In recent years, the topic of “AI will steal jobs” has increased from the discussion of science fiction movies that “artificial intelligence will kill humans “, and the word “AI” has gradually fallen into the context of the real world. I feel
On the other hand, many problems remain in developing human resources that are needed in the times, such as the mass production of humanities graduates who dislike mathematics.
No company has not introduced AI technology among the top 10 companies in the world’s market capitalization ranking.
Japanese financial companies have a deep-rooted image of careers for the humanities elite, but foreign financial companies around the world are actively hiring engineers.
As the times change, the human resources required also change.
In this article, I will explain why engineers should learn AI, and give three facts to those who have given up saying, “I can’t learn AI.”
Even if you are about to give up and say, “I hate mathematics, AI is impossible”, please read it.
Table of Contents
- Why Non-Engineers Should Learn AI
- AI will become a common education even for non-engineers
- For business opportunities and career advancement
- For those who have given up on learning AI
- Useful tools are increasing
- Data analysis without programming
- Mathematical formulas are not just symbols
- Summary: It’s important to move your hands
Why Non-Engineers Should Learn AI
AI will become a common education even for non-engineers
It will become commonplace for everyone to have AI knowledge.
In China, the government and companies are already working together to introduce artificial intelligence education at elementary, junior high, and high school levels.
If you go to a café at Tsinghua University, Asia’s No. 1 university, it’s not uncommon to see students talking about self-driving car technology.
In a few years, it may not be long before the day when AI is called “AI is education” even in the field of education.
The basic scholastic ability level of Japanese elementary and junior high schools is at a high level compared to the rest of the world.
Therefore, the basic academic ability of many Japanese people will be high on average.
However, if education is reformed according to the times, as in China, the gap will widen in the future.
The Japanese government is also moving to provide AI as basic education for high school and university students, but there are many issues such as the content of the program, its ability to execute, and the number of human resources who can be educated.
Please try to imagine
Suppose 10 years have passed without you knowing anything about AI as much as the information that is flowing in the news.
In the meantime, the current 1st-grade elementary school student has become a 3rd-grade high school student.
“You’re only weapon with a social experience that doesn’t know much about AI,” and “High school students who can write deep learning programs based on basic knowledge of AI.”
Which one will be the most valuable talent in 2030?
Of course, you can’t make simple comparisons, and there’s no point in making predictions, but it wouldn’t be surprising if there were a lot of companies that would rather pay high school students than you.
It would be a great shame if Japan, which is concerned about further deterioration of GDP growth due to the impact of population decline, will decline without weapons to fight.
AI learning materials and development environments are well-developed, so it may be a good idea to learn AI in general education.
As long as you have a basic knowledge of mathematics, you should be able to acquire the minimum knowledge in about two months.
The important thing is to move your hands.
For business opportunities and career advancement
Acquiring AI technology will increase the possibility of leading to business opportunities and career advancement.
In addition to that, if you acquire + α knowledge such as business, your market value will increase further.
In order to find new value, it is of course necessary to understand technology (deep learning). If managers don’t understand, they can’t start using technology. Despite this, unfortunately, there are few managers in Japan who understand both technology and business.
This is an interview with Mr. Matsuo, a professor at the University of Tokyo and a director of SoftBank.
If you can master both technology and business, you can see that there is a big chance.
Learning AI does not mean being able to program. Think of AI in the context of data science.
As you can see on the slide, having the following three strengths creates value as a data scientist.
- business power
- Data science power
- Data engineering power
If you have high business power, you can create great value by acquiring the other two insights.
Data scientists at the world’s top companies like Google and Amazon don’t have PhD-like expertise in these three areas.
It is precisely because we have specialized knowledge at some level and knowledge of other domains that we can collaborate effectively with other specialists and produce overwhelming results.
As I said earlier, “There are few managers in Japan who understand both technology and business.” can be said to be high.
For those who have given up on learning AI
I talked about data science and engineering skills that extend your business skills, rather than just being able to write programs.
However, I think many people are worried that they will be frustrated if they come up with a formula.
Of course, the market value of people who can master mathematical formulas is high, but it is possible to handle machine learning (machine learning) and deep learning (deep learning) without becoming a mathematics specialist.
- Useful tools are increasing
- You can operate machine learning without programming
- Mathematical formulas are not just symbols
Useful tools are increasing
Programming has tools such as modules and libraries that allow you to handle useful functions with simple code.
By using this tool, even a series of difficult formulas can be expressed in a single line of code.
Also, these tools are increasing as the AI market grows.
It is necessary to be able to explain why it is best to use that function (tool), but it is becoming easier day by day to write programs used in AI without implementing the code in detail. There is no mistake.
Data analysis without programming
If you are already a member of society, there are probably many people who are accustomed to working with Excel.
Not all work is done in code in the world of data science.
Tools such as Excel (or similar Tableau) are also used as a matter of course.
There is also Google’s Cloud AutoML, a service that allows you to do a lot of machine learning manually.
Just as there are services such as WordPress and Wix that allow you to create a website even if you are not an engineer, services that allow you to do machine learning even if you are not an engineer will continue to expand in the world of AI.
In both Excel analysis and machine learning program implementation, it is important to clarify the purpose, set the correct task, and derive reliable inferences.
This requires the multiplication of business and scientific capabilities, as I mentioned earlier.
Mathematical formulas are not just symbols
Mathematical formulas can be fun to learn if they are viewed as simple representations of real-world facts, rather than mere symbols.
In addition, this is not a rule made by someone, it is just a number that replaces the facts, so it cannot be changed.
As long as there is a common understanding of English rules and formulas, even if the rules change, conversations can still be held.
Mathematical formulas make it possible to see universal facts that do not change no matter who says them, in an easy-to-understand form in the form of numbers.
Do you remember learning formulas like this in high school calculus class?
Of course, since it is a formula, you can derive the desired solution by substituting the correct values.
For that reason, many people can memorize the formulas before the test and get the correct answer on the test but cannot explain them themselves.
A mathematical formula is a rule and is like a linguistic expression.
However, the difference is that English formulas can be changed, whereas mathematical formulas cannot be changed by humans.
For example, when translating “I am Taro” into English as “I am Taro”, you would apply the rule/formula of Subject + Be Verb + Noun.
Humans defined it as “Let’s make it a rule of ‘Subject + Be verb + Noun'”.
English formulas are defined by humans and can be changed.
Even if the king ordered, “Let’s omit the verb to be” and the expression “I Taro” was officially accepted, there would be no discrepancy in conversation if there was a common understanding.
On the other hand, the laws of physics in nature, such as universal mechanics, cannot be changed by humans.
Universal mechanics proves that the gravitational force works between all objects in the universe.
Just as we cannot turn rainy weather into sunny weather, the laws of physics in nature cannot be changed by the command of a king.
Mathematical formulas are really simple descriptions of our world.
You may have understood why mathematicians and physicists say that describing the world with mathematical formulas is “beautiful.”
The founder of Google, Sergey Brin, is a genius in mathematics, and Elon Musk, a genius entrepreneur, values physics because he understands the importance and enjoyment of principles and principles that cannot be changed by the king’s orders. It may be because
In other words, mathematics is not just a series of symbols, it is a beautiful description of the universality around us.
That’s why it’s important to imagine the movement when studying mathematics.
Just as once you get used to speaking English, you will be able to speak fluently without thinking about grammar.
Summary: It’s important to move your hands
There are three points I would like to make in this article:
- Think of AI as education, and if you’re interested, you should learn it.
- It is not enough just to multiply the program, the multiplication of skills is important.
- It’s easier to learn when mathematical formulas are broken down into real-world laws rather than mere symbols
By learning about AI, some people may notice the high level of human perceptual ability.
If we can notice the high level of human perception, we can improve the efficiency of work that can be replaced by AI (not only IT infrastructure), and focus on work that can only be done by humans.
Many things can be seen by actually moving your hands.
Of course, you don’t have to force yourself to study, but if you’re interested, I recommend that you learn by actually moving your hands.