Researchers at Fujitsu and the MIT Center for Brains, Minds and Machines (CBMM) have achieved a "major milestone" in the quest to bolster the accuracy of AI models tasked with image recognition. As described in a new paper presented at NeurIPS 2021, the collaborators have developed a method of computation that mirrors the human brain to enable AI that can recognize information that does not exist in its training data (also called out-of-distribution data, or ODD). Although AI is already used for image recognition in a range of contexts (e.g. the analysis of medical x-rays), the performance of current models is highly sensitive to the environment. The significance of AI capable of recognizing ODD is that accuracy is maintained in imperfect conditions – for example, when the perspective or light level differs from the images on which the model was trained. MIT and Fujitsu achieved this feat by dividing deep neural networks (DNNs) into modules, each of which is responsible for recognizing a different attribute, such as shape or color, which is similar to the way the human brain processes visual information.

The news: WhyLabs, a spinout from the Seattle's Allen Institute for Artificial Intelligence (AI2), raised $10 million and released a new tool to support machine learning applications. The problem: As more companies leverage machine learning and artificial intelligence, the need to capture and correct failures is becoming more urgent. Reliance on algorithms can lead to negative implications, as evidenced this week by Zillow Group, for example. "The challenges begin once the machine learning system is live -- it automates millions of decisions a day," a WhyLabs spokesperson told GeekWire in an email. "Monitoring how well it's working becomes critical, because machine learning systems fail in often catastrophic ways."

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