On the social platform Twitter, we are often stunned by the unpredictability of the "wind of public opinion".
It used to be the darling of the technology industry, the birthplace of several political, economic, social and entertainment events, and the "future of social networking" in the eyes of analysts and Wall Street;
After the high-profile listing, the stock price continued to dive, and it was labeled as "Twitter is dead" and "the next Yahoo", making phone number list it impossible to sell even if you wanted to "sell yourself";
After the first profit in Q4 in 2017, Twitter’s revenue began to resume growth and continues to this day, so there have been countless successful studies on Twitter’s second rise: focusing on “news” positioning, video, managing spam, and organizational adjustment ...
In short, these are hidden dangers when performance declines, and these are advantages when performance rises, which makes passers-by extremely confused. This is probably the legendary "everything has two sides" (we don't dare to ask. jpg anyway).
Of course, in Twitter's "PUBG", no one would deny the need to introduce AI technology. And Twitter has indeed done a lot of work on AI.
Nonetheless, we also know that it is meaningless to reverse the process from the results, after the phenomenon occurs. Therefore, as a media that insists on advocating for AI, we are also reluctant to simply regard AI as a life-saving straw for Twitter to "return from the dead". On the contrary, what Twitter has planted and harvested on AI is far from enough in our opinion. So, looking back at Twitter’s AI evolutionary history, what lessons and enlightenments did it bring?