Technology is continuously advancing and exponentially increasing the amount of data produced. Data comes from a multitude of sources and formats, requiring systems to process different algorithms.

AI R&D runs on a cycle of hypothesis, experiment, and analysis — each step demanding substantial manual engineering effort. A new framework from researchers at SII-GAIR aims to close that bottleneck ...