How a New Algorithm Cuts Imaging and AI Costs for SMBs
A student developed algorithm from the University of Hawaiʻi promises to transform how machines detect orientation in noisy two dimensional data. By minimizing a continuous Frobenius norm it offers a low compute, interpretable prealignment that could cut AI training costs and boost imaging, medical scans and robotics. Read more now.