The roadmap of generative AI: use cases and applications | 生成式AI的应用路线图
License: Creative Commons Attribution 4.0 International
The roadmap of generative AI: use cases and applications.
This work is licensed under a Creative Commons Attribution 4.0 International License.
The original diagrams and text contents are in Chinese. Here is an English Translation:
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.
Is it possible to have an English version of this repo?
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