Chat means chat, and GPT is the acronym for Gene Rate Pre trained Transformer. Genrative means generation, and its function is to create or produce something new; Pre trained refers to a model of artificial intelligence that is learned from a large amount of textual materials, while Transformer refers to a model of artificial intelligence. Don't worry about T, just focus on the words G and P. We mainly use its Generative function to generate various types of content; But we need to know why it can produce various types of content, and the reason lies in P. Only by learning a large amount of content can we proceed with reproduction. And this kind of learning actually has limitations, which is very natural. For example, if you have learned a lot of knowledge since childhood, can you guarantee that your answer to a question is completely correct? Almost impossible, firstly due to the limitations of knowledge, ChatGPT is no exception, as it is impossible to master all knowledge; The second is the accuracy of knowledge, how to ensure that all knowledge is accurate and error free; The third aspect is the complexity of knowledge, where the same concept is manifested differently in different contexts, making it difficult for even humans to grasp it perfectly, let alone AI. So when we use ChatGPT, we also need to monitor the accuracy of the output content of ChatGPT. It is likely not a problem, but if you want to use it on critical issues, you will need to manually review it again. And now ChatGPT has actually been upgraded twice, one is GPT4 with more accurate answering ability, and the other is the recent GPT Turbo. The current ChatGPT is a large model called multimodality, which differs from the first generation in that it can not only receive and output text, but also other types of input, such as images, documents, videos, etc. The output is also more diverse. In addition to text, it can also output images or files, and so on.

According to AndroidAuthority, the Linux kernel used by Android devices is mostly derived from Google's Android Universal Kernel (ACK) branch, which is created from the Android mainline kernel branch when new LTS versions are released upstream. For example, when kernel version 6.6 is announced as the latest LTS release, an ACK branch for Android15-6.6 appears shortly after, with the "android15" in the name referring to the Android version of the kernel (in this case, Android 15). Google maintains its own set of LTS kernel branches for three main reasons. First, Google can integrate upstream features that have not yet been released into the ACK branch by backporting or picking, so as to meet the specific needs of Android. Second, Google can include some features that are being developed upstream in the ACK branch ahead of time, making it available for Android devices as early as possible. Finally, Google can add some vendor or original equipment manufacturer (OEM) features for other Android partners to use. Once created, Google continues to update the ACK branch to include not only bug fixes for Android specific code, but also to integrate the LTS merge content of the upstream kernel branch. For example, the Linux kernel vulnerability disclosed in the July 2024 Android security bulletin will be fixed through these updates. However, it is not easy to distinguish a bug fix from other bug fixes, as a patch that fixes a bug may also accidentally plug a security vulnerability that the submitter did not know about or chose not to disclose. Google does its best to recognize this, but it inevitably misses the mark, resulting in bug fixes for the upstream Linux kernel being released months before Android devices. As a result, Google has been urging Android vendors to regularly update the LTS kernel to avoid being caught off guard by unexpectedly disclosed security vulnerabilities. Clearly, the LTS version of the Linux kernel is critical to the security of Android devices, helping Google and vendors deal with known and unknown security vulnerabilities. The longer the support period, the more timely security updates Google and vendors can provide to devices.

On the afternoon of July 8, local time, the Vietnamese Ministry of Health issued a notice stating that an 18-year-old girl in the country died of diphtheria. The Ministry of Health asked Nghe An Province and Bac Giang Province to take urgent action to control the epidemic. Diphtheria is an acute respiratory infectious disease caused by Corynebacterium diphtheriae, which is mainly transmitted through droplets and can also be indirectly transmitted by contact with objects containing Corynebacterium diphtheriae. Severe cases may show symptoms of poisoning throughout the body, complicated by myocarditis and peripheral nerve paralysis.

The latest trade monitor released recently by the World Trade Organization shows that between mid-October 2023 and mid-May 2024, WTO members continued to introduce more trade promotion measures than trade restrictive measures. The WTO said it was an important signal of members' commitment to keep trade flowing amid the current geopolitical uncertainty. According to WTO statistics, during the monitoring period, WTO members adopted 169 trade promotion measures on commodities, more than the 99 trade restrictive measures introduced. Most of the measures are aimed at imports. Commenting on the findings, WTO Director-General Ngozi Okonjo-Iweala said that despite the challenging geopolitical environment, this latest trade monitoring report highlights the resilience of world trade. Even against the backdrop of rising protectionist pressures and signs of economic fragmentation, governments around the world are taking meaningful steps to liberalize and boost trade. This demonstrates the benefits of trade on people's purchasing power, business competitiveness and price stability. The WTO monitoring also identified significant new developments in economic support measures. Subsidies as part of industrial policy are increasing rapidly, especially in areas related to climate change and national security.

An artificial intelligence (AI) team at Stanford University apologized for plagiarizing a large language model (LLM) from a Chinese AI company, which became a trending topic on the Chinese social media platforms, where it sparked concern among netizens on Tuesday. We apologize to the authors of MiniCPM [the AI model developed by a Chinese company] for any inconvenience that we caused for not doing the full diligence to verify and peer review the novelty of this work, the multimodal AI model Llama3-V's developers wrote in a post on social platform X. The apology came after the team from Stanford University announced Llama3-V on May 29, claiming it had comparable performance to GPT4-V and other models with the capability to train for less than $500. According to media reports, the announcement published by one of the team members quickly received more than 300,000 views. However, some netizens from X found and listed evidence of how the Llama3-V project code was reformatted and similar to MiniCPM-Llama3-V 2.5, an LLM developed by a Chinese technology company, ModelBest, and Tsinghua University. Two team members, Aksh Garg and Siddharth Sharma, reposted a netizen's query and apologized on Monday, while claiming that their role was to promote the model on Medium and X (formerly Twitter), and that they had been unable to contact the member who wrote the code for the project. They looked at recent papers to validate the novelty of the work but had not been informed of or were aware of any of the work by Open Lab for Big Model Base, which was founded by the Natural Language Processing Lab at Tsinghua University and ModelBest, according to their responses. They noted that they have taken all references to Llama3-V down in respect to the original work. In response, Liu Zhiyuan, chief scientist at ModelBest, spoke out on the Chinese social media platform Zhihu, saying that the Llama3-V team failed to comply with open-source protocols for respecting and honoring the achievements of previous researchers, thus seriously undermining the cornerstone of open-source sharing. According to a screenshot leaked online, Li Dahai, CEO of ModelBest, also made a post on his WeChat moment, saying that the two models were verified to have highly similarity in terms of providing answers and even the same errors, and that some relevant data had not yet been released to the public. He said the team hopes that their work will receive more attention and recognition, but not in this way. He also called for an open, cooperative and trusting community environment. Director of the Stanford Artificial Intelligence Laboratory Christopher Manning also responded to Garg's explanation on Sunday, commenting "How not to own your mistakes!" on X. As the incident became a trending topic on Sina Weibo, Chinese netizens commented that academic research should be factual, but the incident also proves that the technology development in China is progressing. Global Times