We’ve seen the positive impact of AI in various industries that include increased productivity, enhanced processes, deeper insights, reduced labor-intensive tasks, and fewer errors that overall contribute to growth.

AI has significantly progressed due to breakthroughs made in deep learning, artificial neural networks, and big data. However, it still has areas for enhancement. One such challenge is the need to develop AI hardware that can handle complex computations than traditional systems can’t. At present, many AI systems still require some form of human intervention to label training data properly.

Another issue is the lack of generalized training tactics that would enable AI systems to apply their training or learning from one circumstance to another. Experts believe the answer lies in neuromorphic computing. Can it live up to expectations?

What is Neuromorphic Computing?

Neuromorphic computing is not a new concept. It was introduced as early as the 1980s by Carver Mead. It aims to mimic how the human mind works when coming up with decisions by deducing inferences and memorizing data.

Neuromorphic computing aims to develop machines that can learn to recognize patterns independently and perform analysis with fewer data inputs while using less memory compared to a digital neural network. That requires using chips or artificial neurons that function like synapses or biological neurons. That revised structure will make them more efficient than neural networks. Learn more in this video.

Neuromorphic Computing Tweaks for It to Benefit AI

AI systems are currently limited when it comes to what problems they can solve. The solutions depend on the data they receive. They can’t generalize knowledge and put context to information as humans do. That disables them from applying a resolution process for one issue to a similar circumstance.

While steps toward the right direction have been made, neuromorphic computing still has a long way to go. To maximize its potential, experts believe the technology needs to meet additional requirements, which include:

  • In-memory computing: Fetching data from remote places is resource-intensive. Neuromorphic computers need to store information in the same place where analysis occurs. Using electronic processing semiconductors that combine transistors (memory processors) with storage can help with this.
  • Parallelism: Computers typically work linearly. But for neuromorphic computing to benefit AI, systems must be made to work like the human brain. They must be able to perform numerous operations simultaneously. That translates to extending the capabilities of graphical processing units (GPUs) that create large-scale graphics using simultaneous calculations called “matrix multiplications.”
  • Probabilistic computing: Instead of performing precise calculations, it may be more helpful if systems instead come up with certain degrees of probability like the human brain, which requires less information.

From what we’ve read, neuromorphic computing seems to be more than just another AI-related buzzword. Current studies and experiments show that it can, indeed, benefit AI. It can enable AI systems to function more like the human brain that they are meant to imitate. But as we’ve also seen, the technology still needs much exploration before it can revolutionize today’s AI systems.

Neuromorphic Computing Can Take AI to the Next Level.
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