This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence.

Human-level performance. Human-level accuracy. Those are terms you hear a lot from companies developing artificial intelligence systems, whether it’s facial recognition, object detection, or question answering. And to their credit, the recent years have seen many great products powered by AI algorithms, mostly thanks to advances in machine learning and deep learning.

But many of these comparisons only take into account the end-result of testing the deep learning algorithms on limited data sets. This approach can create false expectations about AI systems and yield dangerous results when they are entrusted with critical tasks.

In a recent study, a group of researchers from various German organizations and universities have highlighted the challenges of evaluating the performance of deep learning in processing visual data. In their paper, titled, “The Notorious Difficulty of Comparing Human and Machine Perception,” the researchers highlight the problems in current methods that compare deep neural networks and the human vision system.

#blog #ai research papers #artificial intelligence #computer vision #deep learning