I control the AI ​​empire thanks to the geniuses in my group.

Su Zhou carefully considered his words before replying.

[A Warrior Who Endures Hard: Mathematics specialization, primarily focusing on combinatorics, number theory, and geometry...]

Su Zhou felt that just talking wasn't enough, so he also posted a question in the group.

then……

[Newton: Hmm, sir, I won't comment on this problem.]

Leibniz: Pfft!

Shannon: Hahahaha, I can't take it anymore...

[Von Neumann: So you're teaching children?]

[Turing: Combinatorial number theory and geometry...isn't this just something for kids to learn?...You're really wasting your talent!]

Su Zhou: "..."

Do you want to listen to what you're saying?

To this group of people, the problems in math Olympiads are no different from origami crafts in kindergarten.

But Su Zhou did not refute it.

His current persona is that of a "big shot who condescends to teach kids," so refuting him would only make him seem suspicious.

So he climbed up the pole.

[The Eagle Warrior: There's no way around it, I promised them, so I have to see it through. The only problem is there are a lot of people, over a dozen, how to train them is a problem.]

The moment those words were spoken, the atmosphere in the group chat changed drastically.

The laughter that had been filling the air suddenly stopped, replaced by a deep silence.

Three seconds later, Newton broke the silence.

[Newton: A dozen little devils... Boss, I sincerely suggest you think twice.]

Leibniz: Although I'm not happy with Newton, I second this point.

[John von Neumann: +1.]

Shannon: +10086.

Su Zhou looked completely bewildered.

What's going on? These geniuses, who stand at the pinnacle of human wisdom, react as if they've heard some kind of horror story the moment they hear the words "teaching students"?

[Newton: When I was the Lucasian Professor at Cambridge, I taught optics and calculus. Do you know how many students came to my class?]

Shannon: How much?

[Newton: Often there were only single-digit numbers. The worst time was when the entire lecture hall was empty, not a single person was there, and I lectured to the chairs for the entire period.]

Leibniz: Hahaha, Isaac, so you have moments like this too!

[Newton: Stop laughing. When you popularized your calculus notation system, weren't your students just as confused?]

Leibniz: That's different! My system of symbols is supremely elegant! It's their lack of comprehension that's the problem! ∫ and d/dx, such perfect expressions! They actually said they couldn't understand them; I still can't figure it out!

[Von Neumann: So what is your solution?]

Leibniz: I made the symbols bigger.

[Von Neumann: Is this what you call a solution?]

Leibniz: At least they saw it clearly! If they still don't understand after seeing it clearly, then it's not my problem.

[Turing: When I was in Manchester, I supervised graduate students. Once, I asked him to run a verification program for me. I wrote out the logic very clearly, and labeled every step. Guess what?]

[John von Neumann: Did he go to the wrong place?]

[Turing: He didn't run away. He looked at it for three days and came to me saying, "Teacher, I think there's a bug in this logic." I spent an hour explaining to him that it wasn't a bug, but a feature. Then he nodded and said he understood. The next day he came again and said, "Teacher, I found another bug." When I looked, it was the same place.]

[Turing: Later, I couldn't stand it anymore, so I broke down that logic into thirty-seven small steps, each with a natural language comment; I was practically ready to draw a diagram to explain it to him.]

[John von Neumann: Is it useful?]

[Turing: It worked. He finally stopped asking me questions because he switched teams.]

[John von Neumann: A moment of silence.]

Su Zhou watched as the group of people went back and forth exposing each other's shortcomings, and he couldn't help but laugh.

The most brilliant minds in human history have all produced students with remarkably poor teaching abilities.

It seems that geniuses are truly not suited to teaching!

Suddenly, a system notification popped up in the group chat.

[System notification: Shannon has invited Ramanujan to join the group chat.]

[Shannon: We were just talking about teaching students, and I brought someone in @Ramanujan. Come on, tell everyone about your teaching experience back in the day.]

[Ramanujan: Me?]

[Shannon: You, didn't you also teach students when you were in England? Did your students really handle your style of just spouting formulas purely from intuition?]

Ramanujan.

An Indian mathematician with almost no formal mathematical training derived thousands of mathematical formulas purely through intuition and inspiration, many of which are still being verified and applied today.

If this person were to teach students, the scene would be quite exciting.

[Ramanujan: I did briefly teach mathematics at the university in Madras.]

[Von Neumann: How effective was it?]

[Ramanujan: Not good. I wrote the sum of an infinite series on the blackboard, and a student asked me how I derived it. I said the formula was correct. He then asked me for the proof, and I said the formula was correct.]

[Newton: And then?]

[Ramanujan: Then he asked again, and I said that the goddess Namagiri told me in a dream, so it must be true.]

Turing: ...

[John von Neumann: ...]

Leibniz: ...

[Von Neumann: Good grief, I only omitted seventeen steps of derivation, you skipped the entire proof and replaced it with a dream from a goddess, you win. You're the grandmaster of teaching slacking off.]

[Ramanujan: But that formula is indeed correct; it took Hardy three years to complete the proof.]

Shannon: Yes, yes, but you can't expect students to have the same dream!

[Ramanujan: What are you laughing at? This is my real experience (╯‵□′)╯︵┻━┻]

After reading this conversation, Su Zhou couldn't hold back anymore. The teaching skills of this group of people were truly amazing.

If they were to teach their eleven customers, they'd probably return the goods, get a refund, and block them within ten minutes.

However, the experience of this group can be used as a negative example.

He should be able to avoid the pitfalls that geniuses have encountered by doing the opposite of what they have done.

Just as Su Zhou's thoughts were becoming clearer, a message suddenly popped up in the group chat.

[Rosenblatt: @EagleWarrior, buddy, since you're going to be a teacher, I'll give you something.]

Immediately afterwards, a red envelope icon popped up.

Su Zhou clicked on it.

[Congratulations on receiving a red envelope: Perception, Transmission, Reconstruction – Enhancing Teaching Cognition]

[Note: This skill originates from Frank Rosenblatt's lifelong research on the cognitive mechanisms of human neural networks, and includes a complete methodology for knowledge decomposition, structured representation, and learner cognitive path construction.]

[Bonus: Fragments of Rosenblatt's personal research on memory—the entire development process of the Mark I perceptron.]

[Accept or not?]

Su Zhou clicked "Yes" without hesitation.

The next second, a torrent of information rushed into my mind.

Then he saw a dimly lit laboratory.

A massive, seemingly clumsy machine sits in the center of the laboratory, its metal frame crisscrossed with wires connecting hundreds of photoresistors.

That's the Mark I perceptron.

In 1957, the first machine in human history capable of "learning" was created.

Rosenblatt stood in front of the machine, his fingers constantly adjusting the parameters as he tried to get the machine to recognize simple geometric shapes, such as triangles and squares.

Every parameter adjustment is like groping in the dark. There is no experience to refer to, no ready-made framework to apply, and everything starts from scratch.

Connection weights, activation functions, error feedback...

These concepts, which would later be written into textbooks and used by countless engineers, were all newborns in this laboratory.

Su Zhou sensed Rosenblatt's almost obsessive excitement. When Mark I successfully distinguished between a triangle and a square for the first time, he wrote a line in his lab notebook:

"Today, machines have learned to see."

The memory fragments lasted for about a minute before fading away.

But what remained was deeply embedded in Su Zhou's cognitive structure.

He now understands neural networks.

From the mathematical model of the perceptron, the weight update rules, the limitations of single-layer networks to the theoretical possibilities of multi-layer networks, everything is as clear as formulas etched into my mind.

Of course, it takes time to fully digest all of this.

At the same time, that teaching skill is also taking effect.

He suddenly realized how to break down a complex concept into hierarchical cognitive modules, how to determine which level a learner is currently stuck at, and how to use the most precise language to push them from the current level to the next.

Su Zhou realized that teaching students knowledge is just like teaching machine learning.

This is the underlying logic of teaching that Rosenblatt extracted from his research on human neurocognitive mechanisms: the human brain itself is a neural network, and the essence of teaching is to adjust the connection patterns of this network.

Rosenblatt recalls that this research was once highly anticipated in the 1960s and 70s, but was ultimately shelved by a paper by Minsky because the single-layer perceptron could not solve the XOR problem, and the entire field remained dormant for nearly 20 years.

what about now?

Where has neural network research progressed in 2006?

Su Zhou opened his computer browser and entered "neural network" into the search engine.

There are papers, but not many.

In 2006, this field was still a niche within a niche in academia. The mainstream AI research focused on expert systems and statistical learning, while neural networks were considered a failed approach from the last century and were rarely explored.

But Su Zhou found a name on the second page of the search results.

Jeffrey Hinton.

A professor at the University of Toronto in Canada, who has long been dedicated to neural network research, published a paper on "deep belief networks" in 2006, proposing a layer-by-layer pre-training method and proving for the first time the trainability of deep neural networks.

The paper has a very low citation count so far. After reading the abstract, Su Zhou, who has a good command of English, felt a sudden jolt in his heart.

Because he had Rosenblatt's research memories in his mind, although he hadn't fully grasped them, he could roughly understand what the paper was about.

Hinton solved the core problem that Rosenblatt had failed to address.

This refers to the training method for multi-layer networks.

It's like laying a foundation; once the foundation is solid, you can build skyscrapers on top.

Skyscrapers, on the other hand, represent artificial intelligence.

True artificial intelligence that can learn and make decisions autonomously.

Su Zhou's breathing became rapid.

Unsure if his judgment was correct, he quickly switched back to group number 42 and decided to test the reactions of the geniuses in the group.

[Su Zhou: I've recently seen some new developments in neural networks, and there seems to be a breakthrough in training multi-layer networks. What are your thoughts on this direction?]

Rosenblatt was the first to respond.

[Rosenblatt: Are you also looking in this direction?]

[Su Zhou: I'm somewhat interested.]

[Rosenblatt: To my shame, my biggest regret in life is not being able to create a truly perfect neural network. Mark I was just a starting point, a very rough starting point. I proved that machines can learn, but I failed to prove that machines can learn deeply.]

[Rosenberg: Minsky is right that perceptrons can't solve the XOR problem; single-layer networks do have insurmountable limitations. However, his conclusion is too arbitrary!]

As soon as these words were spoken, the previously cheerful and joking atmosphere in the group disappeared.

[Turing: I share a similar regret. I proposed the concept of the Turing machine and proved the possibility of universal computing, but I didn't live to see a truly intelligent machine. My Turing test has yet to be passed, which shows that we are still far from our goal.]

[John von Neumann: I designed the basic architecture of modern computers, but the core idea of ​​this architecture is deterministic execution—the input is certain, so the output is certain. True intelligence, however, is precisely uncertain, fuzzy, and probabilistic. Alas, I wonder when we will reach that level.]

[Shannon: I quantified information, but I couldn't quantify intelligence. Information theory can tell you how many bits of information a message contains, but it can't tell you how many bits of wisdom a brain contains. I still can't see the end of that gap.]

Leibniz: 0 and 1 can encode all logic, but logic is not the same as wisdom. I used binary to describe the skeleton of reason, but I have never been able to touch the soul.

One message after another, the big shots in the group collectively revealed their deepest regrets, a rare occurrence.

They are pioneers in their respective fields; they are giants themselves.

Even so, when faced with the ultimate question of artificial intelligence, each of them only completed a small piece of the puzzle, and then left the game with unfinished regrets.

The neural network, the path that Rosenblatt pioneered but was later abruptly cut short by Minsky, is considered by the group to be the most likely direction to reach the end.

Just then, a system notification popped up in the group chat, in gold font and bold.

[System notification for Group 42: A common regret among most group members has been detected—"The construction and implementation of a perfect neural network."]

[If group member "Eagle Warrior" can promote and solve this core issue in real life, it will trigger the group consensus reward mechanism, at which time they will receive a high-level red envelope issued within the group.]

[Reward details: ? (Subject to dynamic adjustment based on the degree of problem resolution)]