In a July 2018 mailing list post titled “Power Transfer”, Guido van Rossum wrote:
I don’t want to worry about PEP anymore, and despite the hard decisions I made, I still find that many people are still dissatisfied.
Guido van Rossum screenshot of some emails
“PEP” is a Python Enhancement Proposal. Guido will personally sign and confirm each PEP, which is one reason why he is called a benevolent dictator. In sharp contrast to this, similar improvements to PHP were all done by voting.
Guido is undoubtedly better at Python-but on the third day after the acceptance of PEP 572, feedback from flakes flew like this, making this 62-year-old famous engineer overwhelmed.
He was born in Haarlem, a small Dutch city, and graduated in mathematics and computer science from the University of Amsterdam in the Netherlands in 1982. Then he entered the Mathematics and Computer Science Center in Amsterdam and became a programmer.
At CWI, Guido had been working for 12 years, and did not leave until 1994. During Christmas in 1989, in order to pass the boring Christmas holiday, Guido determined to develop a new script interpreter as an inheritance of the ABC language.
The reason why I chose Python as the name of the program is because he really likes a British soap opera: “Monty Python Flying Circus”. He thinks the word Python is very interesting and very attractive. From the name of Python, it can be seen that Guido loves it.
Since the birth of Python in 1989, this straightforward engineer has spent 30 years of hard work to continuously improve it, and this is where Python is today.
Guido took a photo with everyone wearing a T-shirt with the words “Life is short, I use Python”
I will still be here
“As an ordinary core developer, I will still be there all the time, and I can still guide people, maybe more time.”
He added: “But I basically gave myself a permanent vacation improper BDFL (Benevolent Dictator For Life), and then you are on your own.”
No matter how the staff of the Python team changes, it is undeniable that more and more people use it.
July 2018 programming language index list CodingDojo recently ranked Python as the second most popular skill in developer recruitment ads. Stack Overflow’s 2018 developer survey ranked Python as the seventh most popular “programming, scripting, and markup language”, ahead of C #, Ruby, and PHP.
Facts have proved that Guido’s achievements are extraordinary: Python has become one of the most used languages in the world.
It is widely used in various fields such as machine learning, artificial intelligence, scientific computing, finance, games, mathematics, and physics. Various universities and major companies including Google, Facebook, Microsoft, Alibaba, etc. are also using Python.
“Life is short, Python is the shore”
Python is an ideal beginner’s language and is also used in heavyweight enterprise applications.
Its design is very simple, elegant and clear.
The most important thing is that Python is the first programming language of artificial intelligence-no one can shake its advantages in artificial intelligence.
The Python language is widely sought after by programmers, and its employment and salary have not disappointed.
In fact, as Guido once wrote in a letter to readers:
Python is a language that makes people’s lives easier. It allows you to focus on the job at hand, rather than entangled in every obscure detail of the computer system.
When it comes to the rounding system, everyone should be familiar with decimal, because most of life uses decimal, and we have learned the four arithmetic of decimal since elementary school. Since humans are so familiar with decimal, why don’t computers use the same decimal as humans?
Binary representation The bottom layer of the computer is composed of a bunch of electronic circuits. Let’s take a look at the simplest circuit example:
You may have guessed the function of the circuit above:
When the switch is closed, the light bulb is on When the switch is off, the bulb is off We can use the closed switch to represent 1, and the open switch to represent 0. This is a simple binary circuit.
Similarly, the magnetic disk is actually composed of many small magnets. The N pole of the magnet indicates 1, and the S pole indicates 0. So each small magnet has two states of 0 and 1 just like a switch.
Early components can only represent these two states (such as on and off, N and S, etc.), which is why computers use binary. Think about it, if the computer uses decimal, this electronic component must have ten states, how complicated it should be!
Binary bits and bytes However, the number that can be expressed with only one digit is limited. For example, binary is 0-1, decimal is 0-9, and hexadecimal is 0-15. Of course, a single number cannot meet our needs. If a computer can only handle the range of 0-1, humans can only handle the range of 0-9, and a disk can only store the two numbers 0 and 1, then social development may be early. Just stand still!
Smart humans choose to count with more digits, in decimal we can use eight digits to represent numbers within 100 million, the same is true for binary. We generally call the 8-bit binary a byte. One byte can represent 0-255 values (think why 255?). If you want to process more information, you need more bits. For example, a 64-bit CPU can process more and faster information than a 32-bit CPU.
The representation of “3” in binary Now that you know so much, how do you represent binary 3 in decimal?
In fact, this problem is the same as “9 + 2 in decimal”. That is through carry. Decimal is one for every ten, and binary is one for every two.
Now let’s see how to express 3: in binary:
The above picture clearly shows the calculation process in binary, is it the same logic as decimal decimal?
In a byte (8-bit binary number), the weight of each digit is different, namely 1, 2, 4, 8, 16, 32, 64, 128. Similarly, decimal is one, ten, one hundred, one thousand, ten thousand, etc.
In the past few years you have heard the words “artificial intelligence” in many different places: Alpha Go, which can play Go; Google’s voice assistant, which can call to order food; Amazon’s Alex, which can open TV, play music .
But in fact, very few people come forward to explain clearly, what is artificial intelligence?
A streamlined answer is this: Artificial intelligence is the use of machines to do things that only people could do in the past. Scientists have classified these things: reasoning, planning, learning, communication, perception, movement.
Use machines to do things that only people could do in the past. This sentence sounds simple, but if you look at history, you would not think so.
Since the birth of human civilization, we have probably experienced: the Stone Age, the Iron Age, the Machine Age, the Electrical Age … Today, the most critical milestone should be the “Machine Age”, which is what we often see in history textbooks “The Industrial Revolution”.
Why do you say that? All along, as the creators of value, we have always had two ways of creating value: one is manual labor and the other is mental labor. When the industrial revolution comes and we enter the machine age, manual labor is less important and the income of mental workers is improved. Everyone started to enter the factory and into the office.
“Machines do things that only humans could do in the past”, and humans can only play the abacus of mental work. What makes a person is what we need to spend more time thinking about.
Artificial intelligence technology In the 1940s, almost at the same time as the computer was invented, some people began to study this field. Since then, it has been obscured.
There is a law in the development of technology: the last month is still unknown, but once it breaks through a certain critical point, you can see it in life next month.
Things started to change dramatically more than ten years ago. In 2006, the emergence of deep learning algorithms, which pushed artificial intelligence to take a big step forward. Because of this big step, the rapid development of artificial intelligence like a blowout seems to have entered thousands of households overnight. All the cases we mentioned earlier all come from this.
In fact, since 2006, no underlying technology with such a huge influence has appeared. However, we have heard more and more information about “artificial intelligence” in recent years. This is because technology is applied to life.
In the future, there will be more applications. In it, it contains huge opportunities.
Let ’s take an example: we said earlier that artificial intelligence requires machines to do things that only humans used to do. There is a part in it called “perception”. In “perception”, there is a very important part is “image recognition”. In layman’s terms, it means that the computer can “perceive” the image like a human, and it can understand the picture.
In the old camera, it could not understand the picture. In its eyes: the rose is just a combination of a bunch of images and shapes. It is no different from the moon and no different from the bear.
You may be wondering, what is an algorithm? To make an analogy: We humans need “learning methods” to learn things. The same is true of computers, and we can understand “deep learning algorithms” as “learning methods” of computers. In the past, it has never had a good learning method. Now that we have it, we can learn something from it.
Let’s take a picture of 10,000 roses to the computer and tell it: Remember, this is a rose. With the help of algorithms, the computer calculates the common law of these 10,000 roses. It learns and remembers this law. When we go to show the computer the tenth and a thousandth photo. It makes a judgment: this picture basically conforms to the law I remembered before, and it has a 99% probability of being a rose.
So, it said: This is a rose.
Let the computer say: this is a rose. This may seem like a trivial thing, but it is a beginning.
We feed it more data: roses, ladders, faces, bread … let it calculate, learn, remember. Image recognition is like this: from now on, the computer can read the pictures.
Just because the computer can’t read the pictures, we still have to find ways to apply the technology to life scenes, so that it can create value.
As we said before, unmanned shopping requires image recognition technology: responsible for identifying the goods taken by the customer, identifying the customer ’s face for security, and responsible for scanning the code to complete the payment … Below, I want to give you a few A case: An image recognition technology alone, in what areas can it be used.
It can be used to fight pesticides.
With image recognition technology. You can use a drone with a camera to spray pesticides instead.
In our eyes, the farmland looks like this:
But in the eyes of drones, things look like this:
Which is the weed, which is the crop, at a glance. Pesticides are only applied where they should be. In this way, one is to improve the efficiency. It takes about two hours for two drones to spray pesticide on a farmland of about 100 acres. One is to reduce pesticide residues, which can save more than 90% of pesticides compared to the previous flooding scheme.
Another scenario is medical. As our standard of living improves and the average life span is extended, the incidence of cancer also rises. Normally, we go to check the body, to take a film, and then the doctor took it and told you: I am afraid it is not a cancer.
But now, we can let the computer do the movie. In fact, at present, artificial intelligence is already more powerful than humans in identifying early cancer.
Here we have to think about something: a doctor who wants to grow up to be able to diagnose cancer has to read many years of books and accumulate years of clinical experience. But for machines, this cost is too low.
You can see that the machine does not need to imitate the way people do things, the machine only needs to imitate, or even surpass the results of people doing things, which is enough.
You can also see that it is also an image recognition technology, which can be applied to various seemingly unrelated scenes. At this time, it can create great value. We can get an equation: artificial intelligence = algorithm + data + application.
Faced with artificial intelligence, what can we do?
According to this equation, we can play, in this wave of opportunities in artificial intelligence, what can we do?
Algorithm, this is a good idea. But the threshold is quite high. You have to learn the knowledge of mathematics and computer science, at least you have to read a research knowledge. From shallow to deep, the relevant content will probably be: Python programming, crawlers, data mining, advanced mathematics, machine learning, deep learning …
Data … Doing artificial intelligence does require a large amount of regular data. However, in the hands of ordinary people, it is difficult to have a large amount of data that can train algorithms. These data are often controlled by large Internet companies, such as: Google has search data, Facebook has social data, and Amazon has business behavior data …
Big companies hire high-paying talents for algorithms, and big companies have data. So, as you can see today, artificial intelligence technology is basically provided by these big companies. With the help of data and algorithms, Google and Facebook provide precise advertisements, earning tens of billions of dollars in profits each year; Amazon ’s cloud computing provides technical services to countless small and medium-sized companies, and supports the market value of nearly trillions of dollars.
But this does not mean that ordinary people have no chance in front of artificial intelligence. At least for applications, there is still much to be done. Moreover, the sooner you participate, the better your first-mover advantage.
You only need to know some basic programming skills, you can call the artificial intelligence technology that has been developed, and apply it to your industry. Let technology replace and help people to work. It can reduce costs and increase profits, it is profitable. It’s like applying image recognition to agriculture, medical treatment, etc.
Do technology or application? This is an issue we need to consider now. But there is a certain proposition-programming, which should be learned. If our goal is to learn to transfer existing technology and develop programming thinking. That’s actually quite simple …
And learning to program is more than artificial intelligence. You can use code to automate many of your usual tasks: image processing, document processing, market analysis, web crawlers, data analysis …
Choose Python
With so many programming languages, which shovel should I choose?
Answer, that is what you have already contacted, Python.
There are many programming languages, but if the purpose of learning programming is to participate in the wave of artificial intelligence, then the first choice is Python. Choosing Python is not only safe but also necessary.
After all, it is the leading language in the era of artificial intelligence, which is something that has been agreed in the technical world.
The Python language helps artificial intelligence develop faster, and the development of artificial intelligence drives more people to learn Python. From beginning to end, they are complementary to each other.
(The fifth annual programming language list released by the International Federation of Electronic and Electrical Engineers, Python tops the list.)
Not only that, Python has a dominant position in the most popular applications such as big data analysis, machine learning, cloud computing systems, web development (such as Facebook / Google, all developed in Python), game scripts … and so on.
The reason for this result is determined by the characteristics of the Python language:
It’s simple enough
To achieve the same function, C ++ requires 1,000 lines and Java requires 300 lines, but Python may only need 20 lines.
It is easy to learn and easy to read
Python language is highly similar to human language. Mastering the basic grammar of Python is as fast as a few days and as slow as 1-2 weeks. Almost no comments are required to allow you to read Python code. Next, you can use it to call existing technologies to implement your own projects.
It acts as a glue language, which is convenient for people from different industries to collaborate
The research and development of artificial intelligence requires the cooperation of people from different industries, not only developers, but also scientific researchers and creative talents in different fields.
Python is cross-platform and runs on systems such as windows, mac, linux, etc. It is particularly easy to learn. Everyone can package their own code very conveniently, and others can easily call it without reading it.
These characteristics make the cost of collaboration extremely low.
Learning artificial intelligence starts with Python.
