Rapid growth in machine learning and AI has made huge leaps in expanding the capabilities of machines. But this has come at the large energy cost of compute clusters which are making a bad impact on the environment. At the same time advances in computational neuroscience and neuromorphic engineering are converging and offering a viable bio-inspired alternatives to AI which perform with orders of magnitude less energy requirements, much faster latency, and many unique advantages.
My talk would inform the audience about this new trend and the many exciting developments happening in academia and the industry like bio-inspired neural networks and neuromorphic hardware platforms (Intel, Qualcomm, IBM, etc.).