities like adapting to changes, faster damage recovery and develop via learned principles.
Potential Uses for Neuromorphic Chips:
· They utilize much less energy and this means they can be used many kinds of devices.
· Identify external environments using sensors and then respond using audio cues.
· Identify patient’s records to search for warning signs or notify on treatment adjustments.
· Smartphones making suggestions based on interrelated surroundings or applications.
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