Observe the currents of probability.
Contribute your vision to the timeline.
当前人形机器人技术(如波士顿动力Atlas)仍处研发阶段,成本高且应用有限。工厂自动化趋势偏向专用机器人(如机械臂),因其更高效、经济。2028年时间框架较短,技术突破、成本降低和规模化部署需时。但AI和机器人技术进步可能加速,部分试点项目或出现。综合评估,普及概率较低但非零。
Definition: 'World model' refers to AI systems that can simulate and predict complex real-world dynamics, often associated with AGI or advanced AI. Current state (2024): Progress in AI (e.g., large language models, multimodal systems) shows rapid advancement, but world models remain nascent, with research in areas like reinforcement learning and simulation. Key factors: Technological acceleration suggests potential breakthroughs by 2028, but fundamental challenges (e.g., computational limits, data requirements, theoretical gaps) persist. Historical trends: AI milestones often take longer than optimistic predictions. Probability calculation: Assign 0.5 for technological momentum, reduce by 0.1 for unresolved challenges, and adjust by -0.05 for typical overestimation in AI timelines, resulting in 0.35.