The Impact of Continual Learning on AI Performance
Artificial intelligence (AI) is constantly evolving, but there’s still one major challenge scientists are trying to overcome: the issue of “catastrophic forgetting.” This is when AI agents lose the information gained from previous tasks as they learn new ones, hindering their overall performance. To tackle this problem, electrical engineers at The Ohio State University have delved into the complexities of continual learning in AI systems.
Understanding Continual Learning
Continual learning is the process of training a computer to continuously learn a sequence of tasks by utilizing its accumulated knowledge from old tasks to improve performance on new tasks. However, AI systems often struggle with remembering previous tasks, which can be problematic as society becomes more reliant on AI technology.
The Importance of Diverse Tasks
A team of researchers at Ohio State University discovered that AI systems can recall information better when faced with diverse tasks in succession, rather than tasks that share similar features. Just like humans, AI neural networks have an easier time remembering inherently different situations. These insights provide a bridge between how machines learn and how humans learn.
Optimizing Machine Learning Algorithms
Traditional machine learning algorithms are typically trained on data all at once, but the research team found that factors such as task similarity, correlations, and task order impact how long an artificial network retains certain knowledge. By teaching dissimilar tasks early in the continual learning process, AI algorithms can optimize their memory and improve their ability to subsequently learn similar tasks in the future.
By understanding the similarities between machines and the human brain, this research opens the door to a deeper understanding of AI. The ultimate goal is to develop intelligent machines that can learn and adapt like humans. This study was supported by the National Science Foundation and the Army Research Office.