B1259 字1:37新闻报道英国腔发布于 2026年7月15日 · 更新于 2026年7月15日

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In Chennai, southeastern India, a 25-year-old homemaker named Nagireddy Sriramiyachandra attaches a smartphone to her head every morning.

在印度东南部的金奈，一位名叫纳吉雷迪·斯里拉米亚钱德拉的25岁家庭主妇每天早上都会将智能手机固定在头上。

The camera captures her hands while she slices a mango, folds laundry, and washes dishes.

当她切芒果、叠衣服和洗碗时，相机会捕捉她的双手。

She earns about 250 rupees per hour of video, which is roughly four dollars.

她每小时的视频大约赚取 250 卢比，这大约相当于四美元。

This type of work has been called "data labor" because the camera data is used for robot training.

这种类型的工作被称为「数据劳动」，因为相机数据是被用于机器人训练。

Thousands of Indian homemakers and factory workers have started filming their daily routines.

数以千计的印度家庭主妇和工厂工人已经开始拍摄他们的日常生活。

These first-person videos show how humans grasp objects, move their fingers, and complete tasks step by step.

这些第一人称视频展示了人类如何一步一步地抓取物体、移动手指并完成任务。

The recordings become a kind of "behavior textbook" that teaches robots how to perform actions like slicing fruit or folding clothes.

这些录像成了一种「行为教科书」，教导机器人如何执行像切水果或摺衣服这样的动作。

Agence France-Presse reported that this trend is growing quickly across India.

法新社报道，这一趋势正在印度各地迅速增长。

This new form of data labor is different from the work that was done for language-based AI systems.

这种新型的数据劳动与过去为基于语言的人工智能系统所做的工作不同。

#10

Large language models like ChatGPT were trained on text that had already been organized and categorized.

像 ChatGPT 这样的大型语言模型，是利用已经被整理和分类过的文本进行训练的。

#11

However, physical AI and humanoid robots need real-world movement data.

然而，实体人工智能和人形机器人需要真实世界的运动数据。

#12

Technology workers in the field of AI development say that robots must learn from actual human actions and sensory experiences.

人工智能开发领域的技术人员表示，机器人必须从实际的人类动作和感官体验中学习。

#13

Automation experts believe that this kind of data collection will become more common in the future.

自动化专家认为，这种数据收集在未来将会变得更加普遍。

#14

Humans naturally adjust their grip when a cup starts to slip, and robots need to learn these small but important reactions.

当杯子开始滑落时，人类自然会调整握力，而机器人需要学习这些微小但重要的反应。

#15

As a result, everyday activities performed by ordinary people have become valuable for AI development.

因此，由一般人所进行的日常活动，已变得对人工智能的发展极具价值。

#16

The growing demand for camera data has created new job opportunities for workers in developing countries.

对镜头数据日益增长的需求，为发展中国家的劳工创造了新的就业机会。

Workers' Daily Tasks Train Robots via Camera Data上班族的日常工作，正在训练机器人

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