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Learn more - Join the world's largest professional organization devoted to engineering and applied sciences and get access to all of Spectrum's articles, archives, PDF downloads, and other benefits. Learn more - CREATE AN ACCOUNTSIGN IN JOIN IEEESIGN IN Enjoy more free content and benefits by creating an account Create an account to access more content and features on IEEE Spectrum, including the ability to save articles to read later, download Spectrum Collections, and participate in conversations with readers and editors. For more exclusive content and features, consider Joining IEEE. CREATE AN ACCOUNTSIGN IN The Institute Topic Artificial Intelligence Type Article Deep Learning Can't Be Trusted, Brain Modelling Pioneer Says Stephen Grossberg explains why his ART model is better Kathy Pretz 30 Dec 2021 4 min read Photo of a man in glasses and a blue checkered shirt. Stephen Grossberg artificial intelligence deep learning brain Boston University ieee member news 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. For Grossberg's "contributions to understanding brain cognition and behavior, and their emulation by technology," he received the 2017 IEEE Frank Rosenblatt Award, named for the Cornell professor considered by some to be the "father of deep learning." Grossberg attempts to explain in his nearly 800-page book how "the small lump of meat that we call a brain" gives rise to thoughts, feelings, hopes, sensations, and plans. In particular, he describes biological neural models that attempt to explain how that happens. The book also covers the underlying causes of conditions such as Alzheimer's disease, autism, amnesia, and post-traumatic stress disorder. "Understanding how brains give rise to minds is also important for designing smart systems in computer science, engineering and tech, including AI and smart robots," he writes. "Many companies have applied biologically inspired algorithms of the kind that this book summarizes in multiple engineering and technological applications." The theories in the book, he says, are not only useful for understanding the brain but also can be applied to the design of intelligent systems that are capable of autonomously adapting to a changing world. Taken together, the book describes the fundamental process that enables people to be intelligent, autonomous, and versatile. THE BEAUTY OF ART Grossberg writes that the brain evolved to adapt to new challenges. There is a common set of brain mechanisms that control how humans retain information without forgetting what they have already learned, he says. "We retain stable memories of past experiences, and these sequences of events are stored in our working memories to help predict our future behaviors," he says. "Humans have the ability to continue to learn throughout their lives, without new learning washing away memories of important information that we learned before." Understanding how brains give rise to minds is also important for designing smart systems in computer science, engineering, and tech, including AI and smart robots. One of the problems faced by classical AI, he says, is that it often built its models on how the brain might work, using concepts and operations that could be derived from introspection and common sense. "Such an approach assumes that you can introspect internal states of the brain with concepts and words people use to describe objects and actions in their daily lives," he writes. "It is an appealing approach, but its results were all too often insufficient to build a model of how the biological brain really works." The problem with today's AI, he says, is that it tries to imitate the results of brain processing instead of probing the mechanisms that give rise to the results. People's behaviors adapt to new situations and sensations "on the fly," Grossberg says, thanks to specialized circuits in the brain. People can learn from new situations, he adds, and unexpected events are integrated into their collected knowledge and expectations about the world. ART's networks are derived from thought experiments on how people and animals interact with their environment, he adds. "ART circuits emerge as computational solutions of multiple environmental constraints to which humans and other terrestrial animals have successfully adapted...." This fact suggests that ART designs may in some form be embodied in all future autonomous adaptive intelligent devices, whether biological or artificial. "The future of technology and AI will depend increasingly on such self-regulating systems," Grossberg concludes. "It is already happening with efforts such as designing autonomous cars and airplanes. It's exciting to think about how much more may be achieved when deeper insights about brain designs are incorporated into highly funded industrial research and applications." From Your Site Articles * How Deep Learning Works - IEEE Spectrum > * Stop Calling Everything AI, Machine-Learning Pioneer Says - IEEE ... > Related Articles Around the Web * Stephen Grossberg's Conscious Mind, Resonant Brain | BU Today ... > * What is deep learning and how does it work? > * What Is Deep Learning AI? A Simple Guide With 8 Practical Examples > artificial intelligence deep learning brain Boston University ieee member news Kathy Pretz Kathy Pretz is editor in chief for The Institute, which covers all aspects of IEEE, its members, and the technology they're involved in. She has a bachelor's degree in applied communication from Rider University, in Lawrenceville, N.J., and holds a master's degree in corporate and public communication from Monmouth University, in West Long Branch, N.J. The Conversation (4) [defa] Brian Josephson 06 Jan, 2022 H In connection with the theme of the article, the physics preprint archive arXiv has as its aim fostering communication between scientists, but operates in ways that may work against this by refusing to accept preprints that propose new ideas but are otherwise perfectly satisfactory, or refusing to allow preprints to be listed in the area of the archive where they would be most likely to be viewed by interested readers. I suspect algorithms are responsible for these tendencies, which have been commented on by a number of people. It has been admitted that algorithms provide preliminary filtering, and the management team has written (see https:// prod-physicsworld-iop.content.pugpig.com/blog/2021/11/29/ in-moderation/pugpig_index.html ) of a 'moderation process that verifies material is appropriate and topical'. My guess is that the algorithms decide what is 'appropriate and topical' on the basis of having been trained on papers that have recently been published. While this might at first sight seem very reasonable, such a process will discriminate against new thinking, which is less likely to fit the usual current patterns and be deemed acceptable by the algorithms. This outcome may well have serious consequences for the advance of science. Problems with the way the archive functions have been brought to the attention of the administrators but have not as yet met with a satisfactory response. 0 Replies Hide replies Show More Replies [defa] Jose Vanderhorst 06 Jan, 2022 LS Yes: "Deep learning can't be trusted" but: biology doesn't apply to all situations of inquiry and: the late Charles Sanders Peirce upgraded the scientific method for those other situations. 0 Replies Hide replies Show More Replies [defa] William Adams 05 Jan, 2022 LS Artificial Intelligence is genuine stupidity that has been overhyped and under performs when you consider both the problems it caused not just what it actually did did rirht. AI is just fast programs crunching big data to assume that correlation is causation. 0 Replies Hide replies Show More Replies An illustration of four people talking with a variety of word bubbles over their heads. The Institute Topic Type News Careers IEEE WIE Conference Will Explore the Future of Work 05 Jan 2022 3 min read illustration of head showing brain and covid virus symbols in background Biomedical Topic Type News Zapping the Brain and Nerves Could Treat Long COVID 05 Jan 2022 7 min read Line drawing of a solar panel with a pattern of connected lines on it on it, with a net of lines radiating out from it. Topic News Type Energy New Simulator to Speed Up Solar Cell Development 04 Jan 2022 2 min read More from The Institute Topic The Institute News Type IEEE Honors Pioneering Engineers Type The Institute Article Topic Access South Africa's Leading Research Journal's Entire Collection The Institute Topic Type Article History of Technology The First U.S. Human-Operated Submersible Changed the Course of Oceanography The Institute Topic Article Type Careers 5 Courses to Beef Up Your Knowledge of Blockchain Technology The Institute Topic Article Type Telecommunications Solutions for Providing Internet Access to Rural Areas Get unlimited IEEE Spectrum access Become an IEEE member and get exclusive access to more stories and resources, including our vast article archive and full PDF downloads JOIN IEEESIGN IN Get access to unlimited IEEE Spectrum content Network with other technology professionals Establish a professional profile Create a group to share and collaborate on projects Discover IEEE events and activities Join and participate in discussions Aerospace Topic Magazine Type Feature Special reports Top Tech 2022: A Special Report Preview two dozen exciting technical developments that are in the pipeline for the coming year IEEE Spectrum 03 Jan 2022 1 min read Photo of the lower part of a rocket in an engineering bay. NASA's Space Launch System will carry Orion to the moon. Frank Michaux/NASA At the start of each year, IEEE Spectrum attempts to predict the future. It can be tricky, but we do our best, filling the January issue with a couple of dozen reports, short and long, about developments the editors expect to make news in the coming year. This isn't hard to do when the project has been in the works for a long time and is progressing on schedule--the coming first flight of NASA's Space Launch System, for example. For other stories, we must go farther out on a limb. A case in point: the description of a hardware wallet for Bitcoin that the company formerly known as Square (which recently changed its name to Block) is developing but won't officially comment on. One thing we can predict with confidence, though, is that Spectrum readers, familiar with the vicissitudes of technical development work, will understand if some of these projects don't, in fact, pan out. That's still okay. Engineering, like life, is as much about the journey as the destination. See all stories from our Top Tech 2022 Special Report From Your Site Articles * January 2022 - IEEE Spectrum > * Top Tech 2022 - IEEE Spectrum > * 12 Exciting Engineering Milestones to Look for in 2022 - IEEE ... > Related Articles Around the Web * Forrester's Predictions 2022: This Is A Year To Be Bold > * Ten tech predictions for 2022: what's next for Twitter, Uber and NFTs ... > * Gartner's 2022 Top Predictions For Technology Organizations ... > Keep Reading | Show less