Recently, according to the FT, citing the latest forecast data of the market research institute SemiAnalysis, AI chip giant NVIDIA will ship more than 1 million new NVIDIA H20 acceleration chips to the Chinese market this year, and it is expected that the cost of each chip is between $12,000 and $13,000. This is expected to generate more than $12 billion in revenue for Nvidia. Affected by the United States export control policy, Nvidia's advanced AI chip exports to China have been restricted, H20 is Nvidia based on H100 specifically for the Chinese market to launch the three "castration version" GPU among the strongest performance, but its AI performance is only less than 15% of H100, some performance is even less than the domestic Ascend 910B. When Nvidia launched the new H20 in the spring of this year, there were reports that due to the large castration of H20 performance, coupled with the high price, Chinese customers' interest in buying is insufficient, and they will turn more to choose China's domestic AI chips. Then there are rumors that Nvidia has lowered the price of the H20 in order to improve its competitiveness. However, the latest news shows that due to supply issues caused by the low yield of the Ascend 910B chip, Chinese manufacturers in the absence of supply and other better options, Nvidia H20 has started to attract new purchases from Chinese tech giants such as Baidu, Alibaba, Tencent and Bytedance. Analysts at both Morgan Stanley and SemiAnalysis said the H20 chip is now being shipped in bulk and is popular with Chinese customers, despite its performance degradation compared to chips Nvidia sells in the United States.

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

Google Wallet can help you store your IDs, driving license, loyalty cards, concert tickets and more. You can also store your payment cards and use tap to pay to pay anywhere Google Pay is accepted. Google wallet is available in various countries but Google never launched it in India. Google let indian users stick with the Gpay which facilitates UPI payments. Tap to pay is not part of it. Also we can not store things such as IDs and Passes in indian version of Gpay. This might change and Google may launch Google Wallet in India. With the recent version of Google Wallet and Google Play Services, Google has added some flags and code which indicate that Google is working on something for Indian users regarding wallet. The first change I noticed recently when going through the Google Play Services apk was addition of two new flags Both flags are part of com.google.android.gms.pay package in the Google Play Services. This package contains all the flags for features of Gpay/Wallet. Google does server side flipping of flags to enable/disable features for users. So both these flags doesn't really provide any info about what features enabling these flags is going to bring. But the point here is that Google Wallet is not launched in India so why Google added these flags inside Play Services ? The answer could be that Google may be working on bringing Google Wallet to India. It can enable tap to pay, store payments and various other features for Indian users which we don't have in the current Gpay for India. I found similar flags in the analysis Google Wallet APK - These flags are also disabled by default. But this is again a clear indication of Google working towards something for Indian users. In both cases, enabling the flags doesn't bring anything noticeable UI or feature because there is nothing much added besides flags. Google has dogfood/testing versions internally, so the code will show up slowly in upcoming versions. The last piece of code I found is also from Google Play Services. In case you don't know, Google was working on Digilocker integration in the Google Files app which was supposed to bring your digital document inside the app such as driving license, COVID certificates, aadhar card. But Google has ditched the effort of bringing these features and they removed the "Important" tab (where digilocker was supposed to be integrated) from the Google Files app completely. So things are going to change and here is how. This is the code which I found in the Google Play Services - So the word "PASS" along with PAN, DRIVERS LICENCE, VACC CERTIFICATE & AADHAR CARD, is clear indication of the possibility of Google adding support for these directly through Google Wallet using Digilocker, just like Samsung Pass does it. This code is not old as I have checked older beta versions of Play Services where this code is not present. Here is a string which was added in a previous beta version a few weeks ago but I completely ignored it because it didn't make any sense without flags and the other code - This addition was surprising because there was nothing regarding digilocker before in the Play Services. In the words "pay_valuable", the "pay" to Wallet/Gpay and "valuable" refers to the things like Passes, loyalty cards and transit cards. Since we are talking about digilocker, these "valuable" are driving license, vaccination certificate, PAN card and Aadhar card which can be store in Google Wallet after digilocker integration. That's all about it. We will know more about it in upcoming app updates or maybe Google can itself annouce something about this.

South Korea's Minister of Health and Welfare Cho Kyu-hong said at a press conference on the 8th local time that after comprehensively considering the suggestions of frontline interns and the situation on the front line of medical care, the government decided that from that day on, all interns and residents who resigned would not be given administrative sanctions such as revoking their medical licenses. Cho Kyu-hong also said that for interns and residents who have returned to work and those who have resigned and are preparing to re-register for internship courses in September, the government will make special cases to try to minimize the internship gap and not affect the relevant doctors from obtaining specialist medical licenses. Cho Kyu-hong said that the government believes that in order to minimize the diagnosis and treatment gaps for critically ill and emergency patients and ensure the smooth training process of interns and residents, it is in the public interest, so it has made a decision not to punish interns and residents who resigned. It is hoped that major hospitals will complete the resignation processing of doctors who have not returned to work before July 15 and determine the scale of vacancies. Previously, large general hospitals in South Korea, such as Seoul National University Hospital, Yonsei University Severance Hospital, and Seoul Asan Medical Center, suspended or limited their medical services in an effort to cancel all penalties against interns and residents.

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.