Explore neuromorphic computing in this 33-minute conference talk by Ivan Schuller from the University of California, San Diego, delivered at the Fred Kavli Special Symposium during the APS March Meeting 2018. Delve into the concept of machines that work like the human brain and understand the limitations of the Turing-von Neumann paradigm. Examine crucial aspects such as computation limits, energy consumption versus time, and operational temperature ranges. Investigate the relationship between ATI and cognitive ability (EQ), and gain insights into global energy consumption related to computing. Address general questions and characteristics of neuromorphic systems, providing a comprehensive overview of this cutting-edge field in computational science.