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Deep Learning Can’t be Trusted Brain Modelling Pioneer Says – IEEE Spectrum

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Stephen Grossberg explains why his ART model is better
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.
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.”
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.
A variety of climate-friendly strategies will be on show, along with the athletes
The National Speed Skating Oval (known as “The Ice Ribbon”) in Beijing will host speed skaters during the upcoming games. Ice here is formed using climate-friendly refrigeration. The facility also boasts outside architectural glass that includes photovoltaic elements, allowing the structure to generate electricity during the day.
About 160 kilometers northwest of Beijing, the city of Zhangjiakou with its rugged terrain boasts some of the richest wind and solar resources in China. Renewables account for nearly half of the city’s electricity output with less than a third of its full solar and wind potential of 70 gigawatts installed so far.
That makes it an ideal cohost with Beijing for the 2022 Winter Olympic and Paralympic Games, which China plans to make the greenest yet. The plan is to power all 26 venues fully with renewables, marking a first in the games’ history.
The Beijing 2022 Organising Committee aims to make the games carbon neutral, or as close as possible—a benchmark for the International Olympic Committee’s mission to make the Olympics carbon positive by 2024.
Besides being a symbol for President Xi Jinping’s ambitious goal of China being carbon neutral by 2060, the 2022 games should drive sustainable development in the region. The event has already helped Beijing clean up its skies and environment, and has fired up local energy-technology markets. It will also be a global stage to showcase new energy-efficiency, alternate-transport, and refrigeration technologies.
The Olympics will account for only a small fraction of the country’s annual electricity consumption. Powering them with clean energy sources won’t be difficult given China’s plentiful renewable capacity, says Michael Davidson, an engineering-systems and global-policy expert at the University of California, San Diego.
But Davidson also points out that insufficient infrastructure to manage intermittent renewables and electricity-dispatch practices that don’t prioritize them mean that much of China’s green-power capacity is often not put to use. And because the game venues are connected to a grid that is powered by a variety of sources, asserting that all the electricity used at the games is 100 percent from clean energy sources is “complicated,” he says. Nonetheless, the games will be important in raising the profile of green energy. “The hope is that this process will put into place some institutions that could help leverage a much broader-scale move to green.”
The Games will offer a global stage to showcase new energy-efficiency, alternate-transport, and refrigeration technologies.
Case in point: The flexible DC grid put into place in Zhiangjiakou in 2020 will let 22.5 billion kilowatt-hours of wind and solar energy flow from Zhiangjiakou to Beijing every year. By the time the Paralympics end in March, the game venues are expected to have consumed about 400 million kWh of electricity. If all of it is indeed provided by renewables, that should reduce carbon emissions by 320,000 tonnes, according to sports outlet Inside the Games. After the athletes go home, the flexible DC grid will continue to clean up around 10 percent of the capital’s immense electricity consumption.
Green transport infrastructure being built to shuttle athletes and spectators between venues will also be part of the games’ lasting legacy. A clean energy–powered high-speed railway that takes 47 minutes to travel between Beijing and Zhangjiakou was inaugurated in 2019. More than 85 percent of public-transport vehicles at the Olympics will be powered by batteries, hydrogen fuel cells, or natural gas, according to state media.
In August, officials at the Chinese capital revealed a five-year hydrogen-energy plan, with goals to build 37 fueling stations and have about 3,000 fuel-cell vehicles on the road by 2023, for which the Olympics should also be a stepping-stone. Already, hydrogen fueling stations built by China’s petrochemical giant Sinopec, Pennsylvania-based Air Products, and French company Air Liquide have cropped up in Beijing, Zhiangjiakou, and the Yanqing competition zone located in between.
In Yanqing alone, 212 fuel-cell buses made by Beijing-based Beiqi Foton Motor Co. will shuttle spectators around. Even the iconic Olympic torch will burn hydrogen for its flame.
Even the iconic Olympic torch will burn hydrogen for its flame.
The 2022 event will also put a limelight on climate-friendly refrigeration. The immense 12,000-square-meter speed-skating oval in downtown Beijing—8 times the size of a hockey rink—will be the first in the world to use carbon dioxide for making ice.
“We’ve built skating rinks with carbon dioxide direct cooling but never a speed-skating oval,” says Wayne Dilk of Toronto-based refrigeration company CIMCO Refrigeration, which has built most of the National Hockey League arenas in North America and designed and provided consulting services for the Olympics’ icy venues.
Ice-rink technology typically relies on refrigerants siphoning heat away from brine circulated under the floors, Dilk explains. But CO2-based cooling systems, which are getting more popular mainly in Europe and North America for supermarkets, food-manufacturing plants, and ice rinks, use CO2 both as the refrigerant and for transporting heat away from under the floor where it is pumped in liquid form.
CO2 is a climate villain, of course, but conventional hydrofluorocarbon refrigerants are worse. The common R-22 form of Freon, for example, is about 1,800 times as potent as a greenhouse gas. CO2 cooling systems are also 30 percent more energy efficient than Freon, says Dilk. Plus, the CO2 system produces higher-temperature waste heat, which can be used for space heating and hot water. And while the system is more expensive to build because it runs at higher pressure, the temperature across the large surface stays within a range of only 0.5 °C, giving more uniform ice. Consistent temperature and ice quality generate better competitive racing times. The Beijing 2022 hockey arenas and sliding center for bobsled and luge use climate-friendly ammonia or Opteon as refrigerants. Besides being a key part of the greenest Winter Olympics, these state-of-the-art ice venues should seal the deal for another goal China has in 2022: to establish itself as a world-class winter sports and tourism destination.
This article appears in the January 2022 print issue as “China’s Green Winter Olympics .”


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