Multitasking and continual learning models
Multitasking and continual learning models represent two pivotal areas of research in the field of AI and ML, each addressing distinct but complementary challenges related to the flexibility and adaptability of AI systems.
Multitasking models
Multitasking models, also known as multi-task learning (MTL) models, are designed to handle multiple tasks simultaneously, leveraging the commonalities and differences across tasks to improve the performance of each task. This hypothesis is grounded in cognitive science, suggesting that human learning often involves transferring knowledge across different but related tasks. In AI, this translates into models that can process and learn from multiple tasks simultaneously, optimizing shared neural network parameters to benefit all tasks involved.
The central idea is to share representations between related tasks to avoid learning each task in isolation, which can be inefficient and require more...
