庞若鸣本科毕业于上海交通大学,并于2006年在普林斯顿大学拿下计算机科学博士学位。他的职业生涯同样亮眼。在谷歌深耕五年后,他于2021年成为苹果基础模型团队的领军人物,负责开发Apple Intelligence背后的核心系统。当时苹果在AI领域的进展相对缓慢,甚至不得不考虑引入Anthropic或OpenAI的模型来为Siri提供支持。这种对自身局限性的默许,反衬出庞若鸣在苹果内部突围行动中的关键价值。
You can include multimodal data like images. There’s something strange about including images when going back to Roman times or 1700 because while they had texts, they didn’t have digital images. However, this is acceptable for some purposes. You’d want to avoid leaking information that could only be known in the present. You could include things people at the time could see and experience themselves. For example, there may be no anatomically accurate painting in Roman times of a bee or an egg cracking, but you can include such images because people could see such things, even if they weren’t part of their recorded media. You could also have pictures of buildings and artifacts that we still have from the past.
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The Test PLA evaluates all 148 product terms against this input and produces an 18-bit output: