AI Struggles with Learning Flexibility, Researchers Seek Cost-Effective Fixes

A recent study conducted by the University of Alberta has revealed a significant limitation in artificial intelligence (AI) models, particularly those trained using deep learning techniques. The study found that these AI models struggle to learn new information without having to start from scratch, an issue that underscores a fundamental flaw in current AI systems. The primary problem is the loss of plasticity in the "neurons" of these models when new concepts are introduced. This lack of adaptability means that AI systems cannot learn new information without undergoing complete retraining. The retraining process is both time-consuming and financially burdensome, often costing millions of dollars. This inherent rigidity in learning poses a considerable challenge to achieving artificial general intelligence (AGI), which would allow AI to match human versatility and intelligence. Despite the concerning findings, the researchers offered a glimmer of hope by developing an algorithm capable of "reviving" some of the inactive neurons, indicating potential solutions for the plasticity issue. Nonetheless, solving the problem remains complex and costly.

Challenges of Deep Learning-Based AI Models

One of the most glaring issues identified in the study is the lack of flexibility inherent in deep learning-based AI models. Unlike humans, who can adapt and assimilate new information with relative ease, AI systems find it incredibly challenging to acquire new knowledge without compromising previously learned information. When tasked with integrating new data, these models are often forced to undergo a complete retraining process. This retraining isn’t just a minor inconvenience; it is a significant business expense, often requiring millions of dollars and heaps of computational resources. For companies relying on AI, this means both economic and operational inefficiencies, making it difficult to justify frequent updates or changes to their AI systems.

Furthermore, the loss of neural plasticity in AI models makes it difficult for them to achieve what researchers term as lifelong learning. Lifelong learning is the ability to continuously acquire and apply new knowledge and skills throughout one’s life. For AI, this would mean adapting to new data sources or user inputs in real time without the need for restarting the learning process from scratch. The University of Alberta study underscores that the current state of AI technology is far from achieving this goal. The economic implications are substantial; organizations are likely to face continual expenditure on retraining AI models, thereby stifling innovation and hindering the widespread adoption of AI technologies. This challenge poses a roadblock on the path toward artificial general intelligence, a long-term objective for many researchers in the AI field.

Preliminary Solutions and Future Directions

A recent University of Alberta study has uncovered a significant limitation in artificial intelligence (AI) models, especially those using deep learning techniques. The research indicates that these AI models struggle to learn new information without needing to start from scratch, revealing a key flaw in current AI systems. The main issue is the loss of plasticity in the "neurons" of these models when new concepts are introduced. This lack of adaptability forces AI systems into complete retraining to learn new information, a process that is both time-consuming and financially demanding, often costing millions of dollars. This inherent rigidity is a major obstacle to achieving artificial general intelligence (AGI), which aims for AI to match human adaptability and intelligence. However, the researchers provided a hopeful note by developing an algorithm that can "revive" some inactive neurons, pointing to potential solutions for the plasticity issue. Even so, addressing this problem remains intricate and expensive, representing a significant challenge for the future development of adaptable AI systems.

Explore more

Business Central Mobile Apps Transform Operations On-the-Go

In an era where business agility defines success, the ability to manage operations from any location has become a critical advantage for companies striving to stay ahead of the curve, and Microsoft Dynamics 365 Business Central mobile apps are at the forefront of this shift. These apps redefine how organizations handle essential tasks like finance, sales, and inventory management by

Transparency Key to Solving D365 Pricing Challenges

Understanding the Dynamics 365 Landscape Imagine a business world where operational efficiency hinges on a single, powerful tool, yet many enterprises struggle to harness its full potential due to unforeseen hurdles. Microsoft Dynamics 365 (D365), a leading enterprise resource planning (ERP) and customer relationship management (CRM) solution, stands as a cornerstone for medium to large organizations aiming to integrate and

Generative AI Transforms Finance with Automation and Strategy

This how-to guide aims to equip finance professionals, particularly chief financial officers (CFOs) and their teams, with actionable insights on leveraging generative AI to revolutionize their operations. By following the steps outlined, readers will learn how to automate routine tasks, enhance strategic decision-making, and position their organizations for competitive advantage in a rapidly evolving industry. The purpose of this guide

How Is Tech Revolutionizing Traditional Payroll Systems?

In an era where adaptability defines business success, the payroll landscape is experiencing a profound transformation driven by technological innovation, reshaping how companies manage compensation. For decades, businesses relied on rigid monthly or weekly pay cycles that often failed to align with the diverse needs of employees or the dynamic nature of modern enterprises. Today, however, a wave of cutting-edge

Why Is Employee Career Development a Business Imperative?

Setting the Stage for a Critical Business Priority Imagine a workplace where top talent consistently leaves for better opportunities, costing millions in turnover while productivity stagnates due to outdated skills. This scenario is not a distant possibility but a reality for many organizations that overlook employee career development. In an era of rapid technological change and fierce competition for skilled