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generative-ai-roadmap's Issues

core

核心其实就一句: 现在的学习其实学的就是一个约束/contraints,当前火热的LLM/AIGC是自底向上的学习,其实是个弱约束,而人们需求的是task-complete的东西,是自顶向下的,要的是很强的约束。

未来至少5年10年,大家都绕不开的就是这个, 会有各种折腾: 文字/abstract symbol不够,图像/2d vision来凑;图像不够,视频/time-aware来凑;视频不够,立体/3d~4d来凑;数据不够,交互/interactive-with: code-compiler/executor, human-interactive-align, gui/drag-gan, physical-world, ...来凑;黑盒不够,灰盒/controlnet, interpretable, ...来凑;..........................

从我一个近10年全职AI研究,6年全职AGI(Mathink)的研究者来看, 这几幅图,虚线右侧future部分,明显缺乏思考深度了。 问题可能在于: 未能对智能的整个发展史,脑科学神经认知科学历史和现状,机器学习发展的深入理解,从本质问题出发,自底向上推导,直到图片所表达的应用场景。

最近一年,LLM和AIGC大火了,其实过于乐观了。大牛中就LeCun比较客观冷静,还能指出可能相对可行的路线; 而普通从业者,我觉得SeedV的这些理解,还是相对比较能抓在点子上。

just contraint, no others.

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