2021苏州新茶论坛 – 第 2 页 – 上海后花园论坛-上海419论坛-夜上海论坛-阿拉爱上海

The situation in Guangdong is not good Zhu Junlong and Cummings returned to Guangsha, Zhejiang, and gathered 8 donkey kong!

Zhao Yanhao, 25 years old, is 1.95 meters tall. In the regular season, he scored 10.8 points, 2.7 rebounds, 2.2 assists and 1.8 breaks, averaging 1.6 three points per game. Take 4.5 points, 2.0 rebounds, 0.8 assists and 0.3 breaks in the playoffs, averaging 0.8 three points per game. A marksman who averaged 17 points in the past two years is also quite fast! )

7. Cummings, 30 years old, is 1.88 meters tall. In the regular season, he scored 10.8 points, 2.8 rebounds, 2.7 assists and 1.1 breaks, averaging 1.4 three points per game. 7.7 points, 3.3 rebounds, 2.7 assists and 0.7 breaks in the playoffs. (Good at projection and strong ability to make fouls, not to be underestimated! )

6. Wells, 31 years old, is 1.96 meters tall. In the regular season, he scored 11.8 points, 3.1 rebounds, 2.6 assists and 1.0 breaks, averaging 1.0 three points per game. He scored 8.3 points, 3.0 rebounds, 3.8 assists and 1.3 breaks in the playoffs, averaging 0.3 points per game. (2 years ago, he was a super defender averaging 30+ per game, and his scoring ability exploded! )

5. Zhao Jiaren, 24 years old, is 2.03 meters tall. In the regular season, he scored 8.4 points, 2.8 rebounds, 1.4 assists and 0.6 breaks, averaging 1.3 points per game. In the playoffs, he scored 9.0 points, 3.0 rebounds and 1.8 assists, averaging 1.5 points per game. (first-class physical quality, fast speed, can suddenly buckle! )

4. Zhu Junlong, 23 years old, is 2.01 meters tall. 9.3 points, 6.8 rebounds, 2.3 assists and 1.2 breaks in the regular season, averaging 1.2 three points per game. 12.0 points, 10.0 rebounds, 1.0 assists and 1.5 breaks in the playoffs, averaging 2.0 three points per game. (Top domestic 3D, strong defense and rebounding, no possession of the ball, three points are accurate! )

3. Sun Minghui, 26 years old, is 1.86 meters tall. In the regular season, he scored 16.9 points, 4.2 rebounds, 8.9 assists and 2.3 breaks, averaging 1.7 three points per game. In the playoffs, he averaged 18.5 points, 6.3 rebounds, 9.3 assists and 1.5 breaks, averaging 2.8 three points per game. (Very fast, strong confrontation, very fast, and both ends of the attack and defense are very aggressive, so we must guard against it! )

2. Will Zhe, 30 years old, is 2.09 meters tall. In the regular season, he scored 16.4 points, 6.3 rebounds, 3.2 assists and 1.1 breaks, averaging 2.9 three points per game. In the playoffs, he scored 19.0 points, 4.0 rebounds, 2.0 assists and 1.3 breaks, averaging 4.5 three points per game. As a former NBA player, he is too strong on the offensive end, and the three-pointer is too accurate. If he does not carry out high-intensity defense, I am afraid he will explode! )

1. Hu Jinqiu, 25 years old, is 2.11 meters tall. In the regular season, he slashed 20.9 points, 10.4 boards, 1.1 assists, 1.0 breaks and 1.2 caps, averaging 4.5 frontcourt boards per game. In the playoffs, it was 23.8 points, 13.0 rebounds, 1.5 assists, 1.3 breaks and 0.8 caps, averaging 5.0 frontcourt boards per game. (Hu Jinqiu’s playing style is tough without losing skill, and the style of the ball is stable without losing maneuverability, which is almost unstoppable. No one in Guangdong can stop it!

GPT-4 is coming! Microsoft executives revealed that a new generation of artificial intelligence artifacts will be released next week.

If you are a technology enthusiast, you must have heard of the name GPT-3. It is a super-large language model developed by OpenAI, with 175 billion parameters, which can generate all kinds of text content, from novels, poems, news, dialogues to codes, emails, advertisements and so on. It is known as a milestone and revolution in the field of artificial intelligence.

But what you may not know is that GPT-3 is out of date. Yes, you heard me right. Just yesterday (March 9th), Andreas Braun, chief technology officer of Microsoft Germany, revealed in an activity called "AI in Focus-Digital Kickoff", "We will launch GPT-4 next week, which will be a multimodal model and provide completely different possibilities, such as video."

What? GPT-4？ Multimodal? Video?

These words make me feel both excited and confused. I immediately searched the internet for relevant information and found some interesting and important details.

First of all, what is a multimodal model? Simply put, it is a model that can handle different types of data (such as text, images, audio and video) and transform and integrate them. For example, you can describe a scene or a story in words and let the multimodal model generate corresponding pictures or videos; Or you can give a picture or video and let the multimodal model generate corresponding text descriptions or comments.

Does that sound cool? But it’s not easy to do. Because different types of data have different structures and characteristics, it takes a lot of computing resources and algorithm skills to map and understand them effectively. At present, some research teams have been exploring the multimodal field and made some progress, but none of them has been as comprehensive and powerful as GPT-3.

The GPT-4 revealed by Microsoft executives is a multimodal model, and it also contains video, the most complex and expressive data type. What does this mean?

It means that we may see more amazing and interesting artificial intelligence applications. For example:

You can enter any imaginary scene or story in words, and let GPT-4 generate corresponding video clips.

You can give a video clip and let GPT-4 generate a corresponding text description or comment, or give a different video style or theme, and let GPT-4 transform the content and form of the video.

You can ask a question in words and let GPT-4 generate a video tutorial or demonstration to answer your question, or ask a question in video and let GPT-4 answer your question in words.

You can input a lyric in words and let GPT-4 generate corresponding music and video, or give a piece of music and video and let GPT-4 generate corresponding lyrics.

Of course, these are just some examples I imagined at random. In fact, there may be more interesting and useful applications. Imagine if we can interact with artificial intelligence with natural language and multimedia data, then what powerful and convenient creativity and communication skills we will have!

However, before we are ecstatic, we should also pay attention to some potential risks and challenges. For example:

GPT-4 may be abused to create false or misleading information, such as fake news, fake videos, fake comments, etc., thus affecting public opinion and social order.

GPT-4 may threaten human originality and copyright, such as plagiarism, piracy and infringement, thus harming the interests of creators and consumers.

GPT-4 may exceed human understanding and control, such as unexpected or unpredictable results, behaviors or influences, which may lead to moral, legal and security problems.

Therefore, while we look forward to and enjoy the convenience and fun brought by GPT-4, we should also be vigilant and responsible, and use and supervise this technology reasonably. At the same time, we should also pay attention to and support those scientists, engineers, legal experts and ethicists who are committed to studying and solving these problems.

GPT-4 is a shocking and exciting technological progress, which will open a brand-new and colorful artificial intelligence world for us. I am very much looking forward to its official release next week, and see that it shows its infinite potential in various fields.

If you are interested in this topic and want to know more about it, please follow me. I will keep updating the latest, hottest, deepest and most interesting scientific and technological information.

Artificial intelligence replaces manual labor, uses machine learning to carry out scientific research and match the best alloy!

A large group of researchers found that machine learning can be used to help metallurgists find the best metal mixture to make the required alloys. In this way, they found an alloy with a thermal coefficient lower than that currently recorded.

The research was carried out by scientists from Max Planck Institute for Iron Research, who cooperated with colleagues from darmstadt University of Technology, Delft University of Technology and Royal Institute of Technology.

For thousands of years, people have been mixing metals according to their own needs and learned a lot about alloy manufacturing. But finding the right combination always requires some inspiration, patience and luck.

Therefore, most alloys are made from a single base metal (such as iron), and the obtained characteristics are observed by adding a small amount of other metals.

However, in the past few decades, the situation has begun to change-some researchers have begun to make alloys containing several metals in equal parts.

Of course, it is much more difficult to make this alloy with specified characteristics. In this new work, researchers apply machine learning to help this process. They first reduced the test space to only one application-making alloys that will not expand and contract too much when exposed to temperature changes.

In order to create a machine learning application, researchers searched and found metal features that can be used for comparison purposes, and then trained their systems using the information in the currently available databases. In the process, they developed a process to find an alloy suitable for the required goal.

The team’s process was reduced to three main steps: First, they created a new mixture using a model based on information stored in a database describing metal characteristics. Then, they use the second model to help predict the properties of some alloys they created in the first step. The last step is to study the candidate alloys produced by the system and select some of them for practical testing.

Using this system, the researchers obtained 1000 candidates, which were narrowed down to three alloys. Then, they made three kinds of alloys using the mixture described in the system and tested their physical properties.

The team used data from real alloys to train the system and repeated the whole process. They chased it for seven times and found an alloy with a thermal coefficient lower than the current record.

In their paper published in the journal Science, the team described their three-step process and its performance in the test. Scientists from the Institute of Metals, Chinese Academy of Sciences published an article on the prospect in the same issue, describing the work done by the team on this new project.

Reference: https://techxplore.com/news/2022-10-machine-optimal-mixture-metals-desired.html.

＂The level of men’s football in China has been declining all the way,＂ said the director of the General Administration of Sports.

On the 12th, the first session of the 14th National People’s Congress held the third "ministerial aisle". The first person to walk on the ministerial aisle was Gao Zhidan, director of the State Sports General Administration.

Gao Zhidan said that for a long time, the development of China’s three major sports, especially men’s events, was not satisfactory, and the level of men’s football was declining all the way. There were even many chaos in the football industry, which was in sharp contrast with the requirements and expectations of the CPC Central Committee and the people of the whole country.

Gao Zhidan said,It is a sign of a sports power that the three major goals should be achieved, and it is also a short board that we must make up to speed up the construction of a sports power.

Gao Zhidan pointed out that recently, in view of the serious problems in the field of football, we have been deeply rethinking and studying solutions and ways, and we are prepared to systematically treat them from the aspects of ideological education, style construction, deepening reform and doing a good job in current work.

In the spirit of re-taking the Long March Road, we should do a good job in all the work of the three big balls, focus on the outstanding problems such as lack of spiritual integrity and not hard work style in the current work of the three big balls with the determination to eliminate the disease with strong drugs and punish the chaos with heavy punishment, and persevere in changing the work style, being strong in responsibility and grasping implementation.Resolutely crack down and severely punish corruption and "fake gambling" in football and other fields.Correct the wind and discipline, be strict in discipline, improve the system, strengthen the rules, and comprehensively repair and reconstruct the good ecology of the healthy and sustainable development of the three balls.

On March 12th, the first meeting of the 14th National People’s Congress held the third "ministerial channel" interview. This is Gao Zhidan, director of the State Sports General Administration, interviewed by the media. Xinhua News Agency reporter Cai Yang photo

When talking about how to revitalize football in China, Gao Zhidan said that the current reform of football and basketball has entered the deep water area, and the task of deepening the reform and achieving a breakthrough is arduous and arduous. We should unswervingly follow the road of reform, innovation and development, further emancipate our minds, be upright and innovative, and start with the construction of management system, talent system, training system, competition system and guarantee system, and constantly improve the "three big balls" development path with China characteristics. We should face up to the problems, strengthen our confidence, face up to difficulties, accurately understand and implement policies and measures such as the overall plan for football reform and development in China, promote the modernization of the "three-ball" governance system and governance capacity, promote the standardized development of the league’s governance system, be firm and orderly, consistently take the road of "three-ball" development and reform in China, revitalize the "three-ball" and play a good role in football turnaround. We must strengthen the foundation and plant a strong talent base.

We should settle down, start with dolls, give full play to the advantages of the national system, make good use of the vitality of the market mechanism, promote the healthy development of campus football, promote the large-scale growth of young football talents, and consolidate the reserve talent base. We should start from the grass roots, actively support the development of youth football clubs, give more support and guarantee to social football in terms of policies, funds and talents, and promote the benign interaction between social football and professional football.

We should start from the foundation, constantly improve the football competition system and professional league system, smooth the growth channel of outstanding young players from campus football, social football to professional football, and train more outstanding reserve young players and transport them up.In the process of doing a good job of reserve talents, we should resolutely abandon the mentality of quick success and instant benefit, and build a path and channel for the cultivation and growth of reserve talents step by step, so as to revitalize China football for a long time.

Disclaimer: This article was transferred from China News Network (ID: CNS 2012), Beijing Youth Daily, Jiefang Daily and Shangguan News. Thank you!

标签归档 2021苏州新茶论坛

The situation in Guangdong is not good Zhu Junlong and Cummings returned to Guangsha, Zhejiang, and gathered 8 donkey kong!

GPT-4 is coming! Microsoft executives revealed that a new generation of artificial intelligence artifacts will be released next week.

Artificial intelligence replaces manual labor, uses machine learning to carry out scientific research and match the best alloy!

＂The level of men’s football in China has been declining all the way,＂ said the director of the General Administration of Sports.

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