IBM Research Distributed Deep Learning code breaks accuracy record for image recognition

Deep learning systems continue to gain widespread adoption in the enterprise, tackling photo and voice recognition, customer service interactions, and even spotting abnormalities in medical records. But while the artificial intelligence (AI) models, which rely on massive data sets to "train" themselves on recognizing patterns and making predictions--throughout multiple iterations--timing is still an obstacle. Developing an accurate deep learning model can take up to days, or even weeks. On Tuesday, a new deep learning model developed by IBM Research--Distributed Deep Learning--made big strides in the field: It achieved a record for image recognition accuracy of 33.8%. The model, which used a massive data set of 7.5 million images, achieved "record communication overhead and 95% scaling efficiency on the Caffe deep learning framework over 256 GPUs in 64 IBM Power systems," according to IBM--all in just seven hours.