Introduction: Thoughts on Four AI Waves

In the past two or three years, we can clearly feel that the world has accelerated: companies are restructuring positions, schools are adjusting courses, and even the living habits, expression methods, and creation methods of ordinary people are being redefined. This is not as simple as "technology getting faster," but a massive migration that happens once every few decades— We are entering the true "electrification moment" of the AI era. If the Internet changed "information flow," then this time AI is changing:

• Way of working
• Way of creating
• Way of thinking
• Way of organizing
• Even the relationship between humans and machines

First Wave: Symbolism (1950s–1980s)

• Core Representative Algorithms: This stage was centered on symbolism, with representative algorithms being first-order predicate logic and early perceptrons. The former simulated human reasoning processes by defining clear rules and logical symbols; the latter, as early neural network models, laid the foundation for the development of subsequent connectionism.

  

• Typical Applications: Applications in this period focused on basic logical tasks. For example, the dialogue system ELIZA born in 1966 simulated a psychotherapist discussing with humans through simple pattern matching rules; there were also logical reasoning programs capable of solving algebra and geometry reasoning problems, and early machine translation systems for translating short sentences in minor languages. However, limited by weak computing power and scarce data at the time, these applications could only handle simple scenarios and were difficult to cope with complex real-world tasks, subsequently entering the first "AI Winter".

  

Second Wave (1980 - 1987): The Prosperous Period of Expert Systems

• Core Representative Algorithms: The core algorithm was the production rule algorithm, while the Hidden Markov Model (HMM) began to emerge. The production rule algorithm could dismantle the knowledge of domain experts into a rule base of "condition-conclusion", supporting the system to simulate expert decision-making; HMM paved the way for subsequent statistical learning.

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• Typical Applications: The core application of this stage was expert systems. In the medical field, there was the MYCIN system, which could diagnose bacterial infection diseases and give medication suggestions based on patient symptoms and laboratory results; in the industrial field, there were expert systems for equipment fault diagnosis to help engineers quickly locate mechanical problems; in the financial field, expert systems assisting in credit risk assessment appeared. However, due to the high cost of maintaining rule bases, difficulty in dealing with cross-domain problems, and lack of generalization ability, this wave eventually fell into the second "AI Winter" due to difficulties in commercial application implementation.

  

Third Wave (1990s - 2012): The Rise of Statistical Machine Learning

• Core Representative Algorithms: A variety of classic machine learning algorithms blossomed, including Support Vector Machines (SVM), Decision Trees, Bayesian Networks, etc. At the same time, the optimization of the backpropagation algorithm brought neural networks back to attention, and the LeNet-5 convolutional neural network proposed in 1998 also became an early benchmark for image recognition. These algorithms broke through traditional rule dependence and could autonomously learn laws through data.

  

• Typical Applications: Application scenarios expanded towards practicality. The field of speech recognition achieved preliminary commercialization with the help of HMM algorithms, capable of recognizing simple commands for smart home appliance control; OCR optical character recognition technology matured, capable of converting text in paper documents into electronic text; in addition, spam recognition systems used Bayesian algorithms to filter spam, and handwritten digit recognition systems achieved bank check digit reading through LeNet-5, etc. However, algorithms in this stage overly relied on manual feature engineering, and performance was limited when dealing with complex tasks such as images and natural language.

  

Fourth Wave (2012 - Present): The Explosion of Deep Learning and Large Models

• Core Representative Algorithms: The early stage focused on Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Transformer architectures; the later stage saw large models become mainstream, relying on technologies such as pre-training - fine-tuning and Reinforcement Learning from Human Feedback (RLHF). Among them, CNN revolutionized image processing, and Transformer became the basic architecture of natural language processing, supporting the research and development of various large models.

  

• Typical Applications: Applications in this stage have fully penetrated all walks of life. In the image field, AlexNet significantly improved accuracy in image recognition competitions, promoting the implementation of applications such as security monitoring face detection and autonomous driving environment perception; in the natural language field, large models such as the GPT series, BERT, and Claude can achieve functions such as intelligent writing, machine translation, and intelligent customer service; in addition, AlphaGo defeated world Go champions with reinforcement learning, AlphaFold achieved protein three-dimensional structure prediction, and multimodal large models can also process multiple types of data such as text, images, and audio simultaneously, applied in emerging scenarios such as virtual anchors and intelligent creation.

  

Social Impact Brought by Generative AI

Looking at the context of AI technology evolution, for a long time before, discriminative AI always occupied a dominant position — whether it was early spam recognition, medical image diagnosis, or AlphaGo's Go game, the essence was specific tasks of completing classification and decision-making through feature mapping; while with the proposal of GANs in 2014, the birth of the Transformer architecture in 2017, and especially the explosion of large models such as ChatGPT in 2022, Generative AI has become the core of the current technological wave. From image creation to code generation, from new drug research and development to content creation, its "from nothing to something" creative ability is reshaping thousands of industries, promoting AI from "recognition and understanding" to comprehensively move towards a new stage of "generation and creation".

The social impact of discriminative AI is relatively limited because its core is to complete "classification and decision-making" within a preset framework. Application scenarios are mostly limited to auxiliary judgments in specific fields — whether it is spam recognition, face recognition, or disease diagnosis, the essence is the attribute definition of existing data, only replacing part of repetitive analysis work, not changing the core logic of production and creation, and rarely causing cross-domain ethical or social structure problems.

Generative AI has brought disruptive influence with its "from nothing to something" creative ability: it breaks through traditional task boundaries with probabilistic modeling. It can not only reconstruct education scenarios such as composition correction through tools like Wenxin large model, but also generate images, code, and even protein structures, deeply penetrating into thousands of industries such as creation, scientific research, and industry; but this powerful versatility also derives complex issues such as algorithm black boxes, misuse of false information, and copyright disputes, while promoting the reconstruction of employment structures and the widening of the digital divide. Its impact has extended from the technical level to the deep dimensions of social ethics, economic models, and even cultural identity.

1. Positive Impact: Brain Replacement Releases Innovation Potential

1. Democratization of Creation: Breaking Professional Barriers, Everyone Can Create AI's brain replacement has completely overturned the traditional creation model, allowing non-professionals to also possess the core capabilities of programmers, writers, and designers. With the help of AI coding assistants, zero-based individuals can quickly generate practical programs; through text-to-image tools, text can be converted into high-definition design drawings; creative materials that originally took a team weeks to complete can now be completed by a single person + AI within a day, and content production costs have dropped by more than 90%. This trend of "de-professionalization" makes creativity no longer the privilege of a few, realizing true creative equality.

2. "Less People" Operation: Streamlining Team Size, Focusing on Core Value Brain replacement has caused a qualitative change in team collaboration models. Brain-intensive work that originally required dozens of people to complete can now be efficiently achieved by a few people + AI. In NVIDIA's all-employee AI practice, software engineers significantly reduced repetitive development workload with the help of Cursor coding assistant; a marketing company's creative team, originally of 15 people, now only has 3 people responsible for strategic decision-making and detail optimization through AI undertaking core brain work such as scheme design and data analysis, and project delivery efficiency has actually increased by 40%. This transformation allows companies to break free from the shackles of manpower redundancy and concentrate resources on high-value creative decision-making links.

3. Efficiency Multiplier: Amplifying Brain Value, Accelerating Growth Curve As a brain extension tool, AI can multiply human cognitive and execution efficiency. Stanford University research shows that AI assistants increase the number of problems solved per hour by customer service by an average of 15%, and the efficiency of low-skilled employees soars by 30%. Novices can reach the business level of veterans in half a year within two months with the help of AI. In professional fields, lawyers use AI to quickly retrieve cases and generate legal documents, and researchers verify massive data and sort out research logic through AI, shortening brain labor that originally took days to hours. AI not only improves work output but also lowers the learning threshold, allowing humans to master complex skills faster and achieve rapid capability leaps.

   

2. Negative Impact: Intellectual Substitution Triggers Deep Social Challenges

4. Widening Digital Divide: Literacy Differences Exacerbate Class Differentiation AI brain replacement turns the gap between "can use AI" and "cannot use AI" into an insurmountable capability chasm. Urban-rural and educational differences lead to uneven distribution of AI literacy. Urban high-education groups continuously improve competitiveness with the help of AI, while rural areas and low-education groups are gradually marginalized due to lack of access channels and application capabilities. Data shows that the usage rate of AI tools in rural areas in China is less than 1/3 of that in cities, and the speed of improvement in AI application capabilities of low-education groups is only 1/5 of that of high-education groups. This gap is particularly obvious in the field of brain work. Practitioners who can use AI can efficiently complete core tasks such as creativity and analysis, while non-users gradually lose competitiveness in the job market, further widening the social gap.

5. Changes in Interpersonal Relationships: Emotional Connection Faces Risk of Alienation The transformation of work and lifestyle brought about by brain replacement is reshaping the core form of interpersonal relationships. AI companionship services are rising. The user retention rate of Japan's Gatebox holographic partner system reached 82%, with an average daily interaction time of over 3 hours, but Stanford research shows that for users who use virtual partners for more than 200 hours continuously, their willingness to socialize in reality drops by 41%. At work, AI becomes the main collaboration object, human interaction decreases, and the emotional connection originally established through collaboration gradually weakens; in life, some people rely on AI's "non-judgmental companionship" to escape interpersonal conflicts in reality, leading to the degeneration of real communication skills. This human-machine emotional dependence is causing humans to fall into the predicament of "seemingly connected but actually lonely".

6. Restructuring of Unemployment Structure: Intellectual Positions Face Substitution Impact Different from traditional repetitive work substitution, AI's substitution range for brain positions is wider and the impact is deeper. World Economic Forum surveys show that 40% of companies plan to cut brain positions that can be automated by AI between 2025 and 2030. Goldman Sachs reports point out that Generative AI may put 300 million full-time jobs globally at risk. Brain work such as programming, content creation, and junior design bears the brunt. Musk predicts that such work will be replaced on a large scale within 1-2 years. The unemployment problem is no longer limited to low-skilled groups. Middle and high-educated practitioners also face pressure for position reconstruction. Some industries see a situation where "layoff waves" and "skill renewal waves" coexist. The structural change in the job market has quietly arrived.

   

Purpose of This Tutorial

This tutorial is first an entrance to "embrace AI and fill in the common sense of the times". It will not assume that everyone has mastered profound mathematics, nor will it only talk about flashy Demos, but hopes to help you establish a set of new "technological literacy" facing the present: knowing what large models, large language models, and agents are, what they can do, and what they cannot do; knowing the rough map of the current mainstream technology stack, rather than being scared off by various nouns. You can treat this course as a systematic but not obscure literacy campaign: keep up with the rhythm of the times, and upgrade from "having heard a few AI concepts" to "a person who really knows how to use, dares to use, and dares to chat about AI with others".

Secondly, starting from Dify is because compared to gnawing on various papers and frameworks right away, starting from a platform that is "visible, clickable, and capable of producing results" is more suitable for most non-full-time engineers. As a currently very popular agent orchestration and application building platform, Dify can build dialogue robots and workflows in a visual way, and retains enough expansion space for you to transition to API, code, and self-built services at any time. By "first using Dify to build a runnable application, then reversely dismantling the underlying technology and principles", you will intuitively understand: what modules a large model application is composed of, and how agents, tool calls, memory, and knowledge bases work together in actual products.

Finally, this tutorial is not just a "long-winded repetition of official documents", but some personal thoughts and working methods after I stepped on pits, tried and erred, and reviewed in actual combat. I will tell you: in real projects, which functions are worth doing early, and which are "good-looking but unnecessary"; what are the common misunderstandings, hallucinations, and true landing paths for enterprises when introducing AI; how individuals can turn AI into their "second brain" in this wave, rather than a new source of anxiety. I hope that after you finish learning, you will not only have a set of skill toolkits, but also form a set of your own judgment standards: knowing when to use AI, how to use it, and where human value and choice are still needed.