Deep Learning’s Diminishing Returns

While room-temperature quantum qubits have been around experimentally for more than 20 years, Quantum Brilliance's contribution to the field is in working out how to manufacture these tiny things precisely and replicably, as well as in miniaturizing and integrating the control structures you need to get information in and out of the qubits. Deep learning is now being used to translate between languages, predict how proteins fold, analyze medical scans, and play games as complex as Go, to name just a few applications of a technique that is now becoming pervasive. Success in those and other realms has brought this machine-learning technique from obscurity in the early 2000s to dominance today. Although deep learning's rise to fame is relatively recent, its origins are not. In 1958, back when mainframe computers filled rooms and ran on vacuum tubes, knowledge of the interconnections between neurons in the brain inspired Frank Rosenblatt at Cornell to design the first artificial neural network, which he presciently described as a "pattern-recognizing device."