Why did Kimi get scolded for her honor, but the Silicon Valley carnival is touted as culture?

Author: Dongcha BeatingOriginal title: Why can't KIMI go to a nightclub to celebrate? This past weekend, Kimi hosted a celebration party at a nightclub in Beijing. There are three lines on the big screen at the nightclub: K3, scale upK4, scale the Fuck Up to the Moon. Some people posted Xiaohongshu that night, saying that Kim's programmers were all very strict. They sat down in the card slot and played with their phones. They rounded up and added WeChat themselves and were rejected. Co-Founder Zhang Yutong is also said to have been present. The next day, reviews on the community and social media were that people who are impetuous, addicted to gold, and big models shouldn't be like this. Some people say they haven't reached the best technology in the world, so drink alcohol first. Kimi's celebration party at the nightclub should not have attracted so much criticism. Kimi K3 has already caused an international uproar soon after its release. It can even be said that it is one of the most critical sources of recent controversy in the US tech industry and politics about the open source model. Of course, the team has reason to be happy. However, when an ordinary celebration arrived in Beijing, it became a trial about original intentions. Strangely enough, if the same scene happened in a different place, the perception would be completely different. Silicon Valley Cult Imagine if a Silicon Valley company celebrated at a Silicon Valley nightclub, wouldn't that be a lot of fun? A group of engineers who have just finished training models are wearing T-shirts and drinking too much at the bar. The founder is standing in the crowd and puts a swear slogan on the screen. The next day, the tech media wrote, and the title would probably include words such as ambition, crazy, and young. Readers will say this is Silicon Valley after reading it. When we talk about Silicon Valley, we're always liberal. Silicon Valley's most valuable products aren't just chips, models, and software. It also produced a complete set of languages to explain success. Jobs wears a black turtleneck all year round, which is called focus. Zuckerberg wears slippers to meet investors; that means he doesn't take stereotypes seriously. Sleeping in the office is devoting oneself to a career, and making money for Burning Man is unleashing creativity. Silicon Valley sells more than chips, models, and software to the world; it also has a whole set of rhetoric to explain why a person succeeds and what he does after success is considered decent. People in Silicon Valley can be proud, they can be offended, and no one takes it seriously when they say a few swear words after drinking too much. They don't have to prove they're good students all the time, because in this line of rhetoric, they default to the future. But Chinese companies have never had this kind of treatment. It must first prove that technology is real, growth is real, and valuations are not blowing. I've proven all of this, and I need to prove again that I'm not proud. There was a lot of English at the press conference; some people think it advocated foreign beauty. The founder will express that some say he only does marketing. The company made money, employees went out to celebrate, and others worried that it had lost its original intention. To put it bluntly, there's a kind of Silicon Valley cult here. For a long time, Silicon Valley was not only the birthplace of advanced technology, but also the birthplace of modern life. Garage entrepreneurship, hacker culture, venture capital, equity incentives, flat management, and even the attitude of rebellion were learned from there. This is where the problem lies. We can accept that Chinese companies learn how Silicon Valley suffers, but we can't accept that Chinese people also learn to enjoy themselves like successful people in Silicon Valley. This is actually a hidden form of cultural inferiority. The worship of Silicon Valley here is not simply about thinking that Silicon Valley is superior. Instead, we subconsciously think that successful people in the West have the right to define their lifestyle, while successful Chinese people only have the obligation to be judged by their lifestyle. The Information recently published an article “Private Jets Are Scarce. “Blame the Gusher of AI Wealth” describes this reality: Today, about 30% of consumers of high-end private jets in the US come from Silicon Valley. Among them are not only the founders of technology companies. Thanks to AI's crazy performance in the capital market, researchers and engineers at many AI companies are now also starting to consume private jets. America's top AI engineers have begun to study buying a Gulfstream G650, and we're still discussing whether Chinese AI engineers are too impetuous to go to work and drink. We're not promoting a luxurious lifestyle. To be sure, if you were one of these top talents, which public opinion environment would you rather work in? Moralism of suffering We are extremely tolerant of America's tech elites, yet we are extremely strict with our own people, not only because we worship Silicon Valley, but also because our education from an early age tells us that decent success should come from a very difficult time. We have long learned to judge a person's character by whether they can bear hardships. Children don't have good grades, and parents are often the angriest not the score...

25d agoburnking#Rhythm BlockBeats

How did the winners of the Fields Medal send the AI Summit?

Author: Qubit Original title: Fields Medal winner Wang Hong also sent NeurIPS, huh? Emerging Fields Medal winner Wang Hong has also crossed borders to work in AI? NeurIPS 2026 is about to be released. Some netizens pointed out that Professor Wang Hong posted an article on NeurIPS 2019. Also, it's not a public name; it's actually a joint work. So the question is, why would a top mathematician in the field of pure numbers publish a paper at the AI Summit? After reading through, we came to the conclusion that this is the best example of mathematical theory+machine learning. What's interesting, though, is that of the nearly 40 papers and preprints listed on Wang Hong's personal website, almost every article has a full link. This one is an exception. What Wang Hong studied in the paper Cross-Border Challenge AI is a basic task in machine learning and data analysis: low-rank matrix approximation. Simply put, actual data can usually be organized into a matrix, but these matrices are often very large, and direct storage and processing costs are extremely high. Low-rank approximation is to use a matrix with a simpler structure and lower rank to restore the original matrix as accurately as possible. Recently, a commonly used approximation algorithm is Column Subset Selection (CSS). The idea is actually pretty intuitive. Facing a data matrix with a large number of columns, instead of directly finding a new low-rank matrix, select a number of representative columns from the original matrix, and then use the space formed by them to approximate the entire matrix. Since the columns selected by CSS come directly from the original data, they are easier to explain than abstract vectors obtained by ordinary matrix factorization. At the same time, it can also reduce storage and calculation costs, and is suitable for processing large-scale data. Previous research has proven that for general low-rank approximations, the upper bound of the approximation ratio of the CSS algorithm is approximately O (k+1). Here k refers to the rank of the target matrix. The larger k, the greater the worst error allowed in theory. The work of Wang Hong and others is to further advance this field: · When 1≤p≤2, the approximate ratio is (k+1) ^ (1/p); · When p≥2, the approximate ratio is (k+1) ^ (1−1/p). Compared to the previous unified O (k+1) results, this boundary is clearly tighter. The algorithm can be strictly limited, and the worst results are only slightly worse than the optimal solution. Furthermore, for the case of p≥2, the paper also constructed a corresponding lower bound to prove that the results were accurate to the constant 1. In other words, the paper gave a nearly capped theoretical answer. The most critical part of this paper, and one that best reflects Wang Hong's mathematical background, is that they used the Riesz—Thorin interpolation theorem, a classic tool from harmonic analysis. Normally, if you want to prove that an algorithm works at all p-values, you need to perform complex analyses for different p values separately. Some endpoint situations, such as p=1, p=2, and p=∞, are relatively easy to handle. The Riesz—Thorin interpolation theorem can then “interpolate” the conclusion to all p-values in the middle after grasping the results at these end points. Specifically, the paper first proved the three special cases of p=1, 2, and ∞, and then introduced an approximation boundary for the entire range through interpolation theory. In fact, this set of tools is a classic method in harmonic analysis and operator theory, but it was not the most commonly used technology by theoretical computer science researchers at the time. NeurIPS's reviewers back then also took note of this. The reviewers finally acknowledged the paper's main technological innovation, which was the introduction of the Riesz—Thorin theorem into the computer field, and the final Meta Review evaluated it as a paper with very solid arguments. Looking at it today, this paper actually also provides a very typical interdisciplinary case. The problem of machine learning may be able to find a breakthrough in pure mathematics. NeurIPS 2026's score is now back in time, and NeurIPS's review mechanism is undergoing a significant adjustment. NeurIPS 2026 requires authors to select the one that best matches the paper's positioning from five contribution types: General, Theory, Use-Inspired, Concept & Feasibility, and Negative Results. There is no doubt that Wang Hong's 2019 paper falls into the Theory category. According to the latest review guidelines for NeurIPS 2026, theoretical papers first examine mathematical rigor...

29d agoburnking#AI #Wang Hong