Understanding “Dynamic Topic Models”: Tracking the “Trend Changes” of the Information World

In the era of information explosion, we are surrounded by massive amounts of text data every day, from news reports to social media, from academic papers to corporate financial reports. How to extract valuable information from this vast ocean of text and understand its deep meaning has become an important topic in the field of artificial intelligence. Among them, Topic Models are a powerful tool, and “Dynamic Topic Models” act as if they endow this information with a dimension of time, allowing us to gain insight into the evolution of “trends”.

What is a Topic Model? Starting from “Organizing a Bookshelf”

Imagine you have a huge bookshelf at home, piled with various types of books, scattered everywhere and very messy. If you want to know which books are about “history” and which are about “science fiction”, you need to flip through them one by one.

Traditional Static Topic Models, such as the most famous LDA (Latent Dirichlet Allocation), are like an intelligent librarian with “keen eyes”. It doesn’t need you to tell it the category of the book in advance. Instead, by analyzing the words that appear in each book (for example, “history books” often contain “dynasty”, “war”, “emperor”; “sci-fi books” often contain “universe”, “robot”, “future”), it can automatically help you organize these books into different “topic piles”—for example, a pile of “history theme”, a pile of “sci-fi theme”, a pile of “cooking theme”, etc. When a new book arrives, it can also determine which topic or mixture of topics it belongs to.

These “topic piles” are not manually defined by us, but are abstract concepts “learned” by the model from the text. Each topic is composed of a group of closely related words combined with different probabilities. In this way, topic models can help us understand the latent structure of a large number of documents, facilitating text organization and summarization.

The Charm of “Dynamic”: A Journey of Information Evolution Through Time and Space

Although static topic models are powerful, they have a limitation: they assume that these “topics” are fixed and unchanging, just like books on your shelf once categorized remain in that category forever, and the word composition of each topic does not change. However, real-world information is constantly evolving. For example, the focus of the concept of “science” 100 years ago is vastly different from today.

This is where Dynamic Topic Models (DTMs) come into play. As the name suggests, it adds a time dimension to topic models, capable of capturing how topics evolve over time.

We can imagine the Dynamic Topic Model as a library curator who is both a “historian and trend analyst”. He can not only organize books for each time period (e.g., every year) like a static model, but more importantly, he can observe and record the “growth history” of each topic in different time periods:

1. Vocabulary Evolution: For instance, in the early 20th century, the topic of “communication” might contain words like “telegraph” and “telephone”; by the 21st century, the “communication” topic would feature words like “Internet”, “5G”, and “smartphone”. Dynamic Topic Models track the process of these words joining, exiting, or changing in importance over time. It assumes that documents in each time slice (e.g., a year) come from a set of topics that evolved from the topics of the previous time slice.
2. Rise and Fall of Popularity: Certain topics may be very popular during specific periods, while gradually fading into silence in others. For example, discussions on “steam engines” were hot spots during the Industrial Revolution, but are relatively niche today; interest in “Artificial Intelligence” has shown explosive growth in recent years. Dynamic Topic Models can reveal the ups and downs of these topic heats.

Simply put, if we view a whole year’s news reports as a time slice, the Dynamic Topic Model can analyze the topics in this time slice, and then associate these topics with the topics of the previous year, observing how they are “inherited” and “developed”. This model effectively analyzes the process of topic evolution over time in large document collections by using state space models on the natural parameters of the multinomial distributions representing the topics.

Application Scenarios of Dynamic Topic Models

Dynamic Topic Models are not just theoretical innovations; they also show great value in practical applications:

• Tracking Scientific Development Trends: Analyzing academic papers spanning decades can reveal the rise, fall, and vocabulary evolution of different topics within a research field (such as physics, biology). For example, it has been used to analyze articles in the journal Science published between 1881 and 1999 to show changes in word usage trends.
• Social Public Opinion and Cultural Change: By analyzing years of news reports, social media posts, blog articles, etc., Dynamic Topic Models can help us understand how the focus of public opinion shifts, the changes in social trends, and the evolution of cultural hot spots.
• Business and Market Analysis: It can be used to analyze consumer reviews and market reports to identify product trends and changes in consumer preferences, and can even help predict the direction of financial markets. For example, analyzing text related to innovation, stock returns, and industry identification.
• Policy Evolution Research: By tracking topics in policy documents, one can understand changes in government focus, adjustments in policy tools, and their impact on society. For example, studies have used it to explore the evolution patterns of food safety governance policy topics.
• Political Communication Analysis: Dynamic Topic Models can be used to analyze how the narratives of policymakers evolve during conflicts, helping to understand the strategies and effects of political communication.

Latest Progress and Outlook

Early Dynamic Topic Models were mostly based on traditional statistical methods, such as D-LDA, an extension of Latent Dirichlet Allocation (LDA). In recent years, with the development of deep learning technology, researchers have also begun to explore Dynamic Topic Models combined with neural networks (such as D-ETM), integrating word embeddings and Recurrent Neural Networks (RNNs) to better capture the dynamics of topics.

Although Dynamic Topic Models perform well in understanding time-series text data, evaluating the performance of such models remains a challenge because they are essentially unsupervised, and the development of evaluation metrics has not yet fully kept pace with new models. Future research will continue to be dedicated to developing more efficient and accurate Dynamic Topic Models and unleashing their unique value in more fields.

In summary, the Dynamic Topic Model is like a magical “time machine” that takes us through the long river of information. It allows us not only to see the current information structure but also to clear the fog of time, providing valuable clues for us to understand and predict the future by gaining insight into the trend changes of the information world.

Dynamic Causal Modeling

Dynamic Causal Modeling (DCM) is a powerful computational modeling technique that originated in the field of neuroscience. It is used to investigate how various components within a complex system influence each other, and especially how this influence changes dynamically over time. Although DCM is primarily applied in neuroscience—for example, to analyze effective connectivity between brain regions—its core idea of understanding dynamic causal relationships holds significant inspiration and potential application value for “Causal AI” and “Explainable AI,” which pursue deeper understanding and decision-making capabilities.

What is “Modeling”? — Drawing a Simplified Map of the World

Imagine you are preparing to travel to a strange place; you would need a map. This map won’t include every tree or stone on the road, but it will show important roads, landmarks, and connections to help you understand how to get from point A to point B.
“Modeling” in science and technology does something similar. We create a simplified mathematical description, or “model,” for a real-world system of interest, such as the brain, an economic market, or a complex AI program. This model captures the key features and operating laws of the system, allowing us to better understand, analyze, and predict its behavior.

What is “Causal”? — Finding the “Real Reason”

We often encounter issues of “correlation” versus “causality” in life. For example, sales of ice cream and the number of drowning incidents both increase in the summer; they are correlated. But does ice cream cause drowning? Obviously not; they are both caused by the same underlying factor (hot weather).
“Causal” refers to when one event (the cause) directly leads to the occurrence of another event (the effect). Distinguishing true causal relationships is crucial. Traditional AI models can often only discover “correlations” between data but cannot identify “causality.” For instance, an AI model might find that “users who frequently click on ads are more likely to buy goods,” but it doesn’t necessarily know if the ad “caused” the purchase, or if these people were already users with “high purchase intent” who just happened to click the ad. One of the goals of Dynamic Causal Modeling is to go beyond mere correlation and reveal deeper causal mechanisms.

What is “Dynamic”? — Understanding Interactions that Change Over Time

The world is constantly changing. The weather varies from morning to noon to evening; human moods fluctuate. This state and behavior evolving over time is what we call “dynamic.”
In “Dynamic Causal Modeling,” “dynamic” means that we not only want to identify that event A causes event B, but we also want to understand how this causal relationship changes over time, and how the intensity and manner of event A’s influence on event B differ at different points in time. For example, when different regions of the brain process information, the interactions between them are rapidly changing rather than static.

The “True Face” of Dynamic Causal Modeling (DCM)

Combining the three concepts above, Dynamic Causal Modeling (DCM) can be understood as: A technique that builds mathematical models to describe how parts of a complex system exert causal influence on each other dynamically and reciprocally over time.

A Daily Life Example:

Imagine you and your friend Xiao Ming are playing a video game together.

1. Modeling: We can build a simplified model for you and Xiao Ming’s gaming behavior and emotional states (e.g., excitement, frustration).
2. Causal: When your spirits are high, your gameplay might be more aggressive, which might “cause” Xiao Ming to become more excited as well; conversely, a mistake by Xiao Ming might “cause” you to feel frustration. DCM aims to identify these causal chains of who influences whom.
3. Dynamic: This influence is not instantaneous. Your excitement might take a few seconds to transmit to Xiao Ming, and the speed and intensity of this emotional transmission might differ at different stages of the game (opening, mid-game, final showdown). DCM captures these time-varying causal relationships.

DCM typically uses a method called “Bayesian inference,” combining our existing knowledge (prior knowledge) with actually observed data to estimate the various parameters in the model (e.g., the intensity of your influence on Xiao Ming, the intensity of Xiao Ming’s influence on you) and to select the model that best explains the data.

The Significance and Bridging of DCM in the AI Field

Although DCM is primarily used in neuroscience to understand brain functional networks—such as analyzing how the brain processes information in cognitive neuroscience or researching the neural mechanisms of mental illnesses—its core idea of discovering dynamic, time-varying causal relationships from data is highly aligned with several important development directions in the current AI field:

1. Explainable AI (XAI): Traditional deep learning models are often “black boxes”; we know they can make accurate predictions, but it is hard to understand why they make them. Models like DCM that emphasize causal explanation can provide a deeper understanding, helping AI systems not only give answers but also explain the causal logic behind their decisions. This is a key step towards achieving “Trustworthy AI.”
2. Causal AI: This is an emerging direction in AI aiming to let systems go beyond simple correlations and truly understand the causal relationships between things. For example, while Generative AI can create content, it often lacks an understanding of the underlying causality, leading to results that may lack logical reasoning. DCM provides a theoretical framework and method for causal inference in dynamic systems. By combining DCM’s causal modeling capabilities with machine learning, there is hope to improve the generalization ability of AI models in complex environments, allowing them to better adapt to new situations.
3. Embodied Intelligence and World Models: Embodied intelligent robots need to understand the complex physical world and the causal feedback resulting from their actions to make better decisions. The goal of a World Model is to let AI understand the operating laws of the world. The dynamic causal modeling capability provided by DCM helps to build more rigorous world models that contain causal logic and temporal evolution, ensuring that robots can understand the causal effects their actions produce on the environment over time.
4. Reinforcement Learning: In Reinforcement Learning, an agent learns the best strategy by interacting with the environment. Traditional reinforcement learning often only learns the total effect of actions on results, without necessarily understanding the deeper causal mechanisms. Causal Reinforcement Learning (Causal RL), which introduces causal modeling, is emerging to help agents better understand causal relationships in the environment, thereby enabling wiser decisions and improving the generalization and interpretability of algorithms.

Latest Progress and Outlook

Despite being primarily a neuroscience tool, the AI field is actively absorbing causal inference ideas under the “Causal Revolution” wave. Recent research shows that DCM methodologies can be combined with machine learning and data analysis techniques to optimize model selection and parameter estimation. For example, machine learning methods are being used to optimize the complex computational processes of DCM, making it more efficient when dealing with large-scale, high-dimensional data.

In the future, DCM, as a powerful tool stemming from neuroscience, is expected to play a more important role in the AI field. It will help us build intelligent systems that can not only predict but also understand “why” and “how they influence,” thereby driving AI from “imitation” to “understanding,” and finally achieving more trustworthy and intelligent Artificial Intelligence.

AI 101: Demystifying “Feedforward Networks” — The “Thinking Assembly Line” of Artificial Intelligence

Have you ever wondered how AI “understands” your intent when you ask a voice assistant a question on your phone, or how it identifies objects in a photo when one is uploaded online? In the vast world of artificial intelligence, there are many sophisticated “brain structures,” and one of the most fundamental and important members among them is what we are going to introduce in simple terms today — the Feedforward Network.

Imagine you are assembling a complex piece of furniture. You follow the steps in the manual, completing them one by one. Each step is based on the result of the previous one, and you never go back to modify the parts that are already done. This is the core characteristic of a “Feedforward Network”: Information flows like water, only in a single direction, traveling from the input end to the output end, never “flowing upstream” or forming cycles.

1. What is a Feedforward Network? — An Efficient “Information Processing Assembly Line”

The Feedforward Network, often referred to as a “Feedforward Neural Network” or “Multilayer Perceptron (MLP),” is one of the most basic and commonly used neural network models in the field of artificial intelligence (especially deep learning). It is called “feedforward” precisely because the information processing flow within it is strictly unidirectional, with no feedback or recurrent connections.

We can liken a feedforward network to an efficient “information processing assembly line”:

• Raw Material Input (Input Layer): Just like the raw material entrance of a factory, data (such as all pixel values of an image, or the encoding of a text segment) is “fed” into the network from here.
• Multiple Processing Stages (Hidden Layers): After raw materials enter the workshop, they pass through one processing stage after another. Each stage (i.e., the “Hidden Layer” in the network) performs a “processing transformation” on the information. This “transformation” is progressive; the result processed by the previous layer is immediately sent to the next layer for further processing.
• Product Output (Output Layer): When information has passed through all processing stages, it finally exits from the end of the assembly line to form the “finished product” — this is the output of the network. For example, identifying whether the image contains a “cat” or a “dog,” or predicting whether tomorrow’s stock price will rise or fall.

In this process, information only moves forward and does not backtrack. This is different from the complex thinking process in our brains, but it is precisely this simple and efficient structure that makes feedforward networks perform excellently in many tasks.

2. “Intelligent Workers” and “Operating Standards” on the Assembly Line

On this “thinking assembly line,” there are several key components that work together to complete the processing of information:

2.1 Neurons: “Intelligent Workers” on the Assembly Line

The core of a feedforward network is the Neuron, which is the basic unit of information processing. You can imagine each neuron as an “intelligent worker” on the assembly line. They are responsible for receiving information from the previous stage (neurons in the previous layer), performing calculations, and then passing the result to the next stage.

2.2 Connections and Weights: “Information Transmission Pipelines” and “Importance Tags” between Workers

There are “connections” between every neuron, much like the conveyor belts connecting various workstations in a factory. These connections are not all treated equally; each carries a Weight. A weight can be understood as an “importance tag” for information transmission. If the weight of a connection is large, the information passing through this connection will be “amplified” and become more important; otherwise, it will be “attenuated.” The network “learns” and recognizes patterns by adjusting these weights.

2.3 Bias: The “Baseline” or “Preference” of Workers

In addition to weights, each neuron also has a Bias. Bias can be seen as the worker’s “baseline” or “default preference” for processing information. Even without any input, the worker will have a basic “tendency.” With bias, neurons can be “activated” even when receiving weaker signals, thereby increasing the flexibility of the network.

2.4 Activation Functions: The “Decision Rules” of Workers

When an “intelligent worker” (neuron) receives all weighted input information and adds the bias, it does not directly pass this result on. Instead, it processes it through a “decision rule” known as an Activation Function. This function determines what information the neuron ultimately passes to the next layer. It introduces non-linear factors, allowing the network to learn and process more complex, non-linear patterns rather than simple linear relationships. Common activation functions include ReLU (Rectified Linear Unit), Sigmoid, etc.

3. How do Feedforward Networks “Learn”? — The “Training Process” of Continuous Improvement

The reason a feedforward network is intelligent is that it can “learn.” Its learning process is like a factory constantly improving its production process.

Initially, the network’s weights and biases are set randomly, just like a newly built assembly line where workers might not be skilled yet, and the quality of produced products varies.
After the network processes a batch of data and produces a “result” (output), it compares this result with the “correct answer” (ground truth) to find “errors” or “gaps.”
Then, based on this error, the network uses an algorithm called Backpropagation. Like a smart chief engineer, it fine-tunes the “weights” and “biases” on each worker layer by layer, moving against the direction of the information flow. The goal of this adjustment is to make the “product” produced next time closer to the “correct answer.”

This process is repeated countless times. With each iteration, the network becomes “smarter,” and its ability to process information grows stronger, eventually enabling it to accurately recognize patterns and make predictions.

4. Applications of Feedforward Networks: The Ubiquitous “Unsung Heroes”

Due to its simple structure and ease of understanding and implementation, the feedforward network is the foundation of many complex AI models and has a wide range of applications in the field of artificial intelligence.

• Image Recognition: Distinguishing whether objects in a picture are people, animals, or scenery.
• Natural Language Processing: Used in the early stages or sub-modules of tasks such as text classification, sentiment analysis, and machine translation.
• Classification and Regression: Predicting stock prices, weather changes, or categorizing emails as “spam” or “non-spam,” etc.

Although more specialized networks like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) perform better in image and sequence data processing respectively, feedforward networks remain their foundation and possess unique advantages when processing static data and performing classification and regression tasks.

Conclusion

The Feedforward Network, this seemingly simple “thinking assembly line,” is an important starting point in the world of artificial intelligence. With its clear unidirectional information flow and iterative learning mechanism, it has laid the cornerstone for various amazing applications of AI. Understanding it allows us to better comprehend those more complex and “smarter” algorithms in the AI world and feel the infinite possibilities that technology brings us.

AI Concepts Explained: Prefix Tuning — The Lightweight Magic That Makes Large Models “Get It” Instantly

In the rapid development of artificial intelligence today, we are seeing more and more powerful AI models emerging around us, especially those “Large Language Models” (LLMs) capable of natural language understanding and generation, such as ChatGPT and ERNIE Bot. They seem to possess encyclopedic knowledge and fluent expression capabilities. However, while these giants are powerful, they also bring a tricky problem: What if I want this generalist model to specifically learn a particular skill, such as writing marketing copy, or just answering professional questions in a specific field? Traditional methods often require consuming huge resources to “reshape” the entire model. The “Prefix Tuning” we are introducing today is an ingenious way to solve this problem.

I. The Dilemma of Large Models: Master of Many, Specialist of None

Imagine a large model is like a university professor who is well-read and knowledgeable. He knows almost everything and can talk about philosophy, history, and science. Now, you want this professor to help write a promotional draft for a “local community event”. Although he has the ability to write it, you might need to guide him repeatedly, or even adjust his writing style and content focus according to a specific writing guide.

In the AI field, this “adjustment” process is called “Fine-tuning”. Traditional fine-tuning methods are like sending this professor to a specialized “Community Event Promotion Academy”, making him relearn all subject knowledge and modify his thinking patterns and expression habits according to the academy’s requirements, in order to write promotional drafts better. While this is effective, the problems are:

1. Huge Resource Consumption: Updating the professor’s entire knowledge system and way of thinking not only takes time and effort but also requires “super brain” level computing resources.
2. Cost of “Just for One Thing”: Learning a new task, such as writing poetry or coding, might require such a large-scale “transformation” each time, which is undoubtedly inefficient.
3. Risk of Knowledge Forgetting: Focusing on new skills might lead the professor to be less flexible and comprehensive than before when dealing with other general tasks.
4. Model Privacy Issues: Model providers may not want users to directly modify the core knowledge (parameters) inside the model, which limits the application of traditional fine-tuning.

II. Prefix Tuning: Cleverly Using “Manuals” Without Touching “Textbooks”

Prefix Tuning was born to solve the above problems as a “lightweight fine-tuning” technology. Its core idea is: Do not modify the internal knowledge (parameters) of the large model, but quietly give it a “task manual” before inputting instructions to the model each time. This manual will guide the model to better understand and complete the current task.

Let’s use a few vivid metaphors to understand it:

Metaphor 1: The Chef’s “Custom Seasoning Packet”

A large language model is like a skilled five-star chef who has mastered the cooking methods and ingredient combinations of countless dishes (pre-trained model). Now, you want him to cook “Spicy Diced Chicken”, but hope this dish fits your personal taste of “more numbing, less spicy”.

• Traditional Fine-tuning: Equivalent to asking the chef to relearn all Sichuan cuisine cooking techniques from scratch, completely adjusting the recipes and production processes of all dishes according to your taste preferences. This is obviously unrealistic.
• Prefix Tuning: You don’t need to transform the chef, nor do you need to change any recipe in his mind. You just need to hand him a special “numbing and spicy seasoning packet” (prefix) made specifically for you every time you order “Spicy Diced Chicken”. When cooking, the chef processes this unique “seasoning packet” together with the main ingredients, which will cleverly guide him to make the final Spicy Diced Chicken product have your favorite “more numbing, less spicy” flavor, while other dishes (other knowledge in the large model) remain undamaged.

This “seasoning packet” is the trainable “Prefix” in Prefix Tuning. It is not natural language, but a string of special “instruction vectors” or “virtual tokens” that can be understood by the model. During training, we only adjust the recipe of this “seasoning packet” so that it can “guide” the large model to complete specific tasks, while the core parameters of the large model itself remain unchanged.

Metaphor 2: The Actor’s “Role Cue Card”

A large language model is like an experienced actor who has played countless roles and mastered various acting skills and lines (pre-trained model). Now, you need him to play a specific role, such as a “calm detective”.

• Traditional Fine-tuning: It is like letting the actor learn to perform the detective role from scratch, even modifying his past acting habits and experience, consuming a lot of time and energy.
• Prefix Tuning: The actor’s acting skills and experience (core capabilities of the large model) remain unchanged. But every time before he goes on stage, you give him a “role cue card” (prefix) full of keywords like “calm, composed, sharp eyes”, and then let him enter the role according to this card. This card will subtly affect his performance, making him more like the “calm detective” you want, without affecting his ability to play other roles.

These “role cue cards” exist in the form of a series of continuous, learnable vectors in AI models. They are “pre-added” to the model’s input sequence or deeper attention mechanisms, just like inputting a special “previous summary” or “psychological suggestion” to the model, thereby guiding the model to produce output that is more in line with expectations on specific tasks.

III. The Unique Charms (Advantages) of Prefix Tuning

As a Parameter-Efficient Fine-Tuning (PEFT) method, Prefix Tuning has several significant advantages:

1. Saves Computing Resources: Only a small portion of “prefix” parameters need to be trained and stored (usually only 0.1% or less of the total model parameters), greatly reducing the demand for computing resources (GPU memory).
2. Fast Training Speed: Since there are very few parameters to optimize, the training process becomes very rapid, allowing large models to adapt to various new tasks at a lower cost.
3. Avoids Catastrophic Forgetting: Since the parameters of the main model are frozen and remain unchanged, the situation of “forgetting” old knowledge in order to learn new skills will not occur, and the general capabilities of the model are preserved.
4. Adapts to Private Models: Even for closed-source large models whose internal parameters cannot be accessed, as long as an input interface is provided, theoretically, personalized guidance can also be carried out by adding “prefixes” externally.
5. Saves Storage Space: For each new task, only the corresponding “prefix” parameters need to be stored, not a copy of the entire model, which can significantly save storage space when facing a large number of downstream tasks.
6. Excellent Performance in Low-Resource Scenarios: In cases where the amount of data is small or resources are limited, Prefix Tuning can often show better results than traditional fine-tuning.

IV. Latest Progress and Applications

Prefix Tuning was first proposed by Li and Liang in 2021, mainly applied to Natural Language Generation (NLG) tasks, such as text summarization and table-to-text generation. It belongs to a type of generalized “Prompt Tuning”, aimed at guiding model behavior by optimizing input prompts.

In recent years, as large models have become larger and larger, Parameter-Efficient Fine-Tuning (PEFT) methods have become mainstream. In addition to Prefix Tuning, there are technologies like Adapter Tuning and LoRA (Low-Rank Adaptation). These technologies have their own characteristics and complement each other. Although some research shows that LoRA may perform better on some very large or complex models, Prefix Tuning and its variants (such as Prefix-Tuning+, which attempts to address limitations in the original mechanism) remain important research directions.

V. Conclusion

Prefix Tuning is like a “smart auxiliary” tailored for AI large models. It brings huge flexibility and efficiency improvements with minimal changes. It makes the almighty AI model no longer a “black box”, but an intelligent assistant that can be cleverly guided and quickly adapted to various specific needs. In the future, with the deepening application of AI technology in various industries, lightweight and high-efficiency fine-tuning technologies like Prefix Tuning will undoubtedly play an increasingly important role in unleashing the potential of large models and promoting the popularization of AI. It allows ordinary users to use and customize powerful AI capabilities with a lower threshold, truly realizing the vision of AI that “clicks instantly” and serves thousands of industries.

In the field of Artificial Intelligence (AI), especially with the rapid development of Large Language Models (LLMs), there is a concept that seems simple but is crucial — Tokenization. It acts like a bridge connecting human language and machine understanding. Imagine when we humans communicate, our brains naturally decompose a sentence into meaningful words or concepts to understand it. However, for computers that do not understand human language, they need a set of rules to complete this “decomposition” process. Tokenization is exactly this task.

1. Tokenization: The “Lego Bricks” of Language

What is Tokenization?

Simply put, tokenization is the process of splitting a continuous text sequence into independent, meaningful units, which we call “tokens”. Just like building a Lego model, we cannot directly use a large block of plastic, but need pre-designed bricks (tokens) to assemble it. These bricks can be individual characters, common words, or even parts of words.

Why does AI need Tokenization?

Computers do not directly understand text itself; they only understand numbers. To enable AI models to process and learn from text data, we need to convert text into numerical representations that the model can recognize. Tokenization is the first step in this conversion process, determining what the basic units of language the model “sees” are. Each token after tokenization is assigned a unique ID, and these IDs are then mapped into numerical vectors that the model can process.

Without tokenization, an AI model is like a child who doesn’t understand words, facing a long string of uninterrupted letters, with no way to start. Only by cutting the text into meaningful “bricks” can the model build an understanding of language.

2. Different Types of “Lego Bricks”: The Evolution of Tokenization Methods

Tokenization methods vary, just like Lego bricks come in various shapes and sizes, each with its own use.

2.1 Character-level Tokenization: The Smallest “Beads”

Idea: Treat each independent character as a token.
Metaphor: Like separating every bead on a necklace.
Pros: High flexibility, no “Out-of-Vocabulary” (OOV) problem, because any text can be decomposed into a known character set.
Cons: Results in the model’s context window being stretched very long, as a word might need a dozen characters to represent, making it difficult for the model to learn high-level semantic information.

2.2 Word-level Tokenization: Common “Word” Bricks

Idea: Split text according to words (via spaces or a dictionary).
Metaphor: Like a puzzle book designed for children, where every word has been pre-cut.
Pros: Easy to understand and implement, especially for languages like English where words are separated by spaces.
Cons:

• New Word Problem (OOV): If it encounters a new word, internet slang, or technical term not in the dictionary, the model cannot recognize it.
• Chinese Tokenization Challenge: Unlike English, Chinese words are not naturally separated by spaces, making Chinese tokenization a more challenging task. For example, is the sentence “我爱北京天安门” (I love Beijing Tiananmen) segmented as “我/爱/北京/天安门” or “我爱/北京/天安门”? This requires reliance on context and semantics to judge.

2.3 Subword-level Tokenization: Smarter “Detachable” Bricks

To solve the OOV problem of word-level tokenization and the inefficiency of character-level tokenization, modern large language models universally adopt Subword-level Tokenization.

Idea: This method lies between character-level and word-level. It learns a vocabulary containing common words and some common word fragments (subwords). If it encounters a word not in the vocabulary, it can split it into smaller, known subword units.
Metaphor: This is like a smart Lego set. It not only has common complete bricks but also some special bricks that can be assembled or disassembled, such as “connectors,” “corner pieces,” etc. When you encounter a new, complex structure (an unrecognized word), it can intelligently decompose it into known small fragments. For example, the word “unhappiness” might be split into “un”, “happi”, “ness”.
Main Algorithms: Byte Pair Encoding (BPE), WordPiece, Unigram LM, etc.
Pros:

• Balance: Can effectively handle common words and decompose unknown words into meaningful sub-units, reducing the OOV problem.
• Reduced Vocabulary Size: Compared to word-level tokenization, subword-level tokenization can significantly reduce the size of the vocabulary the model needs to learn without sacrificing too much semantic information.
• Efficient Use of Context Window: Within a limited “context window” (the number of tokens a model can process at once), more information can be encoded.

3. The Role and Challenges of Tokenization in Large Language Models

Tokenization is the cornerstone of large language models for understanding and generating text.

• Processing Text Input: When you ask ChatGPT a question, your question is first processed by the tokenizer into a sequence of tokens, and then these tokens are read and understood by the model.
• Generating Text: When the model generates an answer, it also predicts and generates one token after another.
• Cost and Efficiency: Many large language model APIs charge based on the number of tokens, so efficient tokenization helps users use the service more economically. At the same time, fitting more content into the model’s “context window” also relies on efficient tokenization.

However, tokenization is not flawless, and it also brings some unique challenges to models:

• The “Reversing Words” Puzzle: Research has found that large language models sometimes struggle with seemingly simple tasks (such as reversing the letters of a word). The reason is that the model “sees” the whole token, not the individual characters inside the token. If “elephant” is a token, the model cannot easily manipulate the individual letters within it.
• Complexity in Chinese Scenarios: The challenge of Chinese tokenization is particularly prominent. Due to the lack of spaces between words, “incorrect tokenization is a key point hindering LLMs from accurately understanding input, leading to unsatisfactory output, and this defect is even more obvious in Chinese scenarios.”
• Adversarial Attacks: Researchers have even built specialized adversarial datasets (such as ADT) to reveal model vulnerabilities and cause inaccurate responses by challenging LLM tokenization methods. This means that even text that looks identical to the human eye, once tokenized differently, might lead the model to a completely different understanding.

4. The Future of Tokenization: Continuously Evolving “Brick” Craftsmanship

With the continuous development of AI technology, tokenization technology is also constantly evolving:

• Domain and Language Customization: Custom tokenizers that are more optimized and professional will appear for different languages (such as Chinese) and specific domains (such as law, medical).
• Optimization Algorithms: Researchers are constantly improving tokenization algorithms and processes to enhance the overall capability of LLMs, such as integrating pre-trained language models, multi-criteria joint learning, etc.
• Possibility of Transcending Text Tokenization: Some frontier explorations have even begun to question the status of traditional text tokenization as the core input of AI. For example, the DeepSeek-OCR model attempts to process text in the form of pixels, converting text directly into visual information, which may “end the era of tokenizers”. Former Tesla AI Director and OpenAI founding team member Karpathy also stated that perhaps all LLM inputs should be images, and even pure text is best rendered into images before being fed to the model, because tokenizers are “ugly, separate,” “introduce all the cruft of Unicode and byte encodings,” and bring security and jailbreak risks.

In summary, tokenization is the cornerstone for AI, especially large language models, to understand and process human language. It is like the craft of creating language “Lego bricks” for machines; its precision and efficiency directly affect the performance and intelligence level of AI models. Understanding tokenization allows us to better recognize the strengths and limitations of AI and look forward to smarter language processing methods in the future.

AI’s “Intelligence” Accelerator: Explaining “Grouped-Query Attention” (GQA) in Simple Terms

In recent years, the field of Artificial Intelligence (AI) has advanced rapidly. Large Language Models (LLMs) such as ChatGPT and Ernie Bot have deeply integrated into our daily lives, capable of writing articles, coding, and even chatting with us. The reason these models are so “smart” is inseparable from a core mechanism—“Attention”. However, as models grow larger, computational costs rise. To make these AIs “shrewder” and more “economical”, scientists have been striving for optimization. Today, let’s talk about a key optimization technique: “Grouped-Query Attention” (GQA).

Part 1: What is “Attention”? How does AI “Focus”?

Imagine you need to find a book about the “History of Artificial Intelligence” in a library. What would you do?

1. Your Need (Query): You think to yourself, “I want to find a book about the history of artificial intelligence.” This is your “Query”.
2. Book Tags/Index (Key): Every book in the library has a tag or index card, perhaps written with “Introduction to AI”, “Principles of Machine Learning”, “History of Computing”, etc. These are the “Keys” for each book, used to describe it.
3. Book Content (Value): When you find the corresponding book based on your query, the specific content inside the book is the “Value”.

Artificial Intelligence models process information in a similar way. When we input a sentence into an AI model, such as “I love Beijing Tiananmen”, the model generates three things for each word in the sentence: a “Query”, a “Key”, and a “Value”.

• Query: Represents the “focus” or “question” the model is currently attending to.
• Key: Represents the “feature” or “label” of each part of the information repository, used to match against the Query.
• Value: Represents the “actual content” or “data” of each part of the information repository.

The model uses the “Query” of each word to match against the “Keys” of all other words. The higher the match degree, the stronger the “relevance” between these words. Then, based on these relevance scores, the model performs a weighted sum of the “Values” of other words to obtain a richer, contextually meaningful representation of the current word. This entire process is the AI’s “Attention Mechanism”, which allows the model, like a human, to know which parts are more important and require “focus” when processing information.

Part 2: Multi-Head Attention: Letting AI “Think from Multiple Angles”

If there is only one “angle of thought”, AI might view problems one-sidedly. To allow AI to understand information more comprehensively from multiple angles, scientists introduced “Multi-Head Attention” (MHA).

This is like a room full of experts discussing a complex project:

• Each expert is an “Attention Head”: Each expert has their own expertise and perspective. For example, one expert focuses on project cost (their “Query” emphasizes cost), another on risk control (their “Query” emphasizes risk), and another on market prospects (their “Query” emphasizes market).
• Independent Consultation: Each expert takes their own question (Query) to consult all project materials (Keys and Values), and then provides their own analysis report (weighted sum of Values). Finally, these reports are aggregated to form a more comprehensive project assessment.

The introduction of the “Multi-Head Attention” mechanism greatly improved the AI model’s ability to understand complex information, which is key to the huge success of Transformer models (like the foundation of the GPT series).

However, this “multi-angle thinking” comes at a cost:

Imagine if there were dozens, or even hundreds, of experts in this room, and each expert needed to independently and completely look through all project materials. With few people, it’s fine, but once there are many experts and vast amounts of materials, the following problems arise:

• Low Efficiency: Everyone is repeatedly consulting, extracting, and processing the same raw data, causing a huge waste of time and computational resources. It’s like many chefs cooking in the same kitchen; if every chef needs to personally run to the fridge to get their own ingredients, the fridge door will get blocked, and efficiency will naturally be low.
• Memory Pressure: Generating and storing the independent consultation results of each expert requires a lot of memory space. For Large Language Models with hundreds of billions of parameters, these storage overheads quickly become a bottleneck, severely limiting the model’s running speed, especially during text generation (inference).

Part 3: Grouped-Query Attention: Shared Resources, Efficient Collaboration

To solve the efficiency and memory problems caused by “Multi-Head Attention”, scientists explored various optimization schemes. “Grouped-Query Attention” (GQA) is one of the very successful attempts, skillfully finding a balance between model effectiveness and operational efficiency.

Before understanding GQA, let’s briefly mention one of its predecessors—“Multi-Query Attention” (MQA):

• Multi-Query Attention (MQA): This is like all chefs cooking their own dishes, but they share a single ingredient list and take from a common ingredient pool (single Key K and Value V). The advantage is that it greatly reduces the number of trips to the fridge, making it the fastest, but the downside is that because the ingredient variety is fixed for all dishes, the flavor might become monotonous, and the model effect (quality) might decline.

The essence of Grouped-Query Attention (GQA) lies in “Grouping”:

GQA proposes that we don’t need every “chef” (Attention Head) to have their own independent ingredient list and pool, nor do we need all chefs to share just one. We can divide these “chefs” into several groups.

• Metaphor: Suppose we have 8 chefs (i.e., 8 Attention Heads). Now we divide them into 4 groups, with 2 chefs per group. Each group will have its own independent ingredient list and ingredient pool. In this way, although each chef’s recipe (Query Q) is independent, the two chefs within a group share an ingredient list (Shared Key K) and an ingredient pool (Shared Value V).
  • Previously, 8 chefs needed to run to the fridge 8 times to get 8 portions of tomatoes (Standard MHA).
  • MQA is 8 chefs running to the fridge 1 time to get 1 portion of tomatoes, which all chefs share (MQA).
  • GQA is 4 groups each running to the fridge 1 time, running a total of 4 times to get 4 different portions of tomatoes (GQA).

In this way, GQA maintains the partial diversity of Multi-Head Attention (different groups still have different perspectives) while significantly reducing the demand for memory and computational resources. It reduces the number of Keys and Values, thereby lowering memory bandwidth overhead and speeding up inference, especially for Large Language Models. GQA is like finding a “sweet spot” between MHA and MQA, maximizing inference speed with minimal sacrifice to model quality.

Part 4: Applications and Future of GQA

“Grouped-Query Attention” is not a purely theoretical concept; it has been widely applied in actual Large Language Models. For example, the Llama 2 and Llama 3 model series developed by Meta, as well as mainstream large models like Mistral AI’s Mistral 7B, have all adopted GQA technology.

This means:

• Faster Response Speed: Users interacting with these GQA-based models will experience faster response speeds and a smoother experience.
• Lower Operating Costs: For enterprises deploying and running these large models, GQA significantly reduces the required hardware resources and operational costs, allowing AI technology to serve more people more economically.
• Promoting AI Popularity: By improving efficiency and reducing costs, technologies like GQA are helping AI models move from research labs to broader practical applications, allowing more people to access and use cutting-edge AI capabilities.

In summary, “Grouped-Query Attention” is an important engineering optimization in the field of AI. It allows Large Language Models to become more “resource-conscious” while maintaining powerful intelligence. In the future, we can look forward to more innovative technologies similar to GQA, achieving a better balance between performance, efficiency, and accessibility for AI models, thereby better empowering social development.

Collaborative Intelligence: Unveiling How “Distributed Reinforcement Learning” Makes AI Faster and Smarter

Imagine you are teaching a child to ride a bicycle. Through constant attempts, falling down, and getting back up, the child gradually masters balance and finally learns to ride. Every attempt and every fall is a learning experience, and successfully maintaining balance is the “reward.” This is a daily life reflection of a fascinating concept in the field of Artificial Intelligence—Reinforcement Learning (RL).

1. From “Solo Groping” to “Team Learning”: What is Reinforcement Learning?

In the world of AI, reinforcement learning is like an agent learning through “trial and error.” It takes actions in an environment, and the environment gives feedback based on its actions—“rewards” or “punishments.” The agent’s goal is to learn an optimal policy to maximize the total long-term reward it receives.

For example, in a video game, if an AI-controlled character walks into a trap, it gets a negative “punishment” and will try to avoid it next time. If it successfully collects a gold coin, it gets a positive “reward” and will actively look for coins next time. Through countless attempts, this AI can learn how to clear the game. The advantage of this learning method is that the AI doesn’t need humans to tell it beforehand “there is a trap here, don’t go”; instead, it explores and discovers on its own. It excels in complex environments and requires less human interaction.

However, when the problems we need to solve become extremely complex, such as autonomous driving, managing large-scale urban traffic systems, or mastering strategy-heavy games like StarCraft II, relying on a single AI for “lone wolf” style learning becomes very inefficient and time-consuming because the amount of data it needs to process and learn from is too vast.

2. Why “Distributed”? — When One Person is Not Enough

This is like building a skyscraper. If there is only one experienced architect and one worker, no matter how smart and hardworking they are, facing such a huge project would be time-consuming and inefficient. What we need is a massive team, with everyone performing their duties and collaborating efficiently.

In AI reinforcement learning, when the complexity of a task reaches a certain level, the computational power and learning speed of a single agent become bottlenecks. To cope with such large-scale decision-making problems and handle massive amounts of data, we need to decompose and extend the learning task to multiple computing resources. This introduces our protagonist—Distributed Reinforcement Learning (DRL).

3. Distributed Reinforcement Learning: Gathering Team Wisdom to Accelerate AI Growth

The core idea of distributed reinforcement learning is to assign the two time-consuming steps in the reinforcement learning process—“exploring experience” and “updating policy”—to multiple “workers” to complete in parallel.

We can use a large restaurant kitchen to vividly illustrate this model:

• “Waiters” (Actors): Imagine dozens of waiters (corresponding to multiple Actors in DRL) scattered in every corner of the restaurant. They each carry a menu (the current policy model), interact with different customers (the environment), take orders (collect experience data), and record customer feedback (rewards). The main duty of an Actor is to interact with the environment and generate massive amounts of “experience data.”
• “Chefs” (Learners): In the kitchen, there are several senior chefs (corresponding to multiple Learners in DRL). They don’t face customers directly but endlessly study and adjust recipes (optimize policy models) from the massive orders and feedback collected by waiters (experience data) to ensure maximum customer satisfaction (maximize rewards). The Learner’s task is to use this experience data to update and improve the model’s policy.
• “Head Chef” (Parameter Server): There is also a head chef who is responsible for unifying the recipes of all chefs, ensuring the dishes taste consistent, and distributing the latest and best recipes (model parameters) to all chefs and waiters. The Head Chef ensures that all individuals involved in learning work based on the same, latest knowledge.

Through this division of labor and collaboration, dozens of waiters can collect experience from dozens of tables simultaneously, while chefs can study these experiences in parallel to constantly improve recipes, and the head chef quickly promotes the best recipes. In this way, the restaurant’s dishes (AI policy) can become better and better at a speed far exceeding that of a single chef.

4. The Superpowers of Distributed Reinforcement Learning

Introducing the “distributed” mechanism brings the following significant advantages to reinforcement learning:

• Lightning Fast Learning Speed: Multiple Actors exploring the environment simultaneously greatly improves data collection efficiency; multiple Learners processing this data in parallel causes model update speeds to soar. This means AI can master complex tasks faster.
• Handling Ultra-Large Scale Problems: Facing complex problems that traditional single machines cannot solve, DRL can mobilize massive computing resources to achieve efficient solutions.
• More Stable Learning: Multiple workers learning from different perspectives and experiences produce diverse gradient updates, which helps smooth the learning process and avoid getting stuck in local optima.
• Better Exploration Capability: More Actors mean a broader range of exploration, allowing the agent to more effectively discover potential optimal strategies in the environment.

5. “Smart Butlers” in Life: Application Scenarios of Distributed Reinforcement Learning

Distributed reinforcement learning is no longer just a theory; it is playing an increasingly important role in our lives:

• Autonomous Driving: Imagine a fleet of unmanned vehicles shuttling through the city. Each car is an Actor, constantly collecting information on road conditions, obstacles, and traffic signals, and trying different driving strategies. These experiences are pooled to cloud Learners for analysis, quickly iterating safer and more efficient driving strategies, which are then synchronized to all vehicles. Tesla’s FSD system uses a distributed architecture based on the C51 algorithm to handle complex urban scenarios, significantly reducing intersection accident rates. Companies like Wayve and Waymo are also using RL to strengthen autonomous driving capabilities.
• Multi-Robot Collaboration: In smart factories, a large number of robots need to collaborate to complete assembly tasks; in logistics warehouses, robots need to move goods efficiently; even in disaster relief, robot teams need to cooperate for search and reconnaissance. DRL can provide efficient and scalable control strategies for these multi-robot systems.
• Game AI: AI like AlphaGo, OpenAI Five (DOTA2), and AlphaStar (StarCraft II) can defeat world champions largely due to the powerful support of distributed reinforcement learning. It allows AI to quickly learn and master complex strategies from massive game matches.
• Personalized Recommendation: When you read news or watch videos, the recommendation system behind it constantly learns your preferences. Facebook’s Horizon platform uses RL to optimize personalized recommendations, notification pushes, and video stream quality.
• Financial Quantitative Trading: In the rapidly changing financial market, DRL can help develop AI systems that optimize trading strategies and capture risk distribution characteristics. JPMorgan’s JPM-X system has applied quantile projection technology to optimize high-frequency trading strategies.
• Distributed System Load Balancing: Optimizing resource allocation and load balancing in large data centers or cloud computing environments to improve system efficiency and fault tolerance.

6. Towards the Future: “Smoother” AI

Currently, distributed reinforcement learning is still evolving. Recent advances, such as the SEED RL architecture proposed by Google, have further optimized the collaborative efficiency between Actors and Learners, allowing Actors to focus only on interacting with the environment while delegating policy inference and trajectory collection tasks to Learners, significantly accelerating training. Stanford University’s recently introduced (October 2025) AgentFlow framework, through a new paradigm of “Flow-based Reinforcement Learning,” allows multi-agent systems to optimize “planners” in real-time during interaction, outperforming large models like GPT-4o on multiple tasks even with smaller models.

In summary, distributed reinforcement learning is the only way for deep reinforcement learning to move towards large-scale applications and solve complex decision spaces and long-term planning problems. It is like forming a super learning team, enabling AI to master complex skills of the human world with unprecedented speed and efficiency, constantly expanding the boundaries of artificial intelligence, and making future intelligent systems more powerful and inclusive.

Fractional Genetic Learning: A Speculative Fusion of Evolution and Mathematics

The field of AI is full of fascinating and complex concepts, and the term “Fractional Genetic Learning“ sounds both fresh and intriguing. However, within mainstream AI academia and engineering, there is currently no widely recognized technical concept specifically named “Fractional Genetic Learning.” This term might be a creative combination of existing AI concepts or point towards a very cutting-edge and not yet popularized research direction.

To better understand the AI ideas that might be embedded in this imaginative name, we can break it down into parts: “Genetic,” “Fractional,” and their application in “Learning.”

1. Genetic: The Wisdom of Nature — Genetic Algorithm (GA)

When we talk about the application of “genes” in AI, the most direct association is the Genetic Algorithm (GA). This is an optimization and search algorithm inspired by the theory of biological evolution and natural selection.

Daily Life Analogy: Finding the Perfect Recipe

Imagine you are a gourmet trying to find the “perfect recipe” for a dish.

• “Recipe” is the Solution (Chromosome/Individual): Your recipe book has thousands of recipes. Each recipe (e.g., a way to make scrambled eggs with tomatoes) is an “individual” or “chromosome.”
• “Ingredients and Steps” are Genes (Gene): Every element on the recipe, like the amount of tomatoes, how eggs are beaten, types of seasoning and order of addition, can be seen as the “genes” of the recipe.
• “Taste” is Fitness (Fitness Function): Every time you finish cooking a dish, you rate it based on its taste (saltiness, freshness, etc.). This score is the “fitness” of the recipe; the higher the score, the better the recipe.
• “Chef’s Secret” is Selection (Selection): You keep the recipes that taste good, perhaps modifying them as a base, and discard those that taste bad. This is “selection,” letting the “fittest survive.”
• “Fusion and Innovation” is Crossover (Crossover): If you have two good recipes (e.g., one for tomato scrambled eggs, one for tomato egg noodles), you try to combine their strengths, like fusing the tomato handling of the former with the egg frying method of the latter to create a new recipe. This is called “crossover” or “hybridization.”
• “Flash of Inspiration” is Mutation (Mutation): Sometimes, you act on a whim and try adding a pinch of spice you don’t usually use, or change frying to steaming. This low-probability random change is “mutation,” which might bring surprises or failures, but helps you explore new flavor combinations.

Through this generation-by-generation “recipe evolution,” the dishes in your recipe book become more delicious, eventually finding that “perfect recipe.” Genetic algorithms simulate this natural evolutionary process to let computers find the best or near-best solutions among massive possibilities, excelling in complex optimization problems like path planning, parameter optimization, or even training neural networks.

2. Fractional: The Power of Fine-Tuning — Fractional Calculus

In mathematics and engineering, especially in control and signal processing recently, “Fractional” points to the concept of Fractional Calculus. Unlike the integer-order calculus (1st derivative, 2nd integral) we learned in high school, fractional calculus allows the order of derivatives and integrals to be any real number, or even complex numbers.

Daily Life Analogy: Fine-Tuning Music

Imagine you are playing music on a stereo.

• Integer-Order Adjustment: Traditional volume knobs usually only allow integer steps adjustments, like from “Volume 1” to “Volume 5”; the change in volume might be abrupt.
• Fractional-Order Adjustment: If the volume knob allowed fractional fine-tuning, like adjusting to “2.35” or “4.78,” you could find a “perfect volume” between integer levels that better suits your hearing preference. This precise and minute adjustment lets you hear more details and emotions in the music.

In AI and control systems, fractional calculus is like this “fine-tuning” ability. It can more accurately describe the dynamic characteristics of complex systems, such as memory effects in materials or viscoelastic system behaviors, which are hard for traditional integer-order models to capture. By introducing fractional operators, AI systems can perform more detailed and flexible adjustments during optimization, control, or learning, leading to:

• More Precise Modeling: Better understanding and simulation of processes with “memory” or “non-local” characteristics.
• Enhanced Robustness: Making systems more stable and reliable when facing noise or uncertainty.
• Larger Optimization Space: Providing more possibilities for parameter tuning, helping algorithms find better solutions.

For example, in intelligent control, Fractional Order PID controllers have shown better performance than traditional PID controllers, offering significant improvements in trajectory tracking error and anti-interference ability.

3. Possible Meaning of “Fractional Genetic Learning”: Finely Crafted Evolutionary Intelligence

Combining the meanings of “Genetic” and “Fractional,” we can speculate that “Fractional Genetic Learning” might describe an AI learning paradigm that combines the wisdom of biological evolution with highly refined parameter adjustment capabilities.

Imagining “Fractional Genetic Learning”:

If we introduce fractional concepts into genetic algorithms, the following might happen:

• Fractional Mutation: In traditional GA, mutation is a bit flip (0 to 1) or a random small perturbation. Introducing fractional-order mutation might mean the “intensity” or “range” of mutation can be fine-tuned non-integers (e.g., 0.5-order mutation), making gene changes more subtle and diverse. This avoids the drastic degradation of solutions caused by drastic changes while allowing larger exploration when needed.
• Fractional Selection Pressure: When selecting high-quality individuals, we could design a fractional fitness evaluation mechanism or a fractional selection probability function. This would make the probability difference of choosing high-fitness individuals smoother or more elastic, better balancing the conflict between exploration (finding new solutions) and exploitation (optimizing known solutions).
• Fractional Crossover: During crossover, gene exchange might no longer be simple cutting and splicing but based on fractional operators for some form of “information fusion,” making the way offspring inherit superior traits from parents more complex and efficient.

Under this hypothesis, “Fruit“ (results) in the name might emphasize that this refined, “fractionalized” genetic evolution process can yield more “fruitful” learning outcomes—finding higher quality, stable, and robust solutions. It seeks not just to find an answer, but to find the “sweetest” answer in an elegant, precise, and efficient way.

Summary and Outlook

Although “Fractional Genetic Learning” is not a standard term in AI academia, it cleverly combines the bio-evolutionary inspiration of “Genetic Algorithms” with the refined, high-order control capability of “Fractional Theory.” This hints at a promising research direction: by introducing fractional mathematical tools, we can design and control the internal mechanisms of genetic algorithms or other evolutionary algorithms (like mutation, crossover, selection) with greater detail and flexibility.

This combination is expected to show advantages over traditional methods when dealing with complex, non-linear real-world problems with memory effects or long-range dependencies, such as in complex system optimization, robot control, new material design, or even protein structure prediction. Future AI development is likely to birth more unprecedented intelligent learning paradigms through such interdisciplinary and cross-conceptual fusion and innovation.

Divide and Conquer: Assessing the Efficiency of Grouped Convolution

The field of Artificial Intelligence (AI) is developing rapidly, with Convolutional Neural Networks (CNNs) playing a central role in tasks like image recognition. At the heart of CNNs lies a clever and efficient operation known as Grouped Convolution, which we will explore today.

I. From “All-Round Chef” to “Assembly Line Teams”: Understanding Standard Convolution

Imagine you are a chef in a restaurant. When a new order (an image) arrives, you need to handle various ingredients (feature channels of the image, like red, green, and blue information). Traditional “Standard Convolution” is like an “all-round chef” who pays attention to all ingredient types simultaneously. When picking up a piece of lettuce (a pixel), he not only looks at its color (current channel) but also considers the tomatoes and chicken next to it (surrounding pixels), thinking about how these ingredients form a delicious dish together (identifying a feature in the image, like edges or textures).

In technical terms, in standard convolution, each “convolution kernel” (which can be seen as a recognition pattern learned by the chef) operates on “all channels” of the input image to extract features. This means if your input image has 3 color channels (Red, Green, Blue) and you need to extract 100 different features, extracting each feature requires processing information from all 3 channels simultaneously, resulting in a considerable computational load.

II. Why Do We Need “Grouping”? Performance and Efficiency Considerations

Although the “all-round chef” is skilled, when faced with a huge number of orders, service speed slows down, and it requires a lot of kitchen space (computational resources) and manpower (model parameters). especially in the early days of AI development, hardware resources were far less powerful than they are today, making it very difficult to train large neural networks.

This problem became prominent in the 2012 ImageNet Image Recognition Challenge. The champion model at the time, AlexNet, introduced the concept of “Grouped Convolution” for the first time because a single GPU could not handle the massive computation of the entire network, so researchers distributed the calculation across multiple GPUs to run in parallel.

III. Grouped Convolution: The Secret to Efficiency Gains

So, what is grouped convolution? It’s like breaking down the work of the “all-round chef” into several “specialized teams.”

Visual Analogy: Specialized Teams on an Assembly Line

Suppose your restaurant is very busy now, and you need to improve efficiency. You decide to form several specialized teams:

• Vegetarian Team: Specializes in processing vegetables and fruits.
• Meat Team: Specializes in cooking various meats.
• Seafood Team: Focuses on processing fish and shrimp products.

When a new order (input feature map) arrives, you no longer let one chef handle all ingredients. Instead, you assign “part of the ingredients” (channels of the input feature map) to the Vegetarian Team, another part to the Meat Team, and another to the Seafood Team. Each team is only responsible for processing the ingredients assigned to them, cooking with their “specialized skills” (corresponding convolution kernels). Finally, all teams combine their cooked dishes to complete the order.

Technical Analysis: Splitting and Parallelism

In AI, “Grouped Convolution” works exactly like this:

1. Input Channel Grouping: It divides the channels of the input feature map (imagine ingredient types) into $G$ “groups.” For example, if there were originally $C$ input channels, they are now divided into $G$ groups, each with $C/G$ channels.
2. Independent Convolution: Each convolution kernel no longer processes all input channels like the “all-round chef” but is only responsible for processing the input channels of the group it belongs to. Just like the Vegetarian Team only handles vegetables and the Meat Team only handles meat.
3. Result Concatenation: After each group completes the convolution operation independently, they obtain their respective output feature maps. Finally, these output feature maps from different groups are concatenated to form the final output feature map.

Thinking Comparison (Simplified):

• Standard Convolution: Input Channels ($C$) —-> Kernel (Process all $C$ channels) —-> Output Channels ($C'$)
• Grouped Convolution:
  • Input Channels ($C$) divided into $G$ groups: $(C/G), (C/G), ..., (C/G)$
  • Group 1 ($C/G$) —-> Kernel 1 (Process Group 1 only) —-> Output Channels ($C'/G$)
  • Group 2 ($C/G$) —-> Kernel 2 (Process Group 2 only) —-> Output Channels ($C'/G$)
  • …
  • Group $G$ ($C/G$) —-> Kernel $G$ (Process Group $G$ only) —-> Output Channels ($C'/G$)
  • Finally, concatenate all ($C'/G$) outputs to get the final Output Channels ($C'$).

IV. Pros and Cons of Grouped Convolution

The reason grouped convolution is so important lies in its significant advantages:

1. Reduced Computation and Parameters: This is the core advantage. After dividing input channels into $G$ groups, the number of channels processed by each kernel is reduced to $1/G$ of the original, so the total computation and parameter count are also approximately reduced to $1/G$. This makes the model “lighter,” allowing larger, deeper networks to be trained with the same computational resources, or allowing the same model to run faster.
2. Improved Parallel Efficiency: As shown by AlexNet, grouped convolution can distribute calculations of different groups to different processors (like GPUs) for parallel execution, speeding up training.
3. Foundation for Lightweight Networks: It is a core component of many modern efficient lightweight networks (such as MobileNet, Xception), specifically designed for scenarios with limited computing resources like mobile devices and embedded systems. In particular, Depthwise Separable Convolution is an extreme form of grouped convolution where each input channel is treated as an independent group for convolution.

However, grouped convolution is not perfect; it has some drawbacks:

• Inter-Group Information Blocking: Since each group processes independently, channel information cannot communicate directly between different groups. This may lead to the model lacking in capturing global features or cross-channel correlations. To solve this problem, improved methods have emerged, such as Microsoft’s “Interleaved Group Convolutions” (e.g., ShuffleNet), aimed at facilitating information flow between groups.
• Actual Speedup Not Always Theoretical: Although theoretically reducing computation, in actual hardware (especially GPU) acceleration libraries, optimizations for standard convolution are more mature. Grouped convolution may not reduce memory access frequency, so in some cases, the actual efficiency improvement may not be as significant as the theoretical reduction in computation.

V. A Brief History of Applications and Development

• Origin (2012, AlexNet): Grouped convolution was originally born to overcome hardware limitations at the time, slicing the network across multiple GPUs for parallel execution.
• Development (2017 to Present, MobileNet, Xception, etc.): With technological advancements and significant improvements in hardware performance, the main application scenario for grouped convolution shifted from “solving hardware limitations” to “building efficient, lightweight neural networks,” especially on mobile and edge computing devices. It became the cornerstone of Depthwise Separable Convolution, which is the core of efficient models like the MobileNet series.

Conclusion

Grouped Convolution is a seemingly simple but highly influential concept in the AI field. By “dividing and conquering” complex convolution operations, it significantly reduces computation and parameter overhead, enabling AI models to run efficiently on resource-constrained devices, and playing a key role in milestone works like AlexNet and MobileNet. Like flexible “specialized teams” in a restaurant, it allows AI models to achieve powerful functions while being “lighter” and “faster.” Understanding grouped convolution gives us a deeper insight into the design principles of modern AI models.

The “Swiss Army Knife” of AI: Demystifying Function Calling

Artificial Intelligence (AI) has moved from science fiction into our daily lives, appearing as smartphone assistants, online translators, and recommendation systems. However, early AI models, especially Large Language Models (LLMs), were like “armchair strategists”—eloquent and knowledgeable about everything, yet unable to “get their hands dirty” to perform tasks. They were good at “talking” but not at “doing.”

So, how did AI move from simply “talking” to “doing”? A concept called “Function Calling“ has played a crucial role in this transition. It acts like a “Swiss Army Knife” that empowers AI to interact with the real world.

Part 1: What is a “Function”? AI’s “Toolbox”

Before diving into “Function Calling,” let’s understand what a “function” is.

Imagine a very smart child who is well-read and knows everything about astronomy and geography. If you ask him to explain knowledge, he can do it perfectly. But if you ask him to “check tomorrow’s weather in Beijing” or “book a flight based on my schedule,” he might blankly reply, “I don’t know how to do that.” This is because, while he possesses vast knowledge, he lacks the specific “tools” and “skills” to execute these concrete tasks.

In computer programming, a “function” is such a “tool” or “skill.” It is a piece of pre-written code designed to perform a specific task. for example, a “weather query” function returns the local temperature and humidity when given a city name; or a “booking” function completes a flight reservation when provided with departure, destination, and date information. These functions exist independently, perform their specific duties, and can be combined to complete complex tasks.

For today’s AI, independent of the model itself, a “function” is an external operation or information retrieval mechanism that can be triggered by specific instructions. These functions are usually defined by developers who “declare” their capabilities and required parameters to the AI model, just like preparing a toolbox filled with labeled instruction manuals for that smart child.

Part 2: What is “Function Calling”? AI Learning to Use “Tools”

Since AI now has the “instruction manuals” (function definitions) in its “toolbox,” “Function Calling” describes the process where the AI intelligently identifies which “tool” (function) to use based on the user’s instructions and intent, generates the necessary parameters to call that tool, and instructs the application to execute it.

Let’s continue with the smart child analogy:

You say to him: “Check tomorrow’s weather in Beijing for me.”

• The smart child (AI model) immediately understands your intent is to “check weather.”
• He looks into his “toolbox” and finds a manual named “Weather Query Tool Manual” (corresponding to the “weather query function”).
• The manual says this tool requires a “city name” as information. The child extracts “Beijing” from your request as this parameter.
• Then, the child doesn’t predict the weather himself; he simply follows the manual and hands the parameter “Beijing” to a “real weather checking device” (the application executing the function).
• After the “weather checking device” finds the result (e.g., Sunny, 25°C), it returns the result to the child.
• Finally, the child tells you in human-understandable language: “Tomorrow in Beijing it will be sunny with a temperature of 25 degrees Celsius.”

This is the core workflow of “Function Calling”:

1. User Request: E.g., “Book a flight from Shanghai to Beijing for this afternoon.”
2. AI Intent Analysis: The LLM understands the user wants to “book a flight” and extracts key information: “Origin (Shanghai),” “Destination (Beijing),” “Time (this afternoon).”
3. AI Tool Selection: The model identifies a function in its preset “tool list” (provided by developers) that can handle the booking request, e.g., book_flight(origin, destination, date, time).
4. AI Parameter Generation: The model converts the extracted information into parameters required by the function, e.g., origin="Shanghai", destination="Beijing", date="2025-10-26", time="Afternoon".
5. Application Execution: Crucially, the AI model itself does not execute the booking. It generates a structured instruction (usually in JSON format) telling the external application: “Please use parameters origin='Shanghai', destination='Beijing', date='2025-10-26', time='Afternoon' to call the function book_flight.”
6. Result Returned to AI: After the external application executes the booking (e.g., via an airline API), it returns the execution result (e.g., “Flight booking successful, Flight No. AC123”) to the AI model.
7. AI Formulation of Response: Upon receiving the result, the AI model formulates a natural, friendly response to the user, e.g., “Your flight from Shanghai to Beijing for this afternoon has been successfully booked. The flight number is AC123.”

Part 3: Why is “Function Calling” So Important? A Leap in AI Capabilities

The emergence of “Function Calling” marks a significant leap in AI model capabilities from “Understanding & Generation” to “Understanding, Execution & Interaction.”

• Breaking Knowledge Cutoff Constraints: LLM knowledge is fixed at training time and cannot access real-time information. Through function calling, AI can connect to external APIs and databases to fetch the latest weather, news, stock prices, real-time traffic, etc. For instance, when asked “What’s the news today?”, AI can call a news API to get and summarize headlines instead of relying on old training data.
• Expanding AI’s Action Capabilities: AI is no longer just a “chatbot”; it can perform practical actions. It can send emails, schedule meetings, control smart home devices, perform complex math calculations, search the web, or query internal corporate databases. It transforms AI from a passive question-answering tool into an “Agent” that proactively interacts with the external world to solve real problems.
• Improving Accuracy and Utility: Offloading tasks requiring precise calculation or real-time data to specialized external tools allows AI to avoid “hallucinations” (generating false information), significantly improving the accuracy and utility of responses. For example, letting AI call a calculator function for math is much more reliable than letting it “think” of the answer.

Therefore, many believe that while 2023 was the year of Large Model Technology, 2024 is poised to be the year of Large Model Applications, as Function Calling greatly accelerates the integration and deployment of AI in the real world.

Part 4: Latest Advances and Future Outlook

Since its official introduction by OpenAI in 2023, “Function Calling” technology has quickly become a hotspot in the AI field.

• Mainstream Model Support: Currently, mainstream LLMs like OpenAI’s GPT series, Google’s Gemini series, and Alibaba’s Bailian deeply support function calling capabilities.
• Handling Complex Scenarios: Modern function calling mechanisms can support calling multiple functions in a single turn (Parallel Function Calling) and chaining multiple functions in sequence (Sequential Function Calling) to handle complex requests and multi-step tasks. For example, for “Schedule a meeting for colleagues in New York and London,” AI might first call a “Time Zone Query” function, then a “Calendar Query” function, and finally a “Meeting Schedule” function.
• Higher Reliability: Developers can use stricter settings (like OpenAI’s strict: true feature) to ensure model-generated function parameters strictly adhere to predefined JSON SCHEMAS, improving reliability and security.
• Thriving Ecosystem: Tools and frameworks like LangChain provide powerful support around function calling, significantly lowering the barrier for developers to build complex AI applications.
• Future Potential: As technology matures, function calling will further empower AI Agents, making them indispensable intelligent assistants in our daily lives. They will not only connect and control the broader digital world (e.g., managing schedules, shopping, finance) but also interact with the physical world via IoT devices (e.g., controlling smart homes), serving humanity more proactively and efficiently.

Conclusion

“Function Calling” is the key bridge taking AI from “Understanding” to “Action.” It transforms AI models from simple text generators into powerful intelligent agents capable of interacting with the outside world and executing actual tasks. By understanding this concept, we can better grasp the direction of AI development and look forward to the convenience and surprises it will bring us in the future.