The race to exascale isn’t the only rivalry stirring up the advanced computing space. Artificial intelligence sub-fields, like deep learning, are also inspiring heated competition from tech conglomerates around the globe.
When it comes to image recognition, computers have already passed the threshold of average human competency, leaving tech titans, like Baidu, Google and Microsoft, vying to outdo each other.
The latest player to up the stakes is Chinese search company Baidu. Using the ImageNet object classification benchmark in tandem with Baidu’s purpose-built Minwa supercomputer, the search giant achieved an image identification error rate of just 4.58 percent, besting humans, Microsoft and Google in the process.
An updated paper [PDF] from a team of Baidu engineers, describes the latest accomplishment carried out by Baidu’s image recognition system, Deep Image, consisting of “a custom-built supercomputer dedicated to deep learning [Minwa], a highly optimized parallel algorithm using new strategies for data partitioning and communication, larger deep neural network models, novel data augmentation approaches, and usage of multi-scale high-resolution images.”
“Our system has achieved the best result to date, with a top-5 error rate of 4.58% and exceeding the human recognition performance, a relative 31% improvement over the ILSVRC 2014 winner,” state the report’s authors.
The Baidu colleagues add that this is significantly better than the latest results from both Google, which reported a 4.82 percent error rate, and Microsoft, which days prior had declared victory over the average human error rate (of 5.1 percent) when it achieved a 4.94 percent score. Both companies were also competing in the ImageNet Large Scale Visual Recognition Challenge.