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Friday, January 14, 2022

Deep Learning Can’t be Trusted

Deep Learning Can’t be Trusted,  Brain Modelling Pioneer Says Stephen Grossberg explains why his ART model is better  in Spectrum  IEEE

By  KATHY PRETZ During the past 20 years, deep learning has come to dominate artificial intelligence research and applications through a series of useful commercial applications. But underneath the dazzle are some deep-rooted problems that threaten the technology’s ascension.

The inability of a typical deep learning program to perform well on more than one task, for example, severely limits application of the technology to specific tasks in rigidly controlled environments. More seriously, it has been claimed that deep learning is untrustworthy because it is not explainable—and unsuitable for some applications because it can experience catastrophic forgetting. Said more plainly, if the algorithm does work, it may be impossible to fully understand why. And while the tool is slowly learning a new database, an arbitrary part of its learned memories can suddenly collapse. It might therefore be risky to use deep learning on any life-or-death application, such as a medical one.

Now, in a new book, IEEE Fellow Stephen Grossberg argues that an entirely different approach is needed. Conscious Mind, Resonant Brain: How Each Brain Makes a Mind describes an alternative model for both biological and artificial intelligence based on cognitive and neural research Grossberg has been conducting for decades. He calls his model Adaptive Resonance Theory (ART).

Grossberg—an endowed professor of cognitive and neural systems, and of mathematics and statistics, psychological and brain sciences, and biomedical engineering at Boston University—based ART on his theories about how the brain processes information.

“Our brains learn to recognize and predict objects and events in a changing world that is filled with unexpected events,” he says.

Based on that dynamic, ART uses supervised and unsupervised learning methods to solve such problems as pattern recognition and prediction. Algorithms using the theory have been included in large-scale applications such as classifying sonar and radar signals, detecting sleep apnea, recommending movies, and computer-vision-based driver-assistance software.

ART can be used with confidence because it is explainable and does not experience catastrophic forgetting, Grossberg says. He adds that ART solves what he has called the stability-plasticity dilemma: How a brain or other learning system can autonomously learn quickly (plasticity) without experiencing catastrophic forgetting (stability).

An illustration of a brain over a blue and red checkered pattern.  

Grossberg, who formulated ART in 1976, is a pioneer in modelling how brains become intelligent. He is the founder and director of Boston University’s Center for Adaptive Systems and the founding director of the Center of Excellence for Learning in Education, Science, and Technology. Both centers have sought to understand how the brain adapts and learns, and to develop technological applications based on their findings. .... ;

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