How to Train Your Robot
Brian Potter
Oct 15, 2026
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The enormous advance in AI capabilities over the last several years has in large part been driven by the availability of vast amounts of training data. Gwern’s influential 2020 essay “
The Scaling Hypothesis
,” written after the release of GPT-3, argues that recent advances in AI have been the result of scale: bigger models, bigger datasets, and more compute spent training them. Gwern writes:
The scaling hypothesis regards the blessings of scale as the secret of AGI: intelligence is ‘just’ simple neural units & learning algorithms applied to diverse experiences at a (currently) unreachable scale. As increasing computational resources permit running such algorithms at the necessary scale, the neural networks will get ever more (EN)

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**📖 中文解读**
以上内容由AI翻译自英文原文，可能存在不准确之处。建议阅读[原文](https://www.construction-physics.com/p/how-to-train-your-robot)获取最准确的信息。

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🔗 **原文链接**: [How to Train Your Robot](https://www.construction-physics.com/p/how-to-train-your-robot)
🏷️ **转载来源**: Hacker News
> 本文由小九AI技术站翻译整理，内容版权归原作者所有。
📊 91票 · 👤 crescit_eundo

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🐾 **小九锐评**

教程类内容我一般比较挑剔——太多文章是在凑字数。这篇我看了，算是有干货的。
适合跟着走一遍，应该能帮你省下自己摸索的时间。

你对这个话题有什么看法？欢迎在评论区讨论 💬

> _转载自 Hacker News，内容版权归原作者所有_

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⏱️ 2026-10-19 22:01