Is there a future for AI engineers? Explaining 5 reasons to stop and who is suitable!

Some of you reading this article may be thinking, “I want to be an AI engineers, but I’m worried because I don’t know if I have a future.”

In recent years, AI has been used in various fields, transforming all industries and becoming closely related to our lives.

In the modern age of AI development, AI engineers are a profession that is attracting worldwide attention and is one of the most in-demand and popular professions. However, some people may be wondering how long the attention and demand for AI engineers will last.

Therefore, in this article, we will explain the current status and future potential of AI engineers, and five reasons why people say, “Don’t be an AI engineer.”

What is an AI engineer?

An AI engineer is an engineer specializing in AI who designs and builds machine learning systems.

An AI engineer’s job is to give various data to AI, process it, and educate AI, and the ability to design ” machine learning ” efficiently is required. “ Machine learning ” is the ability of a computer to reproduce what humans are learning.

The programming language ” Python ” is most used for machine learning, and many AI engineers use Python to develop and update AI.

Differences with Data Scientists

There is a ” data scientist ” as a profession that is difficult to distinguish from AI engineers .

A data scientist is a profession that solves problems based on data in various decision-making aspects. While AI engineers design and build machine learning systems, data scientists use machine learning systems to analyze data and solve client problems.

To become a data scientist, you need knowledge of statistics, programming, and machine learning. In addition to technical aspects, a wide range of knowledge such as business and market trends is required.

High Demand Industries for AI Engineers – Jobs

1. Industries with growing demand for AI – manufacturing, medical care and welfare

Currently, the demand for AI is increasing in the manufacturing, medical and welfare industries. From here, we will introduce examples of AI utilization in each industry and explain what areas AI is needed.

AI utilization examples introduced in the manufacturing industry are “demand forecasting” and “work efficiency improvement.”

“Demand forecasting” is an example of introducing AI in the manufacturing industry. AI can be used to accurately predict demand for products. We analyze a wide range of data such as sales, number of sales, and age groups to predict product demand.

An example of introducing AI in the manufacturing industry is “work efficiency improvement.” AI can now automate tasks that anyone can do, such as inventory management, sorting, packing and picking. This has made it possible to improve the efficiency of work that was previously done manually.

Examples of AI utilization introduced in medical care and welfare are “imaging diagnosis” and “nursing care robots”.

Imaging diagnosis is an example of introducing AI in medicine. RIKEN and the National Cancer Center are using AI image recognition to detect early gastric cancer. Using image recognition technology that utilizes deep learning, we have established a highly accurate detection method with a positive predictive value of 93.4% and a negative predictive value of 83.6%.

“Nursing care robot” is an example of introducing AI to welfare. Aeolus Robotics in the United States has developed an AI-equipped autonomous human support robot, the Aeolus Robot. The Aeolus robot, which has also been introduced in Japan, is used at nursing care sites to recognize residents with its object detection ability, and to monitor seizures and falls with its biosignal detection function.

2. Jobs where demand is expected – DX and product development

DX (Digital Transformation) is an abbreviation for Digital Transformation. DX is the transformation of life and business through the use of digital technology.

Promoting the use of data by AI in companies and reducing work is one of the jobs where we can expect demand for AI engineers in the future. The need to build business systems that utilize digital technology is increasing year by year, and efforts to launch DX organizations are progressing, mainly among major companies.

Also, product development work often uses AI technology such as recognition technology such as cameras and sensors, demand forecasting, and work efficiency systems, so demand for AI engineers is expected.

Current status of AI engineers

What about the current state of AI engineers? The current situation of AI engineers is as follows.

1. The size of the AI ​​market is expanding
2. Insufficient supply to meet demand for AI talent
3. AI engineers have high annual income

I will explain each.

The size of the AI ​​market is expanding

AI is now becoming commonplace as a means of doing business in an advantageous manner. Leverage AI to streamline operations and maximize profits.

According to a survey by ITR Co., Ltd. (ITR), the total sales amount of the eight major AI markets in FY2020 was 51.33 billion yen, an increase of 19.9% ​​compared to the previous year.

Due to the increase in work from home due to the impact of the new coronavirus, the use of AI is expanding in companies promoting work efficiency and DX initiatives.

Insufficient supply to meet demand for AI talent

Here, let’s check the demand for AI engineers with specific data.

According to a survey by the Ministry of Economy , Trade and Industry , there is a shortage of about 44,000 people in the gap between the supply and demand of AI human resources in Japan (supply-demand gap). is predicted.

With the expansion of the AI ​​market, it is said that the demand for AI engineers will continue to grow in the future.

AI engineers have high annual income

AI engineers are more difficult than other IT-related engineering jobs, so the annual salary is set high in proportion to the difficulty.

The range of skills required is wide, and advanced knowledge and know-how are required, so the current situation is that supply cannot keep up with demand.

Now, I will introduce how high the salary is actually based on “average annual income by age group” and “actual job offers”.

Average Annual Income of AI Engineers by Age

Reference: Average Annual Income.jp

Results calculated independently by Average Annual Income.jp based on the ratio of the average annual income and the annual income by age group of the National Tax Agency. Bonuses are calculated for four months, including summer and winter. * Since the annual salary system is not supported, the figure obtained by dividing the annual income by 12 is the forecast figure.

The average annual income for all occupations in 2020 was 4.36 million yen.

On the other hand, the average annual income of AI engineers of all ages is 7.52 million yen, which is about 1.7 times (3.16 million yen) higher than the average annual income of all occupations.

AI engineer recruitment example

Reference: Average Annual Income.jp

Looking at job openings for AI engineers, there are many companies that set annual income of 5 million yen or more even for new graduates, and depending on ability, it is possible to aim for an annual income of 10 million yen.

In recent years, with the increase in demand for AI engineers, the annual income continues to rise.

Is there a future for AI engineers?

According to a paper published in September 2013 by Frey & Osborne of the University of Oxford, advances in machine learning and mobile robotics will eliminate many jobs in the United States within 10 to 20 years (2023 to 2033). (replaced by machines).

It is said that the emergence of “no code” that does not require writing code and “low code” that requires writing less code is one of the factors.

In addition, companies are increasingly accepting foreign IT personnel, and the dependence rate on Japanese programmers in development work is on a downward trend.

4 reasons why AI engineers are told to stop

Although AI engineers are a popular profession with increasing demand, it is sometimes said that “AI engineers should stop”. Why are you saying that?

There are four reasons why it is said that “AI engineers should stop”.

1. Work is difficult
2. use math frequently
3. I have to improve my skills everyday
4. Desk work tends to lead to lack of exercise

I will explain each.

Work is difficult

AI engineers are in high demand, so the level of skill required is very high.

The programming language requires skill in Python, and knowledge of various algorithms such as search algorithms and optimization algorithms is also required.

Compared to IT and other engineering jobs, the work content is overwhelmingly difficult.

Use math frequently

If you want to become an AI engineer, mathematical knowledge is essential. Knowledge of linear algebra and calculus, especially statistics, is essential for AI development.

To be honest, AI engineers are not suitable for those who are not good at mathematics.

I have to improve my skills everyday

More than becoming an AI engineer, daily skill improvement is essential.

As engineers know, libraries, frameworks, and programming languages ​​are frequently updated. It’s not uncommon for technology from a year ago to become obsolete.

Therefore, if you want to become an AI engineer, you must always keep up with the latest information.

Desk work tends to lead to lack of exercise

AI engineers basically work at a computer for a long time. Sitting for long periods of time slows down muscle metabolism and blood circulation, which is detrimental to your health.

Also, spending a lot of time looking at a computer is not good for your eyes.

If you become an AI engineer, it is important to get into the habit of exercising to avoid the risk of illness due to long desk work.

5 reasons why AI engineers are said to be difficult

It is said that AI engineers are difficult to become among other engineering jobs. Why are you saying that?

There are five reasons why it is said that “AI engineers are difficult”.

1. It is difficult to acquire the ability to think logically
2. Technological evolution is fast and difficult
3. difficult to acquire mathematical knowledge
4. Difficult job
5. Difficult to recommend to clients

I will explain each.

1. It is difficult to acquire logical thinking

Developing logical thinking is difficult and you will have a very hard time until you do. Logical thinking is the ability to think logically about things.

AI engineers are required to understand the optimal system specifications and design, and then have the ability to assemble them. The ability to think logically does not come naturally, so it must be acquired through training.

2. Technological evolution is fast and difficult

New technologies related to AI are born one after another from advanced IT countries, especially the United States. In addition, existing technology will be advanced.

Therefore, it is necessary to be flexible and adapt to technology.

3. It is difficult to acquire mathematical knowledge

AI engineers are required to have a high level of mathematical knowledge. Knowledge of linear algebra, calculus, and so on, starting with statistics, is essential for AI development.

Development proceeds on the assumption that such mathematical knowledge is understood. Therefore, if you aim to be an AI engineer, you need to learn mathematics well.

You will have a hard time acquiring these because you need to learn them using specialized books.

4. The work is difficult

An AI engineer’s job is to provide AI with various data, process it, and train it. The ability to design so that ” machine learning ” can be performed efficiently is required.

Since machine learning requires considerable expertise, it is a very difficult job.

5. It is difficult to make proposals to clients

The client does not have development experience like an AI engineer, so they do not understand the mechanism and design of AI.

It is necessary to make a difficult proposal to the client in an easy-to-understand manner, such as “I will use this mechanism of AI to do XX”, so when making a proposal, it is necessary to communicate from the perspective of an inexperienced person.

How to become an AI engineer

You don’t need any particular educational background or qualifications to become an AI engineer. However, a high degree of expertise is required , such as learning programming languages ​​such as Python, which are required for AI development, and knowledge of IoT .

Therefore, it is important to acquire specialized knowledge in data science at a vocational school or science university that specializes in AI. Also, if you are a new graduate, participating in an internship at an AI development company or system development company and accumulating practical experience will be advantageous in job hunting.

If a person with a liberal arts background aims to become an AI engineer by self-study, considerable determination is required.

Characteristics of people suitable for AI engineers

AI is highly specialized and the technology is constantly advancing, so it is necessary to keep up to date with the latest information. Therefore, it is difficult to work as an AI engineer unless you are motivated to learn.

AI engineers have a strong interest in creating and using AI, and are suitable for people who like thinking about AI.

Despite the lack of motivation to learn and interest in AI as described above, if you aim to become an AI engineer just because the demand is high and the annual income is high, there is a high possibility that you will be frustrated.

4 Career paths for AI engineers

Many people are concerned about the career path of AI engineers. The skills cultivated as an AI engineer are in high demand and can be applied to other occupations.

There are four career paths for AI engineers:

1. AI/machine learning consultant in the future
2. Advance your career as a data scientist
3. Advance your career as an AI engineer
4. management position

I will explain each.

1. AI/machine learning consultant

The first is an AI/machine learning consultant.

AI/machine learning consultants utilize their knowledge of AI technology to make proposals according to client issues and needs, and solve problems. We will formulate management strategies using AI and propose products and services.

2. Advance your career as a data scientist

The second is career advancement as a data scientist.

A data scientist’s job is to solve problems based on data in various decision-making phases. We make full use of the machine learning system to analyze data and solve the problems of our clients.

3. Advance your career as an AI engineer

The third is career advancement as an AI engineer.

You can further improve your technical skills as an AI engineer and play an active role as a specialist in the industry. You can run on the cutting edge of technology and continue to provide value to the world with your own hands.

4. Management position

The fourth is management.

Management is responsible for managing project schedules and team schedules. As a project manager, you will be required to have the ability to consistently manage the project from launch to completion and to ensure that progress is made according to the time schedule.

Summary

This time, I explained the future potential of AI engineers, 5 reasons why it is said to stop, and the characteristics of people who are suitable for AI engineers.

AI engineers are a profession that is attracting worldwide attention and is in high demand.

The average annual income is more than 1.5 times higher than the average annual income of all occupations in Japan, and it is possible to receive an annual income of 10 million yen or more, so it can be said that it is a very dream job.

It is possible to become an AI engineer even if you have no experience, so if you are interested, please try it. Thank you for watching until the end!