This year's advancements in the AI field are indeed worth a close look. Reinforcement learning enables robots to perform more stably and reliably in practical applications, which is not just an improvement in data but an upgrade in the system's adaptive capabilities in real-world environments. The addition of multimodal sensors is even more interesting — not only visual and auditory data but also tactile information are incorporated, directly expanding AI's understanding of the physical world.

Deeper changes lie in cognitive architecture. System 1 and System 2 are beginning to truly collaborate, handling longer task chains, which means that complex reasoning and quick responses are no longer mutually exclusive. Meanwhile, improvements in memory mechanisms aim to break through an old problem — the physical limitations of memory.

The key is that these theories, once confined to research papers, are now evolving into practical systems that can be deployed and self-repair. This transformation from concept to product is the fundamental driver pushing the industry forward. Based on these developments, expectations for 2026 will naturally differ.