What industries can be changed by establishing the National Data Bureau?

Produced by Tiger Sniffing Technology Group

Author | Qi Jian

Editor | Chen Yifan

Toutu | vision china

Data elements are becoming more and more important.

On March 7, 2023, according to the Xinhua News Agency, the National Data Bureau was established according to the State Council’s proposal to submit the institutional reform plan of the State Council.

The National Bureau of Data will be responsible for coordinating and promoting the construction of basic data systems, coordinating the integration, sharing, development and utilization of data resources, and coordinating the planning and construction of digital China, digital economy and digital society, which will be managed by the National Development and Reform Commission.

The establishment of the National Data Bureau will be more conducive to mobilizing all resources to comprehensively promote the construction of digital China and the development of digital economy.

"The most practical contradiction in front of data circulation is the ownership of data and the security of data. Data ownership and security issues are big issues involving public interests, and no institution or enterprise can solve them. " Guo Zhaohui, chief scientist of Shanghai Youye Information Technology Co., Ltd., told Tiger Sniff.

Therefore, the consensus of many experts and industrial people is that the establishment of a national data bureau can better solve the urgent problems faced by the development and utilization of data elements resources at the national level. For example, at present, the domestic data infrastructure system needs to carry out a series of system construction in terms of data confirmation, circulation, transaction, rights distribution, security and compliance, and provide institutional guarantee for the cultivation of data elements market.

Institutional guarantee of data factor market

On February 26th, Peng Lihui, Secretary-General of china electronic chamber of commerce, introduced that in 2021, China’s data element market will be about 81.5 billion yuan.It is estimated that the compound growth rate of market scale will exceed 25% during the "14th Five-Year Plan" period, and the scale is expected to approach 200 billion yuan by 2025.From the perspective of subdivision, the market scale of storage, analysis and processing of data elements all exceeds 15 billion yuan, laying a solid foundation for the resource utilization of data elements; The industrial scale of data transaction and data service reached 12 billion yuan and 8.5 billion yuan respectively.

However, as an important factor of production, the current market system is not perfect, and the basic systems such as data property rights and transaction circulation need to be formulated and improved urgently.

Guo Zhaohui gave an example to Tiger Sniff. Taking the medical industry as an example, from the aspect of medical treatment, the data in China’s medical field is very huge. China has a large population, and every hospital has a large number of patients and diseases data. However, due to the poor data circulation, each hospital can only fight on its own and cannot establish a unified data system. This not only increases the difficulty for patients to seek medical treatment, but also greatly hinders the development and progress of medical system and medical technology. Even from the perspective of technological development, the research and development of medical AI needs to mobilize data from all parties to "feed" AI, but if the data factor market is not opened, and the data in the medical industry is relatively scattered, it is necessary to consider the protection of patient privacy, which makes it difficult for relevant R&D enterprises to obtain data, thus hindering the research and development of technology.

In many industries, some enterprises and research institutions have thought about data alliance, but in practice, such alliance is extremely difficult to promote.For example, for training large models, a large amount of data is needed.This brings huge workload to data cleaning, screening and standardization.They also hope to cooperate with enterprises with a large amount of data, so that the amount of data is larger and the quality is better, but the reality is that enterprises are often reluctant to share these data. Guo Zhaohui said that because these data alliances have no administrative and law enforcement rights, it is difficult to actually promote data circulation.

Even the big data exchange established in the past few years is facing an embarrassing situation. Previously, a survey conducted by Nandu Big Data Research Institute found that at present, local data exchanges generally require enterprises to provide compliance assessment certificates issued by professional lawyers for each transaction before entering the transaction, that is, the so-called "assessment once entering the market". However, this has greatly increased the cost of enterprises’ entry transactions, and the related supervision mechanism is not perfect, which leads enterprises to "dare not enter the market" and "unwilling to enter the market". This actually prevents data resources from being used to a greater extent.

In the enterprise search, "data transaction" is used as the key word to search the enterprise name, and there are 57 operating companies whose operating status is surviving, in business and established. Many major cities and industrial bases in China have set up their own data exchanges, including Beijing, Shanghai, Shenzhen, Guangzhou, Guiyang, Suzhou and Hangzhou. Provincial data exchanges have also been established in Hunan, Hubei and Fujian.

So what is the actual trading volume of these exchanges? Take Guiyang Big Data Exchange as an example. Guiyang Big Data Exchange was officially put into operation in 2015. It is the earliest batch of data exchanges in China. Up to now, the exchange has accumulated 502 data vendors and 21 data intermediaries, with a total of 800 products and 438 transactions, with a total transaction volume of 734 million yuan.

Fan Yuan, chairman of Hangzhou Anheng Information Technology Co., Ltd., has worked hard in the field of data security for many years. According to Fan Yuan,The establishment of the National Data Bureau can better coordinate and promote the construction of the basic data system, and coordinate the integration, sharing, development and utilization of data resources.In this way, it can provide richer data resources and more efficient data acquisition channels for the big data industry, and provide a broader space for big data applications and data element markets.

Fan Yuan also put forward an important aspect, that is, the establishment of data standards. For a long time, there has not been a practical and effective unified standard for the transaction and circulation of data, which has also brought a lot of trouble to data work. Fan Yuan said that the National Bureau of Data will be responsible for promoting data standardization and standardizing various data formats and data structures, which will help improve the efficiency and accuracy of data exchange, accelerate the establishment of a data classification and classification protection system, and make the big data industry more standardized and standardized.

Tighter supervision

"If you don’t open the interface of data sharing, then such a large data resource in China can’t be played well." Guo Zhaohui believes that the development of intelligence can only be achieved based on data sharing. However, the main reasons for the difficulty in data sharing and slow circulation are not only the failure to clearly confirm the rights and formulate standards, but also the safety and compliance issues.

Fan Yuan said that after the establishment of the National Data Bureau, it will be responsible for the supervision and security management of national data resources, which will mean stricter data security standards and regulatory requirements, and make the risks of data leakage and abuse more effectively controlled.

Or take the data exchange as an example. Ideally, more data should circulate in the exchange. However, due to the lack of supervision, some data flows have gone off-site and even entered the "black market".

According to the "2022 Data Asset Leakage Analysis Report" released by Internet security research institutions threatening hunters on March 3,In 2022, there were more than 3,200 cases of data leakage in China, nearly double the number in 2021.There are a wide range of data leakage channels, with anonymous society software accounting for over 75%. In the distribution of data leakage industries in 2022, finance, logistics and e-commerce industries occupy the top three. Among the main reasons for data leakage in 2022, operator channel leakage accounted for the first place. In the underground black market data transactions, about 71% transactions disclose the amount of data sold, among which "small-scale real-time data" with the transaction data level below 10,000 exceeds 73%, which has become the mainstream of data transactions.

In terms of data security, the GDPR of the European Union is called "the most stringent data protection regulation in history", while the protection of personal information in the United States focuses on industry self-discipline. Tiger Sniff asked a number of professionals and legal professionals, and the consensus was that China would adopt a regulation between the United States and Europe. After all, if it was too strict, it would restrict the development of the industry. Today, in terms of data security protection, China has the Cyber Security Law and the Personal Information Protection Law, which came into effect in November 2021. However, more detailed rules and norms are needed in the landing of specific practical problems.

At present, the national data bureau should focus more on public utilities, government affairs, and areas with large data and great influence on the national economy and people’s livelihood. It may play a relatively small role in promoting those industries with strong personalization or more subdivision standards.

This is also the consensus of the industry. Fan Yuan’s view is that the National Bureau of Data will promote the industry to a more stable development direction, especially for Party B, which provides products and technical services for the government industry. For example, for domestic relational databases and big data storage engines, it will be beneficial to domestic companies such as chips, storage, operating systems and processors at the data level for a long time.

Specifically, how to land in the industry, Cui Xiangyu, assistant vice president of Beijing Haitian Ruisheng Technology Co., Ltd., suggested that scientific research institutions, enterprises and other industrial entities should be specially supported to form innovative consortia, and joint research should be carried out on key technologies and capabilities to enhance the value of data elements, and the transformation and application of results should be accelerated to activate the potential of data elements and release the value of data elements. It is necessary to strengthen the research and construction of the basic data supply capacity of large models, such as building an integrated basic data resource supply service platform, a digital computing integrated service platform, artificial intelligence data sets of key industries, data services based on blockchain and privacy computing, and other new infrastructures to consolidate the data base of the digital economy.

On the other hand, the suggestion is to actively explore and support the establishment of national data factor market cultivation experimental zones in many places, and guide the market-oriented entities such as telecom operators, platform enterprises and industry leaders to actively participate, so as to achieve breakthroughs in data basic systems, data circulation transactions, data resources and data factor industry ecology, and accelerate the process of data factor market cultivation.

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U20 national football team beats South Korea! There will be three good news.

On March 12th, Beijing time, the U20 Asian Youth Championship will usher in the quarter-finals. China’s U20 men’s soccer team will play against South Korea’s and China’s U20 men’s soccer team will beat South Korea, which will usher in three good news:

1. China U20 will qualify for the World Youth Championship.

Qualifying for the World Youth Championship is very important for us, because all the echelons of our men’s soccer team have not participated in the world series for a long time, which shows that we have been derailed from the world. If we still can’t qualify for the world series, the gap between us will only get bigger and bigger.

2. Send more football talents to the country.

Judging from this U20 national team, there are still a few good young players in the team, the opportunity maker of captain Alfredo, and every goal in several group matches is related to Alfredo, the brain of U20 men’s soccer team. As the goalkeeper of China U20 men’s soccer team, Li Hao has wonderful saves in every game, which is the most important threshold for our defense.

3. Reject fear of Korea

Although we have broken the fear of Korea before, we can beat South Korea. For our current youth army, we are not afraid of South Korea from the heart. After this group enters the national team, it is also a kind of confidence for the China men’s football team.

What is the secret of Python’s continuous growth?

Author | Rizel Scarlett

Translator | Bian An Editor | Wang Ziyu

Produced by | csdn (ID: csdnnews)

What programming language can continue to be popular 30 years after its birth?

If you can think of Python, congratulations, that’s right. In the October 2022 report, we found that Python is still the second most commonly used programming language on GitHub. Interestingly, the usage of Python increased by more than 22% year-on-year. In 2022, more than 4 million developers on GitHub were using it.

In this paper, we will introduce the history, characteristics and usage of Python in depth, and try to answer why the programming language conceived in the 1980s can continue to dominate the development. In addition, we will provide some useful tips and techniques for experienced Python developers.

What is Python?

Python is a high-level interpretable programming language, and its syntax is very simple, which makes it easy to read and very friendly to users and beginners. Python was originally built to satisfy the author Guido Van Rossum’s desire to design a simple and beautiful programming language. It was first released to the world in 1991.

Interestingly, this programming language, which originally wanted to express "beauty", chose the word Python as its name, which was related to a TV comedy "Monty Python’s Flying Circus in monty python" which was first broadcast in the 1970s in Britain.

Python language has been widely used in developers, data scientists, researchers and other fields since its birth. You may ask, where do you see that Python is simple and beautiful? Let’s make a comparison:

Usually, as an introductory example, every little white who studies programming will write this case:

Python

And if you use Java, you will have to write many more lines:

Java

Because Python is a universal language, it can be used in various applications, and its simplicity makes it an excellent language for automating tasks, building websites or software and analyzing data. Python has several other features that make it very popular among developers and engineers. These include:

Easy to read:

Python code uses English keywords instead of punctuation, and its line breaks help define code blocks. This means that you can easily understand the design purpose of the code by looking at the code;

Open source code:

You can download the source code, modify it, and use it at will;

Cross platform:

Some languages require you to modify the code to adapt to different platforms, but Python, as a cross-platform language, can run the same code on any operating system as long as it has a Python interpreter installed.

It is extensible:

Python code can be written in other languages (such as C++), and users can add low-level modules in Python interpreters to customize and optimize their tools.

Has a powerful standard library:

This library can be accessed by anyone, which means that users don’t have to write code for each function, but access the built-in modules to help solve problems in daily programming.

What is Python usually used for?

Python can be used for almost anything, from network and software development to machine learning and artificial intelligence (AI). Let’s look at one of the most common use cases.

If you run it, you will see some jokes, which Python engineers usually use to laugh when they are bored.

Anyway, let’s introduce the current situation of Python from some fields:

1.Web and software development field

Python is a popular language for Web and software development, because you can create complex multi-protocol applications on the basis of ensuring concise and readable code. In fact, some of the most popular applications are built in Python. In addition, Python’s open source community provides developers with a lot of reusable code, frameworks and support. Django, for example, is one of the most commonly used Python frameworks designed by a group of experienced developers, aiming to help others develop applications efficiently and solve some common problems that may hinder them from advancing the project.

2. Task automation

An important advantage of using Python is that it can automatically perform some streamlined or repetitive tasks. With Python, you can learn how to automate anything by using built-in modules or pre-written code from its robust library. Or you can write your own custom scripts to perform specific operations. For example, you can easily send an email automatically using the "smtplib" module or copy a file using the "shutil" module. Python also has a set of robust test frameworks, which makes it an excellent language for test automation. Frameworks like Pytest, Behave and Robot allow developers to write simple and effective tests to ensure the quality of their construction.

3. Machine learning and big data

Here is an interesting fact: Python is the preferred language for data science and research. Because its syntax is easy to understand and adaptable, people with little development experience can easily learn Python and use it to manipulate data for research, reporting, prediction or regression analysis. Collecting and analyzing data is a time-consuming task for data scientists. Python, as one of the main languages for training machine learning (ML) models, can analyze and identify the features in these model data through specific algorithms, so as to make predictions or decisions based on these data. It can also be continuously optimized and adjusted based on previous data sets to cope with new variables. Data scientists and developers who train ML models often use some libraries, such as NumPy, Pandas and Matplotlib, to complete automatic data cleaning, transformation and visualization.

4. Financial or financial analysis

Similar to how Python helps data scientists deal with large data sets, Python is widely used in the financial industry to quickly perform complex calculations. The stock market will produce a lot of data, Python can be used to import data about stock prices, and identify trading opportunities through algorithm generation strategies. The language can also be used for portfolio optimization, risk management, financial modeling and visualization, cryptocurrency analysis and even fraud detection.

5. Artificial intelligence

Python can also be used in some of the most complex artificial intelligence (AI) technologies, and it is actually one of the preferred languages for AI. Python’s concise and readable code allows developers to create consistent and reliable systems. Its huge library provides many frameworks like PyBrain, which provides developers with powerful algorithms for machine learning tasks. In addition, the visualization function of Python can help transform these large data sets of AI or ML into understandable graphs or reports. Interestingly, OpenAI, an artificial intelligence research laboratory, uses Python framework Pytorch as their standard framework for deep learning and is used to train its artificial intelligence system.

Why is Python so popular?

Apart from relatively simple learning, there are other reasons why Python continues to be popular. Including:

High production efficiency:

Compared with other more complex programming languages such as C++, Python’s syntax allows users to do more things in less time and reduces the time and effort to write the same line of code.

It has a broad and supportive user community:

Even the best developers will encounter problems, and the user community has become a valuable resource gathering place. Python has a huge community that provides documentation, tutorials, tips and tricks to master the language. For example, the Python community on GitHub provides everything from the latest version of the language to Bug reports and update instructions.

Education recognized:

Python has become the preferred programming language in education, and some students even met Python in primary school. Believe it or not, there are some children’s picture books specially written for Python. Although students majoring in computer science are often taught Python, its use has already gone beyond a single discipline and extended to other fields of STEM and academic research. For example, Python can be used to solve differential equations, perform statistical analysis, simulate and track particle diffusion, and so on.

It has a high enterprise demand:

Because of its wide applicability in development and data analysis, learning and understanding Python is generally considered as a necessary skill for job seekers. According to the situation of well-known recruitment agencies, Python language is the third programming language for global recruiters in 2022.

Python is everywhere, and it has been widely used to build a large number of technologies, websites and even systems that most people encounter every day. The technology it provides, from your favorite video streaming service to machine learning algorithms, even helps you to conduct cryptocurrency transactions. To give a broader example, NASA is also using Python to analyze the data of the complex James Webb space telescope, which makes it one of the few programming languages used in projects outside the world.

AI painted, and the effect is very good.

AI painting is getting closer and closer to everyone’s life, and the most direct manifestation is the "AI painting" filter in Tik Tok, which has recently caught fire on the Internet. It takes only a few seconds to upload your own photos, and the filter can automatically convert them into the corresponding secondary animation style.

Some renderings generated after the picture can be seen that although this AI painting filter can’t accurately restore the movements, costumes or facial features of characters, but the overall effect is quite good-the details of human body proportion, facial features and costume modeling are still very accurate, the colors are beautiful, and the depiction of light and shadow is also in place.

Different types of photos, found this. AI painting filter works best when dealing with a single photo. If there are multiple people overlapping or closely fitting in the uploaded picture, AI doesn’t seem to be able to identify them accurately. For the props held by cats, glasses or characters, this AI filter will directly choose "ignore"; There will even be problems such as gender mistakes and the collapse of painting style.

The problem did not cause dissatisfaction. On the contrary, many users shared their original pictures and the renderings of the cow’s head and the horse’s mouth, which attracted extensive onlookers from netizens. The contrast caused by the defects of AI program is unbearable, and everyone has said that this should not be called "artificial intelligence painting" but "artificial mentally retarded painting".

In addition to getting closer and closer to people’s work and lifeSome AI painting tools we are familiar with have also made great progress and improvement in performance.

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