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Why AI and human perception are too complex to be compared


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 has 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.

In their research, the scientist conducted a series of experiments that dig beneath the surface of deep learning results and compare them to the workings of the human visual system. Their findings are a reminder that we must be cautious when comparing AI to humans, even if it shows equal or better performance on the same task.

[Read: An introduction to one-shot learning]

The complexity of human and computer vision

In the seemingly endless quest to reconstruct human perception, the field that has become known as computer vision, deep learning has so far yielded the most favorable results. Convolutional neural networks (CNN), an architecture often used in computer vision deep learning algorithms, are accomplishing tasks that were extremely difficult with traditional software.

However, comparing neural networks to human perception remains a challenge. And this is partly because we still have a lot to learn about the human vision system and the human brain in general. The complex workings of deep learning systems also compound the problem. Deep neural networks work in very complicated ways that often confound their own creators.

In recent years, a body of research has tried to evaluate the inner workings of neural networks and their robustness in handling real-world situations. “Despite a multitude of studies, comparing human and machine perception is not straightforward,” the German researchers write in their paper.

In their study, the scientists focused on three areas to gauge how humans and deep neural networks process visual data.

How do neural networks perceive contours?

The first test involves contour detection. In this experiment, both humans and AI participants must say whether an image contains a closed contour or not. The goal here is to understand whether deep learning algorithms can learn the concept of closed and open shapes, and whether they can detect them under various conditions.