The New Era of AI: Unlocking the Brain’s Power
- Saanvi Parige
- Nov 4, 2024
- 2 min read
Nothing short of a technological revolution, neuromorphic computing is a growing field in computer engineering that aims to mimic/replicate the brilliance of the human brain. The radical shift in computing breaks away from the rigid processing of international systems– which process information using binary logic. Neuromorphic systems are designed to operate more like neural networks. This allows for more efficient processing of tasks. The computer will be able to adapt, think and learn just like a human brain.
One of the features of neuromorphic chips is their use of spiking neural networks (SNNs), which simulate the way neurons in the brain communicate (through electrical impulses). This enables the hardware to process data asynchronously, significantly reducing power consumption. When compared with traditional systems, neuromorphic processors can perform more complex tasks with far less energy, which makes them efficient for AI applications.
Cutting edge companies like Intent and IBM are pioneering this technology with chips– Intel’s Loihi 2 and IBM’s TrueNorth. Intel’s Loihi includes over one million artificial neurons that are able to adapt in real time, similar to human/biological brains. Their new ability to adjust based on stimuli opens doors to incredible opportunities in healthcare and robotics.
Neuromorphic commuting also holds significant promise in fields like robotics, autonomous vehicles and healthcare. Neuromorphic chips can be used to develop prosthetics that communicate directly with the human nervous system, providing real-time feedback to users, for example. This has the ability to elevate artificial intelligence beyond today’s static algorithms to create countless systems capable of unsupervised learning and cognitive computing.
As the demand for energy-efficient AI increases, neuromorphic computing is expected to play a pivotal role in next-generation technologies. Its potential for creating smarter and more adaptable machines positions it as a key technology for future AI and robotic systems.
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