Progress in training artificial neural networks reveals that complex biological modeling may not be necessary for achieving impressive outcomes. Innovations are shifting towards energy-efficient hardware architectures that activate only essential network components, enhancing computational efficiency. This evolution highlights the potential of alternative strategies, such as analog circuitry and attention mechanisms, in optimizing performance.
