Combined with case studies, what benefits does using big data bring to Prada?

1. Preliminary understanding of big data

Seemingly overnight, big data (Big Data) has become the most fashionable word in the IT industry.

First of all, big data is not a completely new thing. Google’s search service is a typical big data application. According to customer needs, Google collects massive digital assets (or digital garbage) from around the world in real time. Quickly find the most likely answer and present it to you, which is the most typical big data service. It’s just that in the past, there were too few data processing and commercially valuable applications on this scale, and there was no established concept in the IT industry. Nowadays, with global digitization, network broadband, and the application of the Internet in all walks of life, the amount of accumulated data is increasing. More and more companies, industries, and countries are discovering that similar technologies can be used to better serve customers and discover new technologies. Business opportunities, expansion of new markets and improvement of efficiency have gradually formed the concept of big data.

The Internet is a magical network, and big data development and software customization are also models. The most detailed quotation is provided here. If you really want to do it, you can come here. The starting number for this mobile phone is one. The one in the middle of eight and seven is San Er Zero and the last one is One Four Two Five Zero. You can find it by combining them in order. What I want to say is, unless you want to do it or understand this aspect, if you are just joining in the fun, don’t come. .

There is an interesting story about luxury marketing. Every piece of clothing in PRADA's New York flagship store has an RFID code. Whenever a customer picks up a piece of PRADA and enters the fitting room, the RFID will be automatically recognized. At the same time, the data will be transmitted to PRADA headquarters. The data of each piece of clothing is stored and analyzed in which city, which flagship store, at what time, when it was taken into the fitting room and how long it stayed. If the sales volume of a piece of clothing is very low, the previous approach was to get rid of it directly. But if the data returned by RFID shows that although the sales volume of this piece of clothing is low, it has entered the fitting room many times. That can explain some other issues. Maybe the outcome of this piece of clothing will be completely different, maybe a small change in a certain detail will recreate a very popular product.

Individual data has no value, but as more and more data accumulate, quantitative changes will lead to qualitative changes, just like one person’s opinion is not important, but the opinions of 1,000 or 10,000 people are. More importantly, one million people are enough to create huge waves, and hundreds of millions of people are enough to change everything.

No matter how much data there is, it is of no value if it is blocked or not used. Flights in China are very late, but American flights are much more on time. Among them, the good practice of the US aviation regulatory agency has played a positive role. It is very simple to say that the US will publish the delay rate and average delay time of each airline and each flight in the past year, so that customers can When purchasing air tickets, it is natural to choose flights with a high punctuality rate, thus leading airlines to strive to improve punctuality rates through market means. This simple method is more direct and effective than any management means (such as the Chinese government's macro-control means). Let me say one or two more words here. In the past, a tyrannical country mainly relied on physical violence to control its internal affairs, that is, strong institutions with unlimited power and engaged in state terrorism; but now a tyrannical country mainly relies on monopolizing information and blocking information. Make it difficult for the public to obtain extensive and true information, thereby achieving state control. This information blockade is the blockade of big data.

Without data integration and mining, the value cannot be presented. Cooper in "Never Ending" has no value if he can't integrate and connect the vast amounts of information around a company's stock price.

Therefore, the generation, acquisition, mining and integration of massive data show huge commercial value. This is what I understand as big data. Today, when the Internet has restructured everything, these problems are not a problem. Because, I think big data is the next wave of applications for the in-depth development of the Internet and a natural extension of the development of the Internet. At present, it can be said that the development of big data has reached a critical point, so it has become one of the hottest words in the IT industry.

2. Big data will reconstruct the business thinking and business models of many industries

I would like to start this topic with a wild imagination of the future automobile industry.

A car is a huge investment in a person's life.

Calculated based on a car worth 300,000 yuan and a seven-year car replacement cycle, the annual depreciation cost is more than 40,000 yuan (not counting the capital cost here), plus parking, insurance, oil, repairs, maintenance and other expenses, the annual consumption should be in Around 60,000. The automobile industry is also a leading industry with a long industrial chain, and only real estate can rival it in this aspect.

But at the same time, the automobile industry chain is an inefficient and slow-changing industry. A car has always had four wheels, a steering wheel, and two rows of sofas (Li Shufu said). For such an expensive thing, the data generated around the car is pitiful, and there is almost no data transfer between industry chains.

We are wildly imagining here. If the car is fully digitized and big data is included, what will be the result?

Some people say that the digitization of the car is not just about adding data. An MBB module? No, this is too childish. In my ideal, digitization means that the car can be connected to the Internet at any time. It means that the car is a large computing system plus traditional wheels, steering wheels and sofas. It means that digital navigation and autonomous driving can be achieved. It means that every aspect of your car-related Every action is digitized, including every maintenance, every driving route, every accident video, the status of key car components every day, and even every one of your driving habits (such as every braking and acceleration) is recorded. In this way, your car may generate T bits of data every month or even every week.

Okay, let’s assume that these data can be stored and shared with relevant governments, industries and enterprises. The impact of privacy issues is not discussed here. It is assumed that data can be shared freely under the premise of privacy protection.

So, what will the insurance company do? The insurance company took all your data and modeled it and analyzed it, and discovered several important facts: First, you mainly drive to and from get off work, and the road from Nanshan to Bantian The route is a non-busy route with few traffic lights. The accident rate of this route in the past year is very low; the condition of your car (the age and model of the car) is good, and this model has a low accident rate in Shenzhen; even statistics on your car Driving habits, even refueling, less temporary braking, less overtaking, keeping a proper distance from surrounding cars, good driving habits. The final conclusion is that your car model is good, your car is in good condition, your driving habits are good, your frequent routes have a low accident rate, and you have not had any car accidents in the past year, so you can be given a greater discount. In this way, the insurance company completely reconstructs its business model. Before there was no big data support, insurance companies only made simple classifications of auto insurance customers. They were divided into four types of customers. The first type was those who had not had an accident in two consecutive years, and the second type had not been in an accident in the past year. , the third category is those who had one car accident in the past year, and the fourth category are those who had two or more car accidents in the past year, there are four types. This simple and crude classification is like a woman looking for a husband. She only divides men into four types: those who have never been married, those who have been married once, those who have been married twice, and those who have been married three times or more. Just like getting married. With the support of big data, insurance companies can be truly customer-centric, classifying customers into thousands of types, and each customer has a personalized solution. In this way, insurance companies operate completely differently, and for low-risk customers It would be completely difficult for ordinary insurance companies to compete with such insurance companies if they dare to make bold discounts and offer high prices or even refuse to offer high-risk customers. Insurance companies that own and use big data will have an overwhelming competitive advantage over traditional companies. Big data will become the core competitiveness of insurance companies, because insurance is a business based on probability assessment, and big data is useless for accurately assessing probability. It is undoubtedly the most advantageous weapon, and it is simply a tailor-made weapon.

With the support of big data, the services of 4S stores are completely different. Vehicle condition information will be regularly transmitted to the 4S store. The 4S store will promptly remind the car owner of timely maintenance and repairs according to the situation. Especially for problems that may endanger safety, remote intervention measures will even be taken with the customer's consent. At the same time, the car owner can also prepare goods in advance. You can go to a 4S store for repairs without waiting.

For drivers, when they don’t want to drive, with the support of big data and artificial intelligence, the vehicle can drive automatically, and can self-learn and optimize the routes you often drive.

Google's self-driving car, in order to predict the surrounding environment, collects almost 1GB of data every second. Without the support of big data, self-driving is unimaginable; when it gets too close to surrounding vehicles, it will promptly remind the car owner to avoid it. ; When commuting to and from get off work, it will remind you of the routes you frequently drive based on real-time big data to avoid congestion points and help you choose the most appropriate route; in the event of an emergency, such as a flat tire, the autonomous driving system will Automatically take over and improve safety (it is rare for a person to encounter a flat tire in a lifetime, and people's reactions in emergencies are often catastrophic, which will only make things worse); finding a parking space in the city center is a very troublesome thing, but In the future, when you arrive at the entrance of the shopping mall, you can let the car find a parking space by itself. When you want to return, you can notify in advance and let the car come and pick you up.

Vehicles are the largest and most active moving objects in cities, a source of congestion, and one of the largest sources of pollution. Digital vehicles and big data applications will bring many changes. Traffic lights can be automatically optimized and adjusted according to the congestion conditions of different roads, and traffic lights can even be canceled in many places; urban parking lots can also be greatly optimized, and the design of urban parking spaces can be optimized based on big data. If it cooperates with the automatic driving of vehicles, Function, the parking lot can evolve revolutionaryly, and a parking building specifically for self-driving vehicles can be designed. The underground and above-ground floors can be as high as dozens of floors, and the parking floor can be shorter, as long as it can be higher than the height of the car (or the car can be erected (stop), this will have a huge impact on urban planning; in the event of an emergency, such as a landslide ahead, surrounding vehicles can be notified immediately (especially vehicles heading for the landslide road); the current fuel tax can also be revolutionized Sexual changes can be made. Charges can be truly based on the distance traveled by the vehicle, or even based on the amount of emissions the vehicle emits. Cars with low emissions can even engage in carbon trading, selling their emissions to vehicles with high fuel consumption; the government can also announce the prices of various types of models every year. Actual emissions, taxes, safety and other indicators are used to encourage people to buy more energy-efficient and safer cars.

The e-commerce and express delivery industries are also likely to undergo dramatic changes. The express delivery vehicles can drive themselves, so you don’t have to drive on congested roads during the day, or drive in the middle of the night. An automatic receiving box is designed at your door, and it can be opened with a password to automatically deliver it, just like the newspaper boy in the past.

Imagining it this way, I believe that automobile digitization, Internetization, big data applications, and artificial intelligence will bring about unimaginable huge changes and industrial revolutions in the automobile industry and related long industrial chains. The infinite space of imagination can be completely reconstructed. Of course, it will take at least 50 or 100 years to realize the scene I described, and I probably won’t be able to see it in my lifetime.

The following imagination revolves around the person himself. Human digital survival has only happened in the past few decades. My grandparents had photos at the end of their lives. It was a preliminary digitalization of their personal images, so that we and future generations can still know the glorious images of our grandparents. We have had photos since childhood, and we have become more and more digitized in recent years. Our identities are digital (i.e. ID cards), bank deposits are digital, photos are all digital, physical examination forms are also digital, and shopping is digital (on Taobao) There are dozens of my addresses, hundreds of shopping information, tens of thousands of search information), digital communication (there is a new circle of friends ecology on WeChat), and a state of digital existence has been initially established. And our next generation or the generation after that will enter a completely digital existence. People will have a genetic map from birth, to every subsequent physical examination and test, to every year, every month, and every day. activities, to the trajectories of related relatives, from each person, to each generation, to the entire family tree, to the entire country, to the entire world. The generation of these massive data will change from quantitative to qualitative changes. The mining and use of these data will have a great impact on A revolutionary impact on humanity itself. Here, let us also imagine:

For example, when you are looking for a partner and you meet a beloved girl, the big data system is like a fortune-telling system. Based on the mining of massive data from both parties, it will tell you and What is the girl's matching index tells you the probability of divorce in the future for couples in similar situations around the world. If it is lower than a certain matching index, the big data system will carefully advise you to seriously consider not continuing to date this girl. Doesn’t it sound particularly like the right digitalization? Of course, you may say that such a life is so boring. Mistakes are the most beautiful part of life.

Haha, I only discuss scientific issues and ignore your hooligan-style love in the name of "romanticism" but in fact it is not for the purpose of marriage. In fact, I admit in my heart that it is good to be a gangster occasionally. Haha, just kidding.

Big data will subvert the traditional management methods of enterprises to a certain extent. The management style of modern enterprises is derived from the imitation of the military, relying on hierarchical organizations and strict processes, relying on the collection and convergence of information to make correct decisions, and then through the transmission and decomposition of decisions in the organization, and The standardization of processes ensures that decisions are implemented, that every business activity is quality assured, and that risks are avoided to a certain extent. This used to be a useful but clunky way to do it. In the era of big data, we may reconstruct the management methods of enterprises. Through the analysis and mining of big data, a large number of businesses can make decisions on their own, without relying on large organizations and complex processes. Everyone makes decisions based on big data and relies on established rules. There is no big difference in whether the decision-making is made by a high-level CEO or by front-line personnel. So, do companies still need such multi-level organizations and complex processes? What about the process?

Another major role of big data is to change business logic and provide the possibility of directly reaching answers from other perspectives. Nowadays, people's thinking or business decisions are actually dominated by a kind of logical force. We conduct research, collect data, summarize, and finally form our own inferences and decision-making opinions. This is a business logic process of observation, thinking, reasoning, and decision-making. The logical formation of people and organizations requires a lot of learning, training and practice, and the cost is very huge. But is this the only way? Big data gives us other options, which is to use the power of data to get answers directly. Just like when we study mathematics, we learned the multiplication table when we were young, geometry in middle school, and calculus in college. When we encounter a difficult problem, we use our years of learning experience to work hard to solve it. But we have another way, online Search directly to see if there is such a question. If so, just copy the answer. Many people will criticize and say that this is plagiarism and cheating. But why do we need to study? Isn’t it just to solve problems? If I can search for the answer at any time, and I can find the best answer with the least effort, wouldn’t such a search be a bright road? In other words, in order to get “what”, we don’t necessarily have to understand "Why". We are not denying the power of logic, but at least we have a new huge power to rely on, which is the power of big data in the future.

Through big data, we may have a new perspective to discover new business opportunities and reconstruct new business models. When we look at the world now, such as analyzing food spoilage at home, we mainly rely on our eyes and our experience. But if we have a microscope and we can see bad bacteria at once, then the analysis will be completely different. Big data is our microscope, which allows us to discover new business opportunities from a new perspective and possibly reconstruct business models. Our product design may be different. We don’t have to guess many things. Customers’ habits and preferences are clear at a glance. Our designs can easily hit customers’ hearts. Our marketing is also completely different. We know what customers like and hate. More targeted. Especially with a microscope coupled with a wide-angle lens, we have more new perspectives. This wide-angle lens is the cross-industry data flow, which enables us to see things that we could not see in the past. For example, in the car case mentioned above, driving is driving, and insurance is insurance. They are originally irrelevant, but when we combine the big data of driving If it is passed to an insurance company, the entire insurance company's business model will completely change and be completely reconstructed.

Last point, what I want to talk about is the revolutionary impact of the development of big data on the technical architecture of IT itself. The foundation of big data is IT systems. The IT system of our modern enterprise is basically based on the IOE (IBM minicomputer, Oracle database, EMC storage) + Cisco model. This model is a Scale-UP architecture, which can solve the problem of a certain amount of data under the established model. Business processes are adaptable, but if it is the era of big data, it will soon face problems of cost, technology and business model. The demand for IT from big data will soon exceed the technical peak of the existing manufacturer architecture, and the growth of super big data will It will bring about a linear relationship between IT expenditure growth, making it unbearable for enterprises.

Therefore, the current trend of de-IOE in the industry, using Scale-out architecture + open source software to replace Scale-up architecture + proprietary software, is essentially brought about by the big data business model, which means that big data will drive IT A new round of structural changes in the industry. The so-called national security factors in the trend of de-IOE are completely secondary.

So, Americans say that big data is a resource, and like big oil fields and big coal mines, it can continuously dig out great wealth. And unlike ordinary resources, it is renewable, the more it is mined, the more valuable it is. This is against the laws of nature. This is true for enterprises, industries and countries, and it is also true for people. Who doesn't like this kind of thing? Therefore, it makes perfect sense that big data is so popular.

3. The birth of new intelligent creatures

The following imagination is even more wild. It is estimated that it will take at least ten or a hundred lifetimes for us to truly realize it. At that time, we were already ancestors. Just treat it as a science fiction novel.

Start with a recent speech by a Microsoft vice president. Rick Rashid is the senior vice president of Microsoft Research. One day, he stepped onto the podium in Tianjin, China, and gave a speech in front of 2,000 researchers and students. He was very, very nervous. There's a reason for being so nervous. The problem is that he doesn't speak Chinese, and his translation skills were very poor before, which seems to be doomed to embarrassment this time.

“We hope that within a few years we will be able to break down the language barrier between people,” the senior vice president of Microsoft Research told the audience. After a tense two-second pause, the translator's voice came from the loudspeaker. "I personally believe it will make the world a better place," Rashid continued, pausing, and then another Chinese translation.

He smiled. The audience applauded his every word. Some even shed tears. This seemingly over-enthusiastic reaction is understandable: Rashid's translation was not easy. Every sentence was understood and translated flawlessly. The most impressive thing is that this translator is not human.

This is machine translation of natural language, and it is also an important manifestation of long-term artificial intelligence research. Artificial intelligence has clear and huge business prospects from the past to the future. It was a hot spot in the IT industry in the past, and its popularity is no less popular than the current "Internet" and "big data". However, humans have encountered huge obstacles in advancing artificial intelligence research in the past, and in the end they were almost desperate.

At that time, artificial intelligence was to simulate human intelligent thinking to build machine intelligence. In the case of machine translation, linguists and language experts must work tirelessly to compile large dictionaries and rules related to grammar, syntax, and semantics. Hundreds of thousands of words constitute the lexicon, and there are tens of thousands of grammatical rules, considering various scenarios. , various contexts, simulate human translation, and computer experts then build complex programs. In the end, it was discovered that human language is too complex, and the exhaustive approach simply cannot achieve the most basic translation quality. The final result of this path was that after the 1960s, the technological research and development of artificial intelligence stagnated for several years. Scientists painfully discovered that defining artificial intelligence in terms of “simulating the human brain” and “rebuilding the human brain” had reached a dead end. This caused almost all artificial intelligence projects to fall into limbo.

Here is a small episode. When I was in college, I had a teacher who was a top professor of artificial intelligence in China and the vice president of a domestic artificial intelligence research association. He commented that the artificial intelligence at that time was not artificial intelligence, but artificial stupidity. It decomposes, decomposes and decomposes simple human behaviors, and then simulates them clumsily. It is not how people learn how to be smart, but how to simulate and learn from the stupidest people. Simple action. He said that some people were complacent about the progress of artificial intelligence at that time, saying that it seemed that mankind was getting closer to the moon during the moon landing plan. In fact, it was just standing on a rock and expressing to the moon, ah, I am closer to you. His self-deprecating attitude towards his career is something that I still remember very deeply.

Later, some people thought, why should machines learn logic from humans? It is difficult and difficult to learn logic. The most powerful thing about the machine itself is its computing power and data processing ability. Why not use its strengths and avoid weaknesses and take another path? What about the road? This road is the road that IBM "Deep Blue" has traveled.

On May 11, 1997, chess master Garry Kasparov announced his defeat in a game with the computer "Deep Blue" developed by IBM. The computer "Deep Blue" thus won this far-reaching "human-computer confrontation." "Deep Blue" either relies on logic or so-called artificial intelligence to win, or it relies on super computing power: it can't think of you, but it can kill you.

Similar logic was also used in machine translation later. Google, Microsoft and IBM have all gone down this path. It mainly uses the matching method, combined with machine learning, and relies on massive data and related statistical information. Regardless of grammar and rules, it compares the original text with the translation data on the Internet to find the closest and most frequently cited translation results as output. That is to use big data and machine learning technology to achieve machine translation. The larger the amount of existing data, the better the system can run. This is why new machine translation will only be able to achieve breakthroughs after the emergence of the Internet.

Therefore, there are currently many computer scientists in the machine translation teams of these companies, but there is not even a pure linguist. As long as they are good at mathematics and statistics, and then can program, it will be fine .

In short, using this technique, computers teach themselves to build patterns from big data. With enough information, you can make machines learn to do things that appear intelligent, whether it's navigation, understanding speech, translating languages, recognizing faces, or simulating human conversations. Chris Bishop of Microsoft Research in Cambridge, UK, gave an analogy: "If you stack enough bricks and then step back a few steps, you can see a house."

Here we assume that this technology can continue to improve. In the future, artificial intelligence based on big data and machine learning will be able to simulate human conversations relatively smoothly, that is, humans can have relatively comfortable conversations with machines. In fact, IBM's "Watson" project is such a technological project, such as trying to make computers act as doctors, able to diagnose most diseases and communicate with patients. In addition, it is also assumed that wearable computing devices, which are currently just emerging, will make great progress. How far has this progress? Even your pet puppy is equipped with various sensors and wearable devices, such as image collection, sound collection, smell collection, and monitoring of the puppy's health. Small medical devices and even electronic pills to monitor digestion in your puppy's stomach. Of course, puppies are also connected to the Internet, which also generates a huge amount of data. At this time, we assume that based on these big data modeling, we can simulate the puppy's joy, anger, sorrow, and joy, and then we can also express it through anthropomorphic processing. In other words, we can simulate the puppy speaking human language, such as when the owner comes home. , the puppy wags its tail and barks, then the artificial intelligence system attached to the puppy will say, "Master, I'm so glad to see you come home." Not only that, you can also have a conversation with the puppy's artificial intelligence system, because this artificial intelligence system can basically understand what you mean and can replace the puppy's anthropomorphic expressions. Let’s simulate a possible conversation below:

You: “Puppy, how are you doing today?”

Puppy: “Not bad, Master, the new dog food you changed today tastes good. Very good, I still feel like I haven’t eaten enough.”

You: “That’s good. Let’s continue buying this kind of dog food. Is anyone coming today?”

Puppy: "Only the postman came to deliver the newspaper. In addition, the neighbor's puppy Mary also came to visit, and we played together all afternoon."

You: "So how did you play?"< /p>

Puppy: "I'm very happy. I seem to have entered my first love again."

...

We can treat the above simulated conversation as a joke. But in fact, we will discover an amazing fact at this time, that is, you are actually facing two puppies, one is a physical puppy, and the other is an artificial intelligence virtual puppy based on big data and machine learning. Moreover, the virtual puppy is smarter than the physical puppy and truly considerate. So, is this virtual puppy a new intelligent creature?

We will continue to extend this story and replace the puppy with a person in the future. People generate a large amount of data in their lifetime. According to these Data modeling can directly deduce many conclusions, such as what kind of movies you like to watch, what kind of food you like, and what actions you will take when encountering any problems.

Such data is accumulated until the person dies. We have a bold imagination, can these huge data allow this person to continue to exist in some way? When future generations have questions that need to seek answers, such as when making key decisions in life, such as what major to study in college, Should I marry a certain girl? Can I ask this virtual person (ancestor) for any advice? The answer is of course. In this case, digital existence not only exists during life, but can also continue after death. When a person dies, he or she can continue to exist in the virtual space. After a lifetime or a lifetime of people pass away, these virtual wisdoms can continue to exist. Suppose that many years have passed and there are too many ancestors of these virtual wisdoms. The living descendants can even form an "ancestor joint staff committee", preferably The ancestors of those who have done well in exams (such as winning the top prize), have been senior national civil servants (such as prefects), corporate executives (such as CEOs), professors, writers, etc., are the ancestors of successful people. For future generations to consult and resolve doubts. Let there still be competition after the death of these ancestors, otherwise there will be nothing left to do after they die. Does this scene sound familiar? It’s a scene that appeared in the Disney animated film “Mulan”. When faced with a major life moment of whether to join the army on behalf of her father, Hua Mulan confided her confusion to the “Ancestral Joint Staff Committee” and got Guidance.

Imagine more boldly, assuming that material science also makes great progress, can we re-implant these virtual lives into an ecosystem that simulates humans? Of course it can. This new intelligence is very much like a real person. So does this count as resurrection after death? Can this new intelligent body continue to have its previous ID card? Can it continue to own its previous property? Can it continue to enjoy pensions? Is there also a mandatory requirement? What about the lifespan limit? Will this intelligent body learn and evolve by itself? Will they break out into a war with humans? Thinking about it more deeply, it feels like everything is in chaos. Nowadays, ethics, law, etc. are facing huge challenges. .

What do these mean? With the further progress of big data and machine learning, new intelligent creatures have appeared in the world! Big data and machine learning are changing, reconstructing and subverting many enterprises, industries and After the country, it is finally time to change humanity itself! A new branch of human evolution has appeared!

Some scientists drew the following picture to describe these two intelligent creatures. One is based on biology and evolved after millions of years of evolution; the other is based on IT technology, based on big data and machine learning, and comes through self-simulation and self-learning. The former is more logical, has rich emotions, and is creative, but has a limited life; the latter is not very logical and has no biological emotions, but has strong computing, modeling and search capabilities. In theory, Life is unlimited.

Of course, these things will be very, very far away from happening. Anyway, we won’t be able to see it when we are alive, and we won’t be able to see it when we die, because when we die, I believe that this kind of virtual life based on big data and machine learning will not exist yet.

4. Conclusion

The last thing I want to say is that our understanding of the future is mainly based on common sense and imagination of the future. According to statistics, the amount of information in the New York Times in one week is greater than the amount of information a person received in a lifetime in the 18th century. The amount of information produced in 18 months now is more than the total of the past 5,000 years. Now my home has a computer worth 5,000 yuan. The computing power is more powerful than the computing power of the entire school when I first entered college. The progress of science and technology will always exceed our imagination in many cases. Imagine if in the future the computer equipment owned by one of us exceeds the total computing power of the world today, and the amount of data generated by one person exceeds the total amount of data in the world today, even your The amount of information generated by pet puppies exceeds the current total amount of global data. What will happen to the world? That depends on your imagination.