HN 提问：机器人能力研究是否会加速通用人工智能（AGI）的到来？
2 分•作者: themasterchief•25 天前
为了提供背景信息，我是一名大四的数学与计算机科学专业的本科生，正在考虑从事理论机器人领域的研究，特别是持续学习以及开发能够以类似人类的方式从环境中学习、适应/导航的机器人。然而，我有一个担忧，那就是这类研究可能会加速通用人工智能（AGI）的到来。具体来说，为机器人持续学习设计的架构似乎有可能迁移到通用AGI系统中（即使AGI系统是非具身化的，因为持续适应和长期目标追求等能力可能超越物理任务的范畴）。
这是一个合理的担忧吗？在AI安全社区中，这是一种普遍的看法吗？也就是说，主流的AI安全研究者是否认为这两个方向中的任何一个都会对AGI能力做出有意义的贡献？或者是否有充分的理由相信，机器人领域的持续学习研究不会显著加速AGI的进程？我希望能得到坦诚的看法。
**简而言之：** 机器人能力研究是否极有可能显著加速AGI的进程？如果可能，原因是什么？
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For context, I am a final-year math + CS undergraduate considering pursuing a career in theoretical robotics, particularly in continual learning and the development of robots that can learn from and adapt to / navigate their environments in a human-like manner. One concern I have, however, is that such research might advance AGI timelines. Specficially, it seems possible that architectures developed for continual learning in robots could transfer to general AGI systems (even if the AGI systems are non-embodied, since capabilities such as continual adaptation and long-term objective pursuit may generalize beyond physical tasks.)
Is this a valid concern, and is it a common view within the AI safety community? I.e. would mainstream AI safety researchers view either of these directions as meaningfully contributing to AGI capabilities? Or are there strong reasons to believe that work on continual learning in robotics would not significantly accelerate AGI timelines? Would appreciate honest perspectives.<p>TLDR: Is it very likely that robotics capabilities research meaningfully accelerates AGI timelines? If so, why?