Or is it too late to learn AI?

Often, when I share resources for learning, AI and Data Science from different sources (including Google and courses Harvard textbooks YT), hear repeated, fear of people, it's too late to learn AI and Data Science. The reason for this is the fact that millions of students are learning in Machine Learning and artificial intelligence, and in these areas may soon learn satiety.

In addition, the market is not so much work for professionals in the field of AI, especially in a recession. The famous Machine Learning course by Andrew Ng on Coursera which is, in turn, has almost 4 million students.

At the time of writing this article, on LinkedInie was more than 100,000 jobs related to Machine Learningiem.

Unfortunately, it is clear that far more students than job offers. It is the ratio of 40:1 if you only look at the number of students in the previous course.

Why, however, still need to learn

Despite this, it is worth to learn AI. There are several prerequisites. I will describe them below.

Responsibility for the implementation of new ideas in organizations

First, let's talk about the fact that the responsibility for the implementation of new ideas in the organization. The creation and deployment of AI is now much easier than ever was (especially considering tools like Obviously.AI). This means that employees can add more value, including their tools, AI skills.

Those who develop new solutions with the use of AI rather not contribute to the creation of new jobs in the field of AI. We have, however, more examples.

Great motivation to be human opracowującą new ways of development of the organization. It is potential to automate repetitive and mechanical options and focus on the tasks that typically require human creativity.

Not to mention the fact that the skills related to AI can raise wages and give Your career a kick.

For example, marketers can use AI to predict customer behavior, create a face, as well as identification of demographics. Retail staff can optimize the range, predict the use of reserves, predict staffing needs, etc., the Employees of the insurance sector, can use artificial intelligence to predict future claims, predicting the risk of litigation, the ability to acquire rights and so on.

Prospects and possibilities are absolutely unlimited.

Self-employment

We have another plot, which was not included in the sea of vacancies related to Machine Learningiem entrepreneurship.

This is the second, more dangerous side of creating new solutions for organizations. And that's because it means to walk in their own ways, to look for new ways to add value in the market, without any support or any kind of stabilization.

High risks, however, often with a high salary.

Suppose you start in thirty startupie as an employee of the company, and you are one of the leading engineers in their field. For Holloway.com you can expect the equity of the company at the level of 0.25% -0.5%.

If you start on your own, then you have 100% participation in the capital at the beginning. In that moment, when you add the co-founders, employees and investors, this figure is omitted. However, there is quite a lot of potential.

Knowledge and skills

Even if we come up with new areas of business development, entrepreneurship, or a new role, You're not interested, then still you have the opportunity to learn new things.

AI now in any part of the industry - recommendations from Amazon, Spotify, Netflixa, or Tindera, search results in Google and YouTube. AI helps even in the tracking associated with COVID-19, as well as in the development and delivery of vaccines.

To be aware of the latest technology and truly understand the world, the study and understanding of AI is important.

Summary

Learning AI, the game is worth the candle, and probably always will be. Even if the market will glut (which is actually not yet arrived, so as still there is a lot of new jobs), it is the potential for people who want to change the how to operate various projects and organizations, and to develop their businesses.

Skills related to AI faster and faster become a mandatory question in order to be “in the know”.

The original text in English can be read here.