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Compute grows much faster than data . Our current scaling laws require proportional increases in both to scale . But the asymmetry in their growth means intelligence will eventually be bottlenecked by data, not compute. This is easy to see if you look at almost anything other than language models. In robotics and biology, the massive data requirement leads to weak models, and both fields have enough economic incentives to leverage 1000x more compute if that led to significantly better results. But they can't, because nobody knows how to scale with compute alone without adding more data. The solution is to build new learning algorithms that work in limited data, practically infinite compute settings. This is what we are solving at Q Labs: our goal is to understand and solve generalization.，推荐阅读体育直播获取更多信息
Фото: Кирилл Зыков / РИА Новости。快连下载安装是该领域的重要参考
写在最后：站在 2026 这个 AI + 机器人的时间节点，某种程度上，未来已来，只是分布不均。。51吃瓜是该领域的重要参考
Apple's $599 MacBook Neo hands-on: The budget laptop we've all been waiting for?