Deep-Tech Startup ABR Extends Battery Life with Ultra-Low-Power

Applied Brain Research (ABR) announce a new Algorithm that enables advances in ultra-low-power AI speech, vision and signal processing systems for always-on and edge-AI applications, extending battery life while making them more accurate. ABR's announcement demonstrates the potential to realize ultra-low-power instantiations of a large class of algorithms that learn patterns in data, spanning extraordinarily long intervals of time. Current algorithms, like Long Short-Term Memories (LSTMs), can learn and predict sequences of data for long periods of time and make it possible for neural networks to learn to process data like speech, video and control signals. Present in most smart speakers and voice recognition systems, LSTMs are said to be the most financially valuable AI algorithm ever invented (Forbes). LSTMs fail when tasked with learning temporal dependencies in signals than span 1,000 time-steps or more, making them very difficult to scale and limit commercial application.