名词之争没什么意义。
我也认为机器学习 AI 并不是“AI”，只是统计学上的局部最优解。但我也同样认为纠结“真正的学习”、“智力”等等其实没什么意义。
AI 拟人，其实是一种一厢情愿的意淫。就像人类认为的外星人都是两个眼镜一个鼻子的碳基生物一样。
机器学习 AI 的优势，就是通过强大的并行算力和数据，挖掘人脑没有发现的规律。既然是挖掘，那就一定不会与人的路径完全一样。
所以机器学习 AI ，并不是实现 AI 的唯一道路，我也认为一定不是最佳道路。
只是现在只能这么做，且基于目前的科技、数学水平，这么做最好而已。
btw：机器学习其实是上世纪 80 年代的概念。但基于当时并行算力与统计学的局限，被串行脚本策略 AI 吊打。
未来的研究发现可能会颠覆我们当前的认知甚至超出我们当前的想象能力，今天的”我认为”判断都可能太武断。另外，情景学习其实可以看作模型在隐式地进行微调，参见 Why Can GPT Learn In-Context?
Language Models Secretly Perform Gradient Descent as Meta-Optimizers
未来的研究发现可能会颠覆我们当前的认知甚至超出我们当前的想象能力，今天的”我认为”判断都可能太武断。另外，情景学习其实可以看作模型在隐式地进行微调，参见 Why Can GPT Learn In-Context?
Language Models Secretly Perform Gradient Descent as Meta-Optimizers
啊，我是就把整个帖子前 20 楼丢进去提问他整理下准确和不准确的地方
It is true that the training material for ChatGPT contains natural language, and before being processed by the model, it undergoes various preprocessing steps, such as tokenization and vectorization. However, the model still learns to process and generate human-like text based on the patterns and structure of natural language present in the training data.
I apologize for any misinterpretation of the author's point of view. It's important to note that while ChatGPT is a powerful language model, it is not an example of a general artificial intelligence. The process of fine-tuning the model for specific tasks indeed does not conflict with the author's perspective. There are various ways AI can learn and interact with the environment, and natural language is just one of those methods.
上面是他的回复
