How AI Algorithms Can Learn Like Humans With Continual Learning Training


  • AI algorithms are inching closer to mimicking human learning through continual learning training.
  • Researchers have identified factors for better AI memory retention in machine learning.
  • Understanding catastrophic forgetting in AI agents could lead to safer and more adaptive AI systems for various applications.

Continual learning is a promising aspect of AI algorithms that enables computers to continuously learn a sequence of tasks, building on accumulated knowledge to improve their performance. But, just like humans, machines face a challenge called “catastrophic forgetting,” where they tend to lose previously learned information while tackling new tasks. Electrical engineers have been delving into this phenomenon to understand how AI agents can overcome memory loss and learn more like humans. Their groundbreaking research offers insights into the complexities of continual learning and the potential for AI to mimic human learning capabilities, paving the way for more intelligent and adaptive machines.

Understanding catastrophic forgetting in AI algorithms

Catastrophic forgetting, a perplexing memory loss phenomenon experienced by AI agents, poses a formidable obstacle in the pursuit of true continual learning. As artificial neural networks embark on a sequence of tasks, they grapple with the issue of discarding the knowledge painstakingly acquired from previous tasks, potentially leading to critical implications as AI systems assume ever more indispensable roles in society. 

Ness Shroff, an esteemed Ohio Eminent Scholar and distinguished professor of computer science and engineering at The Ohio State University, accentuates the urgency of surmounting this challenge, particularly for critical applications like automated driving and robotics. The research endeavors to forge connections between the learning mechanisms of machines and humans, with the ultimate goal of bolstering the safety and adaptability of AI systems to navigate evolving environments and unforeseen circumstances effectively.

Recalling information through diverse tasks

The group of researchers involved in the study, comprising not only esteemed professors Yingbin Liang and Ness Shroff but also talented postdoctoral researchers Sen Lin and Peizhong Ju from Ohio State, stumbled upon a fascinating parallel between the memory processes of AI and humans. Their investigation revealed that artificial neural networks exhibit improved information recall when exposed to a series of diverse tasks in succession, as opposed to tasks that share similar features. 

Analogous to how humans may encounter difficulties in recalling contrasting facts about similar scenarios, machines display enhanced performance when confronted with inherently different tasks consecutively. This groundbreaking insight has the potential to revolutionize the very design of AI algorithms, enabling the optimization of their memory capacity and empowering them to effectively handle novel information and adapt to ever-changing environments with unparalleled proficiency.

Mimicking human learning capabilities

The team’s research holds the potential to enable dynamic, lifelong learning in autonomous systems, equipping them with the capabilities to scale up machine learning algorithms rapidly and adapt to unexpected situations. The goal is for these AI systems to mimic the learning capabilities of humans, where knowledge retention and adaptation are essential traits. Traditional machine learning algorithms are typically trained on data all at once, but the team’s findings indicate that factors like task similarity, order of training, and correlations play crucial roles in an algorithm’s memory retention. By identifying and optimizing these factors, researchers move closer to creating intelligent machines that learn and adapt like their human counterparts.

Understanding the parallels between AI and the human brain opens up new avenues for deeper insights into the world of artificial intelligence. By shedding light on the intricacies of continual learning, the research community moves closer to developing AI algorithms that can harness the power of memory and adaptability, thereby revolutionizing various industries and enhancing human-AI collaboration.

Disclaimer. The information provided is not trading advice. Cryptopolitan.com holds no liability for any investments made based on the information provided on this page. We strongly recommend independent research and/or consultation with a qualified professional before making any investment decisions.

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Aamir Sheikh

Amir is a media, marketing and content professional working in the digital industry. A veteran in content production Amir is now an enthusiastic cryptocurrency proponent, analyst and writer.

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