Smarter AI Through Quantum, Neuromorphic, and High-Performance Computing

The current AI and Deep Learning of the present era have a few shortcomings like training a deep net can be very time-consuming, cloud computing can be costly and unavailability of sufficient data can also be a problem. To be rid of these, the scientists are all set in their search for a smarter version of AI, and there seem to be three ways they can progress in the future. Within the process of improving AI, the most focus is on high-performance computing. It is based on the deep neural net but aims to make them faster and easier to access. It aims to provide better general-purpose environments like TensorFlow, and greater utilization of GPUs and FPGAs in larger and larger data centers, with the promise of even more specialized chips not too far away.