Riding the wind of rebirth

Chapter 1865 Granddaughter

Even though Zhou Zhi thought that Mai Mingdong might give a very nonsensical answer, he couldn't imagine it would be like this, and he couldn't help but stand there in a daze.

"What? Can you first tell me what is meant by a dry sled?"

"Oh," Zhou Zhi then quickly explained to Mai Mingdong the principle of this system attack method.

Mai Mingdong's first reaction after hearing this was: "Shouldn't this be a research topic for your Clover System Antivirus Department? Do you think I would work in this field?"

There is nothing wrong with what he said. The projects that Mai Mingdong is responsible for are all large-scale national projects, mainly focusing on applications. They would never take this kind of work of plugging rat holes seriously.

Zhou Zhi said a little embarrassedly: "When I was looking up information, I found your name. I also found it incredible at the time, so I called you. But the article on Yizhi.com is indeed signed by you."

"Wait a minute, I'll check it online..." Mai Mingdong answered, and then Zhou Zhi heard the keyboard clacking, and then heard Mai Mingdong's surprised voice: "Huh? There really is an article, it's really strange..."

"Can you recall it again?" Zhou Zhi asked, "Do you think I didn't lie to you?"

"Hey! It must be Xiao Miao playing a prank!" Mai Mingdong said happily: "This little girl is too naughty, I will punish her when I come back!"

"Mr. Mai, this Xiao Miao you are talking about, she is..."

"My granddaughter! She just came back from Berkeley. She was a student of Cai Shaotang before. You know Cai Shaotang, right?"

AXA Information's Industry Intelligence Department has established files on Chinese people who have made achievements in industries around the world, and Zhou Zhi certainly knows this big shot.

However, he was not from China, but was born in a Chinese family in the Philippines. After obtaining a bachelor's degree in electrical engineering from the Mapúa Institute of Technology in the Philippines in 1971, he went to the United States to study, and successively obtained a master's and doctoral degree from the Massachusetts Institute of Technology and the University of Illinois at Urbana-Champaign. He then taught at Purdue University and joined the University of California, Berkeley as a professor of electrical engineering and computer science in . He is also a foreign academician of the European Academy of Sciences and the Hungarian Academy of Sciences.

In the same year, he proposed the memristor theory.

Memristor is a passive circuit element related to magnetic flux and charge. It is considered to be the fourth basic circuit element after resistors, capacitors and inductors. It has very important application prospects in information storage, logical operations, neuromorphic computing and nonlinear circuits.

The proposal and implementation of this concept brought fundamental changes to traditional circuit theory. On this basis, Cai Shaotang proposed theories such as Cai's circuit and cellular neural network.

The Chua circuit is a simple nonlinear electronic circuit design that exhibits standard chaos theory behavior. It was first published by Shao-Tang Chua in 1983 while he was a visiting scholar at Waseda University in Japan.

The circuit is so easy to make that it is an example of the ubiquity of real-world chaotic systems, leading some researchers to declare it a "paradigm of chaotic systems."

The cellular neural network, abbreviated as CNN, is even more amazing.

Humans have gone through many detours in the development of neural network models, from the earliest nerve cell model to the neural network model to the exploration of the three-layer structure of the perceptron, which eventually led to a wave of text recognition, image recognition, and sound recognition in the 1960s. However, soon, due to the overly simple model at the time, the perceptron had huge limitations in both principle and function. It was not until Minsky and others at MIT pointed out after research that the perceptron under the existing mechanism could not recognize linear inseparable patterns at all, and even simple puzzles could not be solved.

This research result directly led to a significant decline in the research enthusiasm for perceptrons.

However, in the theoretical field, people have never stopped analyzing the models of nonlinear chaotic states, and ideas and viewpoints such as "learning matrix" and "quasi-neuron" continue to emerge.

This theory finally achieved a great breakthrough in the 1980s. New methods such as "fully interconnected artificial neural network", "simulated annealing" methodology, "microstructure theory of cognitive process", "back propagation learning algorithm error correction", and "adaptive resonance theory" began to appear, and successfully proved that the nonlinear perception problems, complex pattern recognition problems, adaptive characteristics problems, and nonlinear system optimization problems that had plagued people before can be completely solved through neural network theory.

Based on these achievements, Cai Shaotang proposed a method of circuit theory design and hardware implementation through his own research, namely the cellular neural network model.

This is a signal nonlinear analog processor with local interconnection and dual-value output. It has the characteristics of continuous real-time, high-speed parallel computing, and is suitable for implementation in ultra-large-scale integrated circuits.

Unlike biological neurons, the connection between CNN cell neurons is mainly controlled by weight templates. Different templates exhibit different nonlinear characteristics. Memristors with memory characteristics can be applied to the functional connection points between neurons, thereby simulating brain cell neuron networks, achieving simulation simplification of information processing mechanisms, and realizing functions such as logical operations and image processing.

This research result directly opens the door for humans to apply artificial intelligence to many fields in the future, such as biomedicine, image processing, automatic control, pattern recognition, signal processing, and secure communications. Emerging technologies such as big data and blockchain decades later are also closely related to it.

Although this technology represents the direction of future development, it is actually a bit too advanced. Currently, it is still basically undergoing laboratory research and there are not many problems that can actually be solved by it.

There is only one place in China that can provide such research, and that is the graph database that Zhouzhi adopted in its digital library against all odds.

There are also application scenarios available for practice in aspects such as root character recognition, graphic recognition, oracle bone conjugation, and chaos super search.

The advantage of graph databases is their powerful functionality.

The current mainstream traditional relational databases require strict data normalization during design, dividing data into different tables and deleting duplicate data. This normalization ensures strong consistency of the data and only after placing huge restrictions on data relationships can rapid row-by-row access be achieved.

However, problems arise when complex connections are formed between data and cross-table associated queries increase to the point where the strong constraints become unbearable.

Although complex queries can be performed by associating different attributes in different tables, the overhead increases exponentially. In the words of programmers, the system is "suffocated" by the huge amount of data associations.

Graph databases do not have this problem. Although their data relationships are also mapped to the data structure, their special organizational structure and network analysis functions make them, unlike traditional relational databases, faster in query speed for data sets with higher and more complex correlations and larger data volumes, making them particularly suitable for object-oriented applications.

At the same time, graph databases can be more naturally extended to big data application scenarios, because graph database construction is not constrained by the strong consistency of the table structure and can be more flexible, so it is more suitable for managing temporary or constantly changing data.

As a time traveler, Zhou Zhi certainly knows what the future will be like, and also knows the significance of winning at the starting line. (End of this chapter)

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