Smiths(US:SMGZY) Xin Hua She·2026-01-29 04:13Core Insights - The future development of artificial intelligence (AI) may hinge on the concept of "lifelong learning," similar to human learning methods [1] - The rise of large language models (LLMs) has been a significant breakthrough in AI, but they have limitations, including a lack of continuous updating and causal reasoning capabilities [1][2] - Achieving "lifelong learning" in AI presents technical challenges, particularly in fine-tuning existing LLMs without compromising their performance [2] Group 1 - The most notable breakthrough in AI is the emergence of large language models, which can understand and generate text based on extensive data training [1] - Current AI systems, primarily based on LLMs, are often "frozen" after initial training, lacking the ability to grow and adapt over time [1] - LLMs excel at identifying correlations but struggle with causal reasoning, which limits their planning abilities and can lead to nonsensical outputs [1] Group 2 - Implementing "lifelong learning" in AI could mimic human learning processes, relying on small samples and selective data rather than vast amounts of information [2] - Robotics and embodied intelligence may enhance AI development by allowing interaction with the physical world, thereby accumulating experience and understanding causal relationships [2] - The future direction of AI includes the development of autonomous agents that can make independent decisions and collaborate with other agents to solve complex problems [2]