Why should we learn AI?

With the rise of AI, the concept of learning is quietly undergoing a transformation, and this change has profound implications for everyone's future.

Nowadays, traditional positions are being impacted by AI technology, and there seems to be a sense of anxiety about being "replaced" in the air.

For example, jobs like customer service and translation can clearly be done quite well by AI.

In the past, we used to say that we should hone our skills and accumulate expertise. But now?

Forget about skills, the positions themselves might cease to exist.

But don't worry, is it really just "replacement"?

Digging deeper, it might be a change in form, but the essence remains the same.

The positions might be gone, but human value still exists.

Our learning goals may need to shift from "I want to become an expert in XX position" to "I want to master XX skills to solve a series of problems".

This is a shift from one extreme to another, a change in mindset.

In this age of information overload, what we need is to quickly find the information we need, and be able to think deeply, using strategies and knowledge to solve problems, rather than just rote memorization.

The key here is that we need to master the full range of actions from information filtering and analysis to application.

At the forefront of technology, you'll find countless free resources, courses, papers, tools, and more.

But the crucial question is, will you use them? Will you use them correctly?

That's why STEM education is so emphasized nowadays; science, technology, engineering, and mathematics are the cornerstones of the information age.

What's your own thinking like? What am I like in the learning process?

In the AI era, the path of self-learning is no longer linear; it's like a network, and you have to be the spider, able to weave webs on it.

So why not take action now and try to become a spiderman?

Note, not a failed man.

This is particularly popular now. As mentioned earlier, STEM, but adding art makes it STEAM.

Why?

Because engineering requires design, science needs presentation, technology requires interaction, and mathematics needs visualization. So, interdisciplinary means innovation, and it also means the future.

This is difficult to teach, and even harder to learn.

Imagine working with an AI that is a hundred times smarter than you. What do you do? Collaboration is not about obedience, nor is it about command; it's about complementarity.

Human intuition and experience, combined with AI's computation and analysis, that's the most powerful.

So, these shifts in mindset sound cool, don't they?

But the problem is, are we ready to actually do this?

Many people doubt whether such a shift in learning concepts is really applicable to everyone.

Some people think it's the result of idealizing technology, and reality may not be as good as expected.

In response to this, we have to admit that this idealization does have its good side, but it also presents us with new challenges.

For example, not everyone has the ability to quickly adapt to this fast-paced change.

And although resources are abundant, information inequality still exists—not everyone has equal access to learning resources.

Do parents and teachers know how to teach children to survive in the AI era? Do we have appropriate policies to guide this change?

In conclusion, the learning concepts of the AI era are indeed changing, and this change is redefining our way of life and our work philosophy.

We need to face it positively, prepare carefully, and adapt to the future with new learning concepts.

After saying so much, I hope it can bring you some inspiration, or even some impulse to take action.

The world of AI is also our world, let's embrace it together.

Feel free to continue the discussion and share your thoughts or stories. Let's learn and progress together.

Next, we will continue to discuss the "AI Beginner's Guide".

Stay tuned, and don't forget to follow me!

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Here's a concise and clear table of contents for readers to read the "AI Beginner's Guide" series articles in order:

Number Article Title Link Address 1 【AI Beginner's Guide】1: Cognitive Learning in AI Click to visit 2 【AI Beginner's Guide】2: AI Prompts (Prompt) Click to visit 3 【AI Beginner's Guide】3: Advanced Concepts of AI Prompts (Prompt) Click to visit 4 【AI Beginner's Guide】4: Principles of Content Output of Large Models (Part 1) Click to visit 5 【AI Beginner's Guide】4: Principles of Content Output of Large Models (Part 2) Click to visit 6 【AI Beginner's Guide】5: Tips for Writing Prompts (Part 1) Click to visit Readers can click on the link address in the table in order to read each article, in order to better understand the relevant knowledge and applications of AI technology.