In a world where AI is advancing at lightning speed, many professionals are feeling the heat. The fear of being replaced by machines is real, but guess what? There’s a way to stay ahead of the curve. Here’s how you can ensure you’re not just surviving but thriving in the age of AI.

1. Embrace Continuous Learning

The ability to learn is your superpower. Transforming your experiences into knowledge and using that knowledge to solve problems is what sets you apart. AI might be smart, but it can’t replicate the human touch of wisdom and creativity. So, keep learning, keep growing, and keep evolving.

2. Leverage AI as a Tool, Not a Threat

AI is here to stay, and it’s not all bad news. Think of AI as your external brain, a tool that can help you manage knowledge more effectively. Use AI to your advantage by letting it handle repetitive tasks while you focus on what you do best—innovating and creating.

3. Iterate Like AI

Just like AI continuously improves through iterations, you too need to keep iterating yourself. Self-improvement isn’t a one-time thing; it’s a continuous process. Keep refining your skills, updating your knowledge, and adapting to new challenges.

4. Master Knowledge Management

AI can help you manage knowledge, but it’s up to you to internalize and apply it effectively. Be proactive in seeking out new information, organizing it, and using it to make informed decisions. The better you manage your knowledge, the more valuable you become.

5. Harness Collective Wisdom

Combining AI with human collective wisdom can lead to better decision-making and problem-solving. Engage with your peers, share insights, and collaborate on projects. The synergy between AI and human intelligence can create powerful outcomes.

Staying relevant in the age of AI isn’t about competing with machines; it’s about leveraging them to enhance your own capabilities. Embrace continuous learning, use AI as a tool, iterate yourself, master knowledge management, and harness collective wisdom. By doing so, you’ll not only stay ahead of the curve but also thrive in this new era.

With the rapid development of artificial intelligence technology, many majors and occupations may face the risk of being replaced by AI. Many knowledge workers have some confusion and anxiety.

In fact, in many professional fields, human skills, wisdom and innovation are still inseparable, and many professional masters have emerged who will not be replaced by AI.

How can we, like them, not be replaced by AI? How to iterate yourself like AI?

In today’s article, let’s talk about learning ability and AI.

1. Don’t be hunted by the times, The only core competitiveness is learning ability

1. The essence of learning: transform experience into knowledge and use knowledge to solve problems

AI is coming with great force and has the potential to reshape all industries.

At a salon, everyone was discussing a topic: How to use AI to make our work more convenient? Some people even asked: AI is coming, as a natural person, do I still need to learn?

At that salon, I suddenly raised a rhetorical question: What we need to think more about is, how can you iterate yourself like AI?

In fact, we have not taught ourselves to iterate at all. We rely on AI to iterate. When we train AI, we actually do the opposite. What you really need to train is your own subconscious mind, and your subconscious mind is the AI you carry with you.

If you can’t make your subconscious mind work like AI and you can’t cooperate with the huge brain trust inside you, then training external AI is actually a lie.

On the contrary, in the AI era, the challenges to natural people’s learning ability, knowledge management, and knowledge refining and processing capabilities are getting bigger, not smaller.

When we first think of it, AI can do many things, but one reality we ignore is that AI is always with us.

For me, if I was asked to read from a manuscript while giving a lecture, I might collapse. Because I am not used to using manuscripts, I can use a post-it note to teach a whole day’s class. I only need to write the outline and then hand it over to my “AI”.

What I like most in class is that students ask me questions and comment on homework. I like the “you ask and I comment” and “you ask and I answer” methods.

Because at this moment, I automatically regard myself as an “AI” and divide it into a front stage and a backstage. The frontstage is the me you can see, and the backstage is the inner me. Any question you asked, I would turn to him (the me backstage, the me inside) , and he would tell me an answer, and I would be inspired by it.

The thinking about “how natural people can iterate themselves like AI” actually originated from a wave of reading on the topic of deep learning that I carried out in 2020. There was a lot of calculus in that batch of books, which was difficult to understand.

But when we finished reading all the books, I was stunned. Because I am a computer major, I actually know that the earliest AI relied on large sample sizes, large databases, and hard calculations. It looked smart, but was actually very stupid.

But after I looked at the deep learning theory, I discovered that this generation of AI is completely different. Its convolution integral and deep learning theory are so powerful.

Inspired by it, I thought about how to use AI learning methods to feed back the learning of natural people. After finishing this wave of reading, I launched a “Learning Power Jump Training Camp”, and the manuscript eventually evolved into this book.

The purpose of this training camp or the fundamental problem it wants to solve is how natural people can iterate themselves like AI. This is a very real problem for everyone.

The so-called “knowledge workers” are those who can turn experience into knowledge and then use knowledge to solve problems. If one of the two is missing, you cannot be called a knowledge worker.

However, these two points are precisely where AI is most powerful. Every smart driving car on the road now is accumulating data, accumulating experience, and then iterating the experience into algorithms, and the algorithms then guide all smart cars. Natural people must also be like smart cars so that we can survive in this era.

In the past, you could survive a lifetime with your stock of knowledge. Today you must have strong enough learning ability to survive. In the past, being able to drive might have been a lifelong job, but now you may not be able to survive retirement even if you change your career five times. One day, your career may suddenly be replaced by someone else’s.

When “Carrot Run” first became popular, I happened to be on a business trip to Wuhan and took a Didi Express. The master told me: Now I feel that I am being hunted by the times.

This master turned out to be a middle-level employee of an IT company. Due to poor management of the company, he voluntarily resigned, but he had nothing to do for half a year (no job found) . Then he got a car and drove around Didi. Not long after running, he noticed that the carrot was running away. came out, so we (natural people ) are being hunted by the times.

In fact, the only core competitiveness that will not be hunted down by the times is learning ability. The core of learning ability is to transform experience into knowledge and use knowledge to solve problems.

Of course, in this process, new fields will continue to emerge. No one has ever worked in these fields (such as knowledge anchors) , and everyone is exploring them. At this time, the comparison is learning ability.

When I wrote the preface to a student’s book, I wrote: Curiosity, the spirit of exploration, and the ability to learn can overwhelm all experiences. Because experience is not worth mentioning in this era, and all experience cannot compare with your spirit of exploration and your ability to learn.

2. Thinking from practical problems is the best way to learn

When you think about an issue for a certain period of time, the issue becomes the subject of thematic reading. Buy books around this issue, buy a bunch of books, and then study and think with the questions in mind.

The reason why I am interested in learning ability is because I have been engaged in teaching work. Teaching and learning are interrelated. As the saying goes, “If you don’t know how to learn, how can you teach?”

At first, I thought cognitive psychology was important because it was about a person receiving and processing information, but this was not enough.

I was just thinking, if artificial intelligence has such a strong learning ability, how does it learn? I did a thematic reading around this issue. At that time, ChatGPT had not yet appeared in China. I just paid attention to some very cutting-edge theories, such as the theories of cutting-edge figures such as Kurzweil.

When I saw what they wrote, I realized that in the 30 years from the 1990s to now, human research on their own brains has reached a very high level.

Because we can clearly see the brain activity at this moment with the help of scientific means, such as wearing a helmet.

These cutting-edge brain sciences have not been applied to the learning of natural people, and the learning of natural people is still at the level of the 1960s and 1970s. Now the entire academic community, or the entire industry, is using the learning results of brain science to study artificial intelligence.

But our education is still very backward. Because I am in the education track, I can naturally think of how to use the research results of human brain science to help natural people learn.

Many of the books I write start from a question, and then through a wave of topic reading, I structure it as my input. After having a lot of input, I can output a knowledge system of mine to recruit students.

Then answer students’ questions and comment on the effects. After a few rounds of iterations and rich content, when you feel that it can be published, you will publish a book.

From the epidemic to now, 9 books have been published in less than 5 years. If I had a closed brain, you wouldn’t read the books I wrote by reading. It’s precisely because I treat the classroom as a practice scene, fully communicating with everyone, and forming a lot of fresh material that I can be so productive. 2. The relationship between knowledge management and AI

1.AI is the best plug-in for the brain

My first job was at Lenovo. It happened to be that McKinsey was consulting for Lenovo. I was fortunate enough to be selected into the project team. The set of processes I was responsible for was the knowledge management process.

I didn’t have a particularly intuitive understanding of knowledge management at the time, but observing the way McKinsey consultants worked gave me great inspiration. Let me explain two aspects.

First of all, I found that McKinsey consultants are generally very young, but they can talk to senior managers with rich experience in large occasions, and can make these managers nod in agreement.

Because McKinsey has been accumulating its internal knowledge base since the 1970s, even consultants who have only worked for three or five years can report their work and present plans in front of management. They are not fighting alone. Behind them are tens of thousands of people around the world. Supported by the knowledge base of professionals.

From this, I thought that if a person wants to achieve rapid growth, there needs to be something like a plug-in brain behind him. And this plug-in brain does not necessarily rely entirely on itself. In fact, it is very similar to the way we use artificial intelligence, knowledge bases and large models today.

As the project progressed, we encountered more and more difficult questions for which answers could not be found in the standard knowledge base.

At this time, I discovered that McKinsey had a second powerful solution. They invited me to the Kerry headquarters center in Beijing, where they held a meeting and connected me with an expert in the UK. Help me solve these difficult problems by interacting with experts.

It was precisely because I had such an interesting experience while participating in the project that I later embarked on a related path.

Of course, in this process, knowledge management helps enterprises systematically accumulate, refine and finally apply knowledge. It is an interdisciplinary field.

In the learning process, everyone will inevitably acquire and absorb knowledge, and then internalize it into their own knowledge network, etc., and finally be able to produce results based on actual problems to solve difficult problems. We have also been developing this process. Research.

Later, we discovered that if we want to complete the life cycle management of knowledge more efficiently, we need to systematically sort out each individual’s knowledge, so that individual knowledge forms organizational knowledge.

During this process, we encountered many problems and challenges, such as: How can we make it easier for everyone to retrieve knowledge? How to proactively push appropriate knowledge to everyone based on their work scenarios?

More than 20 years ago, the relevant needs were relatively simple. But as the demand continues to deepen, everyone is wondering whether retrieval or search can be more intelligent, and whether recommendations can be more intelligent, so we have started research in the corresponding direction.

In recent years, large models have emerged. In many important application scenarios, it can be used as a great plug-in tool, which can better support us to retrieve knowledge more intelligently, answer knowledge-related questions, and make knowledge recommendations.

This coincides with the realm we originally dreamed of, so knowledge management and AI are naturally integrated.

Although these tools and techniques can give us powerful assistance, we cannot rely entirely on them. If we sit there and do not seek our own development, it will definitely not work.

2. “ABC” knowledge view model

Later I proposed a knowledge concept called “ABC”.

“A” refers to AI, which is “AI oriented”, that is, knowledge management oriented to artificial intelligence;

“B” refers to “Business”, because the main core of our services in the enterprise is business, so the business-oriented aspect must not be ignored, it is our source;

“C” refers to “Collective-wisdom”, which means collective wisdom, which is a return to human beings.

With “A, B, C” as the three-layer structure, I call it “one body and two wings”. Specifically, put “B” in the middle of the triangle, “A” on the left, and “C” on the right, so that the layout of “A, B, C” is formed.

Among them, “A” is more technical, and “C” is more people – oriented . In this way, “technology” (technology ) , “process” ( business ) and “people” (people ‘s collective wisdom) ) is consistent with the process, technology and people in Western management, so I think this triangle structure is wonderful.

3. Two things in knowledge management: plug-in and internalization

Tian Junguo: In the final analysis, knowledge management mainly involves two things, one is plug-in, and the other is internalization.

The so-called plug-ins use current AI tools, such as large models, which can be regarded as the knowledge base of all mankind. However, public resources such as plug-ins can be used by everyone. At this time, it depends on whose application level is higher.

Whether you can ask questions accurately is the key to measuring your application level, and whether you can ask precise questions depends not only on the use of tools, but also on your own knowledge accumulation.

Business can be understood as an application scenario of knowledge management, which is another element besides the plug-in part. There is also the entire inner collective subconscious part of the human being that is important.

On the one hand, we must be good at truly internalizing some core skills into our own abilities. On the other hand, we must also be good at using plug-in tools, and we play the role of a mediator in this.

No matter how powerful the AI is, if we don’t have the ability to interact with it, it will be of no use and we can only be Lin Yuanxianyu.

On the other hand, the ability to interact with AI is inherently challenging in many aspects such as one’s own learning ability, model refining ability, and knowledge accumulation.

Therefore, the two are essentially complementary to each other, not one that trades off the other. You cannot think that other aspects are not needed just because you have AI.

As AI continues to iterate, we ourselves must also iterate, and organizations also need to iterate.

As early as the 1970s, McKinsey had basically realized that every small consultant seemed to have an AI support behind him.

It’s just that the “AI” at that time was not as convenient as what we use today, but the relevant concepts were already implemented at that time.

4. Organizational evolution requires the combination of general large models, industry knowledge bases and personal knowledge bases

Judging from the current development trend, the core part of AI in enterprises is natural language processing. It can assist us well by processing, mining, and combining text to express the results in the form of language.

However, many problems also arise when applying general large models, such as the illusion of knowledge. When we need very precise and authoritative knowledge, if the large model has not learned the relevant content, it will seriously talk nonsense.

Currently, there are solutions for this situation in the enterprise. Since the general large model has learned common human knowledge, it can be used as a plug-in brain.

However, for the knowledge of the industry and professional fields within the enterprise, the external Internet cannot provide it to large models, so it is necessary to establish another knowledge base within the enterprise.

In this way, the general large model is combined with the internal knowledge base to form what is now called the RAG ( Retrieval- Augmented Generation) model.

Further extending this logic to the individual level, the know-how possessed by an individual, that is, its most essential and unique thing, cannot be replaced by general large models and enterprise vertical domain knowledge bases.

Therefore, I think that a more perfect combination can only be achieved by combining the general large model, the knowledge base of the company’s own unique fields and industries, and then internalizing it into a very unique knowledge base of the individual itself.

5. No matter how powerful plug-in tools are, they cannot replace your own learning.

Essentially, we need to understand the working mechanism of AI, and treat our subconscious as an AI-like existence, so that we can better use AI as if we cooperate with our subconscious.

I think there is a hierarchy: consciousness, subconscious, AI, and the collective human subconscious.

If you want to practice working with AI, you must first practice working with your own subconscious. This logic seems self-consistent, but the key is to practice and practice. Among these, I found that the most important point is to trust your subconscious mind.

However, many people do not trust their subconscious mind. For example, some people have to prepare detailed verbatim drafts when giving speeches and rely on their conscious mind to work throughout the process.

And I think, for example, when talking to people, you can first clarify what points you want to say. After throwing out the first point, you can let your subconscious mind dominate the subsequent content.

Behind those inspirations and intuitions, the subconscious mind is also at work. Don’t think that only the conscious mind has the ability to model, the subconscious mind also has it. Just like the saying goes, “If you have read three hundred Tang poems, you can recite them even if you don’t know how to read them.” The subconscious mind can understand and play a role after reading it for a long time and experiencing it a lot.

This particularly resonates with me. The ancients talked about “the inner sage and the outer king”, which means to open up the inner and outer.

Today’s data tools such as AI are mostly external things, but if you want to open up the inside, it involves the subconscious or collective subconscious.

Buddhism talks about the eight consciousnesses. The first six consciousnesses are the eyes, ears, nose, tongue, and body. Together with the sixth consciousness, the seventh and eighth consciousnesses are similar to the subconscious mind or collective subconscious mind. 3. The secret of human learning ability

1. Individual learning ability

① The five major networks of the brain

The structure of the brain is extremely complex. It has prefrontal lobes, parietal lobes, occipital lobes, etc. We need to present these organs in a more understandable way and allow them to perceive a certain state of adults.

I abstract it into five aspects in the model: perception network, association network, decision-making network, reaction network and learning network.

The perceptual network is associated with the eyes, ears, nose, tongue, body, etc. They are equivalent to the sensors through which we perceive the world. These sensors can be divided into conscious and subconscious levels.

In other words, the part of the signal that you are aware of is relatively obvious, but sometimes you also process the information at a subconscious level.

Information flow and energy flow also belong to the category of perception: information flow is content expressed through language; energy flow is content expressed through body language, voice intonation, etc.

The difference between people lies in perception, and perception itself is like a filter. What I noticed, you may not notice, because everyone will automatically filter the signal based on their own professional background.

For example, in a room, when an architect comes in, he will immediately pay attention to the building structure, but other people may not necessarily look at it. This is a reflection of the differences in the perceptual networks of different people.

The same goes for Lenovo. When information enters the brain, it can activate certain information in the brain’s neuronal circuits. This is the association that is different for each person, and each person’s association is different.

For example, someone asked where the provincial capital of Shaanxi is? You will say “Xi’an” without thinking. If I ask next, what clues from my question make you think of “Xi’an”? I believe everyone is different.

Some people may think of Liangpi and Roujiamo, some may think of the Big Wild Goose Pagoda and the Thirteen Dynasties, and some may think of the Terracotta Warriors and Horses.

When the associative network and the perceptual network are combined, decision-making is entered. With the same stimulus, some people will be pessimistic and some people will be optimistic. Why? Because the Lenovo network is different, the decision-making algorithm is also different, and there will be a reaction after the decision is made.

Therefore, the brain is such a cyclic network composed of perception, association, decision-making, and reaction. Of course, it is also possible to go directly from perception to reaction, which has already formed an intuitive habit, or it is also possible to perceive a direct reaction without association and decision-making.

In short, the brain will respond in many ways, but they are all inseparable from these four major networks.

The perception network, association network, decision-making network, and reaction network are working systems, and there are more advanced levels.

For example, when a primitive man was hunting, the prey ran away, and he thought under the roots of the tree: If a person was arranged to block the prey somewhere, the prey might not be able to escape, but what if the prey injured the person blocking the prey? Finally I thought of digging a trap.

This thinking process is what the learning network does, that is, constantly reviewing the cooperation and collaboration between the four major networks and their respective response capabilities, and then achieving improvement, that is, learning and upgrading the algorithm.

So, I built a learning network on top of these four networks. In this way, I structured the complex brain into five major networks, four of which serve as working systems, while the other learning network is constantly iterating.

The reason why we need to set up a learning network involves the metacognitive ability in education. I abstracted it. When the complex brain is abstracted into these five major networks, the learning ability model will naturally emerge.

② ACCP model

The ACCP model corresponds exactly to the four major networks of the brain.

“A” stands for “Absorb”, which means absorption. It corresponds to the activity of the perceptual network, which is the absorption of knowledge.

There are two “C”s: the first “C” (construct) refers to the “construction” of constructivism , which corresponds to the associative network, emphasizing the construction of a knowledge system through association in the learning process.

The second “C” (correct) refers to the decision-making network, which means that you must be creative when making decisions and be able to make appropriate judgments and choices.

“P” stands for “perform” (reaction) , which involves the practical application of knowledge and corresponding responses.

I think the focus of learning lies in the association network and the decision-making network, which are the two “Cs”.

There are two signs to measure whether knowledge is truly mastered: one is forming a personal version of understanding, and the other is being able to carry out personalized applications.

Even if you memorize the “Tao Te Ching” by heart, you may not really understand it. You must transform it into your own personal version of understanding, explain what it is talking about in your own words, and be able to apply it flexibly when encountering actual scenarios. .

Real-life scenarios are often different from those described in books. No knowledge can directly solve real-life problems without making any changes.

All real-life problems must be adaptively transformed and used creatively, and then through the response network, unique experiences will be obtained. Then through personal processing, the experience is transformed into a personal version of knowledge.

When the personal versions of knowledge formed by individuals are exchanged, they converge into collective wisdom. It is through this continuous exchange that collective wisdom and individual wisdom are developed.

In fact, teaching is the process of disseminating collective wisdom among individuals.

Why does it spread between individuals? The reason is that all experiences cannot be copied directly from one individual to another. Experience must be transformed into structured knowledge so that it can be replicated between individuals.

Teaching is essentially the transmission of structured knowledge. However, even if you learn structured knowledge, if you cannot convert it into your own language, you have no real understanding and no real comprehension.

If you cannot adapt and adapt flexibly according to the scene you are in, and implement personalized applications to solve problems, it means that the knowledge has not been fully learned. And when the problem is solved through knowledge, new experience will be formed.

It can be seen that the learning process must follow this process:

First, construct public knowledge into a personal version of knowledge, that is, realize the construction of knowledge;

Second, use the constructed knowledge to solve problems. This process is to empiricize the knowledge;

Third, after experience is converted into knowledge, the knowledge after being intellectualized again can be turned into public knowledge and disseminated, thus forming a complete closed loop.

This recycling ability is an essential ability for future individuals. If you have this ability yourself, you can not only learn the ability to guide your own subconscious work, but also learn the ability to guide the work of AI. This is actually a meta-ability in the AI era.

2. The essential meaning of knowledge

① Knowledge is not only content, but also a process

I have been engaged in knowledge-related research for more than 20 years. In this process, my understanding of the word “knowledge” has been constantly iteratively updated.

From the perspective of nouns, people usually think of a book, a document or a piece of content. Combining the five major network-related theories, we find that knowledge is more like a process.

② “Knowledge” is objective and “knowledge” is subjective

Tian Junguo: There are many views on knowledge. One view is that the key to knowledge lies in “knowledge”, and “knowledge” is a person’s understanding of information.

In the past, we generally believed that knowledge is objective, but this is not true. Knowledge must be subjective, because different people have different understandings. In other words, “knowledge” can be objective, but “knowledge” must be subjective.

So, can knowledge be considered objective? When a subjective cognition that most people agree with can be reluctantly called objective, that is, the subjective cognition that most people in the outside world agree on can be relatively regarded as objective.

A person’s understanding and definition of knowledge itself will determine the routines, methods and specific methods used in knowledge management or teaching.

Real experts all have one characteristic: they are particularly good at asking first principles. They are always thinking deeply and silently about work-related issues, and they always see things more deeply and thoroughly than ordinary people.

3.AI, subverting and reconstructing the education system

I want to talk about it from two aspects. On the one hand, there is a definition of knowledge as “the ability to act effectively”, which makes sense from an application point of view as what is now known as a reaction network (reacting based on problems) .

On the other hand, let’s take a look at ChatGPT, which is a reconstruction of the previous education system.

In the past, our education system was as follows: starting from birth, everyone spent many years learning knowledge from elementary school to university, and the knowledge was instilled in a very systematic way. As for what kind of structure this knowledge forms in the brain, we have no idea. Not sure.

But ChatGPT is different. It does not follow a standard knowledge system. As long as you ask a question based on it, it will give you an answer.

This subversion and reconstruction of knowledge will have a major reverse impact on our traditional education or knowledge system model.

Modern education is actually a way of coexistence of evolution and degradation. Why do you say that? Evolution and degeneration are interrelated. Where there is evolution, there must be degeneration.

In the education process, we often want to reinforce a fixed response pattern, but in doing so, we kill all other possibilities.

Therefore, evolution and degeneration exist simultaneously. As we grow, we are actually evolving and degenerating at the same time. Don’t think that growth is all about evolution.

Lao Tzu said, “Things grow old when they are strong, which means they are immoral, and they have already been immoral.” This means that when things develop to be strong, they will become old and will soon die.

In fact, human growth and maturity sometimes mean the solidification of thinking. Once this solidification is formed, people have no flexibility to adapt to social changes and the external environment.

Learning is actually a double-edged sword, it has both positive and negative sides, which we must understand.

Just like the ChatGPT we are discussing now, one of its advantages is that it can overcome the disadvantages of forming a solid reaction pattern during the learning process to the greatest extent, because it is constantly iteratively updating itself.

People also need to constantly iterate and update themselves. There is a saying that goes well, if you don’t feel like a fool at the beginning of the year, it means your progress is too slow.

4. SECI model of organizational learning ability

Knowledge that can be spoken and expressed is just a way for us to objectively express knowledge. Generally speaking, knowledge that can be expressed in strings, sounds, images, etc. is a kind of explicit knowledge.

The SECI model was proposed by the famous Japanese scholar Ikujiro Nonaka, who is known as the father of knowledge creation. He believes that the essence of knowledge innovation starts from the sharing and exchange of tacit knowledge between individuals. This is the meaning of “socialization”.

In the SECI model, the first quadrant is “S” (Socialization) , which is the level of socialization and socialization of tacit knowledge. Just like in the process of communication today, we are actually mobilizing our subconscious mind and stimulating tacit knowledge.

Therefore, it is impossible for someone to become an expert by practicing alone in seclusion for decades, because knowledge emphasizes the mutual excitement and collision between multiple people. Only in this way can true knowledge and newer things be produced.

The second quadrant, “E”, is “exteriorization ” . The process of expressing our hidden things and making them explicit is the real key to measuring whether a person can speak knowledge thoroughly, write well, and explain it clearly.

In this process, we often refine tacit knowledge through review, knowledge extraction, etc. This not only tests our individual knowledge literacy, but also tests the organization’s ability to extract these tacit knowledge.

Of course, once documents, books, etc. are formed, explicitness has been completed.

The third quadrant, “C”, is “combination” . Most of our knowledge bases and information systems are established mainly in this quadrant.

The fourth quadrant, “I”, is called “internalization” (the process of internalization) , which means internalizing knowledge into one’s own ability.

We often have this question, why can’t we live a good life despite learning so much knowledge and reading so many books? This depends on whether the internalization is done well.

There will be a thousand Hamlets in the minds of a thousand people, and there will be a thousand Lin Daiyu in the minds of a thousand people. Because there are differences in the five networks of each person’s brain structure, even if they are exposed to a large amount of externally explicit information or knowledge, But the structure of everyone’s brain is completely different.

When a person reaches a certain level in speed and intensity during the rapid iteration of knowledge, he can slowly stand out from the crowd and become a master, expert, or awesome person.

This is what I understand to be the resonance between the SECI model and the knowledge management model when viewed from an organizational perspective. 4. Learning ability jumps, iterates like AI

1. The core of AI learning ability: algorithm, computing power, and data

The power of artificial intelligence today is mainly reflected in three things.

First, it is never tired and can work 24 hours a day, without meals or wages. The reason is that it performs well in knowledge transformation and can efficiently transform “C” (explicit knowledge) into “I” (internalized knowledge) . It is learning while working and has a strong learning ability.

Secondly, it is also extremely powerful in “S” (socialization, sharing of tacit knowledge ) . The experience accumulated by one terminal can be immediately shared with all other terminals. In human society, there are large barriers to sharing experiences between people, including limitations due to language and other factors.

Third, artificial intelligence can simplify many aspects of human learning. Basically all terminal data will be accumulated into the back-end database, as will experience accumulation, and it can work with algorithms to achieve seamless connection from “C” to “I”.

However, natural people usually work and study separately, and they feel that work is just to deal with tasks, unlike artificial intelligence, where work and study are the same. This highlights the gap between carbon-based life ( human beings ) and silicon-based life (artificial intelligence) .

The most important elements of artificial intelligence are nothing more than algorithms, computing power and data.

For the learning ability of natural people, algorithms can be understood as various methodologies and routines for doing things, such as PDCA, etc., which can be regarded as personal algorithms.

But many times we rely on experience to solve problems. When encountering a sudden problem, we may not quickly extract an established algorithm, but inspiration often emerges.

The so-called inspiration is actually the modeling of the subconscious mind, just like the principle of “familiar with three hundred Tang poems, even if you can’t read poems, you can still recite them”. Because the subconscious mind has already modeled the poems, but it has not refined them into specific routines for composing poems such as Ping Ping Ling Ze.

When reading has accumulated to a certain level, even if there is no clear algorithm for poetry composition, words can be adjusted based on feeling. This shows that when a person has rich experience, in the absence of a ready-made algorithm, the data itself can accumulate an algorithm.

This also reflects a troublesome problem in today’s learning. In the classroom, teachers mostly teach algorithms, but teachers’ decades of experience cannot be passed on to students one by one.

Moreover, the algorithm seems to have routines to follow, but when old experts solve new problems, they often rely on their own data (experience) . Experience can be transformed into knowledge once it has been used to solve a specific problem.

2. Have more conversations to enhance the driving force of your subconscious mind

Vygotsky said: All learning occurs twice, one is the communication process between individuals, and the other is the communication process within an individual. For individuals, the intra-individual communication process is where learning occurs. The final step.

No matter how much others say or how they say it, it is ultimately up to you whether you accept it or not. Only when you accept it and internalize it into your own understanding can real learning happen. As Jergen famously said: “Everything I say means nothing unless you think it means something.”

In other words, we ourselves are the final judges of learning outcomes.

Even if you memorize what the teacher said by heart, if there is no inner intra-individual dialogue, it will only be a mechanical response. Intra-individual dialogue is the “last 100 meters” where learning really takes place.

All inter-individual conversations are often triggered by intra-individual conversations. For example, if I want to talk to others about something, I must first make a rough draft in my subconscious.

There are some effective tips for learning to work with your subconscious mind.

For example, when preparing lessons, you can give your subconscious an “anchor” a few months ago and clearly set a specific problem to solve.

Once this “anchor” is set, the subconscious mind will continue to brew. After a long period of time, you may suddenly have a moment of enlightenment one day, and the solution to the problem will suddenly become clear.

3. Build a field and stimulate learning ability

I am now particularly emphasizing the “field” situation. Just like when we are chatting now, the information flow may be the main focus at the beginning, and everyone is more reserved. When they gradually let go, they can talk about whatever they want. This has reached a certain level. This relatively relaxed state can be called a “field”.

The field has its own specific definition. People actually have two basic states: one is the “me” state, which means they are more focused on themselves; the other is the state of “me” that they forget about when they are chatting. Only the state of “us” remains.

In the field of collective decision-making or collective learning, it has a great impact on the quality of the final learning results, because it will not only affect everyone’s mood, but also affect everyone’s energy state.

Factors that affect the quality of decision-making often do not simply depend on the amount of information. I personally think that there are at least two factors that are equally important, namely the person’s energy state and the amount of information .

When people are in a low-energy state, they will activate the fight-flight mode, which means the “ego” will be stimulated. Everyone is calculating their own little calculations and only thinking about their own security.

Discussing issues in this context makes it difficult to make decisions that are better for the group. Everyone will use the matter itself to hide their hidden interests and demands and give some high-sounding reasons. Decisions made in this way will often deviate from the right direction.

On the contrary, when everyone is in a high-energy state, they will think about how to make the organization better. However, there is a risk here. As mentioned in “The Crowd”, when a group is in a high-energy state and one person sets the pace, the larger the group, the more likely it is that problems will arise.

Everyone may be thinking about the same thing, and at this time the IQ may become very low. For example, as soon as everyone responds, they become a mob, and everyone’s IQ instantly drops to zero.

Therefore, when entrepreneurs run an organization, they must combine the two factors of sufficient information and energy status. Sometimes the energy is too high and it becomes a state of excitement, responding to hundreds of responses at once, which can easily lead to problems; sometimes it doesn’t work if the information is insufficient, which can also affect the quality of decision-making.

These two scenes need to appear alternately frequently. Sometimes it requires everyone to be in a higher state, but when the state is high, it is easy to lose IQ, and sometimes it requires sufficient information.

For individual decision-making, at the moment of decision-making, you should also ask whether your current state is high or low, because it is easy to get excited and make mistakes if the state is too high, but it will not work if the state is too low, so these two factors mainly affect decision-making dimensions.

Embrace Continuous Learning

Leverage AI as a Tool, Not a Threat

Iterate Like AI

Master Knowledge Management

Harness Collective Wisdom

With the rapid development of artificial intelligence technology, many majors and occupations may face the risk of being replaced by AI. Many knowledge workers have some confusion and anxiety. In fact, in many professional fields, human skills, wisdom and innovation are still inseparable, and many professional masters have emerged who will not be replaced by AI. How can we, like them, not be replaced by AI? How to iterate yourself like AI?

So, are you ready to future-proof your career? Let’s get started! 🚀

How to Stay Relevant and Not Be Replaced by AI: The Ultimate Guide