One challenge is having enough training data. Another is that the training data needs to be free of contamination. For a model trained up till 1900, there needs to be no information from after 1900 that leaks into the data. Some metadata might have that kind of leakage. While it’s not possible to have zero leakage - there’s a shadow of the future on past data because what we store is a function of what we care about - it’s possible to have a very low level of leakage, sufficient for this to be interesting.
像百度、阿里本身有着硬件经验的大厂，则是针对银发人群，在智能音箱等成熟品类进一步升级AI能力，如提供AI健康管理等新服务。
，详情可参考搜狗输入法2026
不少作者认为，出版商在保护作品不被 AI 滥用这件事上没有尽力，却拿走了一半赔偿。更关键的是，和解协议并不要求 Anthropic 承认任何违法行为，法院对「AI 训练属于合理使用」的认定照样有效。
Discard new data: drop what's incoming