title: Gibbs Sampling
date: 2025-05-05 14:03:50
tags: [“Machine Learning”, “Probabilistic Models”]

AI’s Wonderful Journey: Gibbs Sampling—Searching for “Perfect” Samples in a Complex World

In the vast world of artificial intelligence, we often need to understand and analyze extremely complex data patterns. Imagine we want to know what the “cat-est” (most cat-like) photograph looks like among all possible photographs? Or, among all possible combinations of patient symptoms, what are the “typical” symptom combinations for a certain disease? These questions all involve extracting representative “samples” from an extremely complex “probability distribution” that is difficult for us to grasp directly. And the “Gibbs Sampling” we are talking about today is a wonderful tool in the AI field to solve such difficult problems.

The Problem: How to “Sample” a Complex World?

First, let’s understand what “sampling” is. In statistics and AI, “sampling” is like picking out some representative examples from a large pile of things. For example, if you want to know the average income of a city, you can’t ask everyone, but random people are selected to survey; these selected people are “samples.”

But sometimes, this “large pile of things” is simply too complex! Imagine you are designing a “perfect” room decoration plan. This plan involves not only wall color and floor material but also furniture style, lighting layout, curtain style, ornament placement, etc. Each element has countless choices, and these elements affect each other: for instance, if you choose vintage furniture, it might not suit ultra-modern lighting. To think through all possibilities at once and directly choose the “best” few plans is almost impossible. Because the probability distribution of this “perfect room” (the set of all permutations of elements) is too vast and too complex, we cannot directly “see” its full picture.

In the AI field, examples of this “perfect room” abound. For instance, in Natural Language Processing, to generate a coherent and meaningful sentence, the choice of each word depends on the preceding and succeeding words. In image recognition, the color distribution of an object’s pixels and the positional relationships of adjacent objects are all interdependent. Directly finding samples that meet specific conditions from all possible sentences or images is extremely difficult.

Enter Gibbs Sampling: The Wisdom of Divide and Conquer

Gibbs sampling is a clever method to solve this “complex world sampling” problem. Its core idea is: since I cannot grasp the whole of all elements at once, I will do it one by one, gradually approaching the “truth” of the whole through local adjustments.

We use that “room decoration” example to intuitively understand Gibbs sampling:

1. Start Randomly: You don’t need to have a “perfect” plan at the beginning. Just pick a room randomly, paint a wall randomly, place a few pieces of furniture randomly, and consider this your “initial decoration plan.” This plan might be terrible, but it doesn’t matter; it’s just a starting point.

2. “Fixate” on One Element, Ignore Others: Now, you start “decorating.” But you don’t consider everything simultaneously; instead, you focus on only one element at a time.

   • For example, look at the wall color first. You pretend that all the furniture, lighting, and curtains in the room are already “fixed” there, and you just think: “Given the background of these furniture and lights, which wall color looks best?”
   • You will pick the most matching or favorite one among all possible wall colors and paint it.

3. Update, Then Move to the Next: After painting the wall, the room’s wall color has changed. Now, you look at the next element, say furniture placement. You again pretend that the room’s wall color, lighting, and curtains are all “fixed” (of course, the current wall color is the one you just painted), and then you ask yourself: “Under the current wall color, lighting, and curtains, how should the furniture best be placed?”

   • You will again choose the most suitable one among all possible furniture placement plans and rearrange the furniture.

4. Iterate and Improve: You adjust element by element like this: Wall -> Furniture -> Lighting -> Curtains -> Ornaments -> (Back to) Wall -> Furniture… cycling continuously. Each adjustment only focuses on one element and makes it perform “best” given the current conditions of other elements.

You might think, what’s the use of such “robbing Peter to pay Paul” adjustments? The wonder lies in the fact that as you continuously adjust and cycle, the overall decoration plan of the room will become more and more reasonable, closer and closer to the “perfect” plan in your heart. After enough adjustments, many “decoration plans” (that is, the room states after all elements have been adjusted for a round) you get, although not necessarily each one is the “perfect of perfects,” they will all be quite good, representative plans, effective samples from that complex “probability distribution.”

The AI Principle Behind It: Markov Chains and Conditional Probability

Behind this intuitive example lie rigorous mathematical principles:

• Markov Chain: “I paint the wall, then adjust the furniture”—this embodies the idea of a Markov chain. The current state (like how furniture is placed) only depends on the previous state (like the color the wall was just painted), and not on states much earlier. Gibbs sampling constructs a special Markov chain whose final stable state is the target distribution we want to sample.
• Conditional Probability: “Given the background of these furniture and lights, which wall color looks best?”—this is exactly the application of conditional probability. We don’t choose directly from all possible wall colors, but choose the probability distribution of wall colors “given other elements (conditions).”

Gibbs sampling effectively navigates through complex, high-dimensional probability distributions through this “local conditional sampling, global update” manner and collects a series of representative samples.

Applications of Gibbs Sampling in AI

As a Markov Chain Monte Carlo (MCMC) method, Gibbs sampling has wide applications in AI and machine learning fields:

1. Bayesian Inference: This is one of the most classic uses of Gibbs sampling. When we need to estimate parameters of complex models, since the posterior distribution cannot be computed directly, Gibbs sampling can approximate the posterior distribution by iteratively sampling from conditional distributions, helping us understand the uncertainty of model parameters.
2. Topic Modeling: In Natural Language Processing, such as the famous LDA (Latent Dirichlet Allocation) model, used to discover latent topics from large amounts of text. Gibbs sampling can be used to infer the topic distribution of each document and the word distribution of each topic, thereby revealing the deep structure of the text.
3. Image Processing and Computer Vision: In tasks like image denoising and image segmentation, when there are complex spatial dependencies between pixels, Gibbs sampling can help the model generate high-quality images or segmentation results while maintaining local coherence.
4. Recommender Systems: In some complex recommender systems, user preferences, item characteristics, and their interactions form a highly complex system. Gibbs sampling can be used to estimate users’ latent preferences for different items, thereby making more accurate recommendations.
5. Graphical Models: In various probabilistic graphical models (like Markov Random Fields, Conditional Random Fields), Gibbs sampling is an important tool for inference and learning, especially when dealing with nodes that have strong dependencies.

The latest research is still exploring methods combining Gibbs sampling with deep learning, for example, in the training of certain generative models (like Restricted Boltzmann Machines, RBMs), Gibbs sampling plays an important role. It is also used to train variants of certain Generative Adversarial Networks (GANs) to improve sample quality and diversity. Additionally, in some Bayesian Deep Learning frameworks, Gibbs sampling and its variants are also used to sample neural network weights, thereby quantifying model uncertainty.

Conclusion

Gibbs sampling is like a patient “decoration designer”; when facing an extremely complex “big project” where elements are interconnected, it doesn’t seek speed or greed but chooses “divide and conquer.” It focuses on only one local part at a time, finding the “best” state for the current local part while keeping other parts unchanged. Through such continuous cycling, the entire system will unknowingly gradually tend towards an overall optimal or representative state. It is this wisdom of simplifying complexity and moving forward layer by layer that makes Gibbs sampling a powerful tool in the AI field for processing complex probability distributions and extracting representative samples, helping artificial intelligence continuously make breakthroughs on the road to exploring the unknown world.

References:
Hinton, G. E., & Sejnowski, T. J. (1986). Learning and relearning in Boltzmann machines. In Parallel distributed processing: Explorations in the microstructure of cognition, Vol. 1. Foundations (pp. 282-317). MIT press. (While an older paper, it lays groundwork for RBMs where Gibbs sampling is key).
“Generative Adversarial Networks (GANs) and Gibbs Sampling” related research can be found in a variety of recent papers exploring MCMC methods in GANs, though not a single definitive paper. For instance, some works use MCMC for sampling from the generator trained by GAN.
Specific literature on combining Gibbs sampling in Bayesian Deep Learning is abundant, such as applications of related MCMC methods in posterior sampling of neural network weights, refer to Bayesian Deep Learning Survey Papers.

title: Gemma
date: 2025-05-05 06:04:37
tags: [“Deep Learning”, “NLP”, “LLM”]

The “Micro Gem” of the AI Field: An In-Depth Interpretation of the Google Gemma Model

In the vast universe of artificial intelligence, Large Language Models (LLMs) are undoubtedly the brightest stars of recent years. They can understand and generate human language, even engage in creation and reasoning, painting a future full of infinite possibilities for us. However, these powerful models are often massive in size, requiring vast computational resources to run. Just as people were daunted by the resource threshold of “large models,” Google brought us a series of “micro gems”—the Gemma models—which, with their lightweight and open-source characteristics, make high-performance AI accessible.

I. What is Gemma? A “Pocket Encyclopedia” in the AI World

Imagine you have an omniscient super-brain that can answer any question, write articles, and even compose poetry, but this brain needs an entire data center to power it. This is what we often call a “large model.” Gemma, on the other hand, can be metaphorically described as the “mini version” or “pocket version” of this super-brain—it is still incredibly smart, possessing powerful learning and reasoning capabilities, but it can fit into your “pocket” and run on ordinary personal computers, laptops, and even mobile phones.

The Gemma series of models was jointly developed by Google DeepMind and other Google teams. Its name comes from the Latin word “gemma,” meaning “gem.” This aptly summarizes its characteristics: although small, it contains immense value. It stems from the same research and technical foundation as Google’s larger and more complex Gemini models and can be seen as a “smaller, lighter version” of Gemini, but equally powerful.

II. Gemma’s “Superpowers”: Lightweight and Open

The reason Gemma has become a highly anticipated “gem” in the AI field is mainly due to its two core advantages:

1. Lightweight: Letting AI Step Out of the Data Center

Traditional large AI models are like luxury jumbo jets; while powerful, they require huge airports and complex logistical support for takeoff and landing. Gemma, however, is more like a high-performance private jet; it is exquisitely structured, fuel-efficient, and can even take off and land on smaller runways. Gemma models offer different size versions, initially with 2 billion (2B) and 7 billion (7B) parameters, and later introducing more parameter scale choices like 1 billion, 4 billion, 12 billion, 27 billion, etc.

What does this mean? Its “lightweight architecture” allows developers and researchers to run AI applications on various devices, whether personal computers, laptops, workstations, mobile devices, or cloud services like Google Cloud’s Vertex AI and Google Kubernetes Engine. This greatly lowers the barrier to using high-performance AI, as if letting the once unattainable “super-brain” enter ordinary homes.

2. Open Source: Sharing the “Secret Recipe,”Sparking Infinite Creativity

If you want to learn to cook a big dish but the recipe is secret, it’s hard to innovate on your own. Gemma is the “cooking secret recipe” opened up by Google—its model weights are public and allow for commercial use.

“Open Source” means developers can obtain Gemma models for free and modify, customize, and deploy them according to their needs. This is like Google sharing the core technology of building an AI brain with the world, encouraging everyone to innovate and collaborate on top of it. For example, it supports mainstream AI frameworks like JAX, PyTorch, and TensorFlow, and provides a rich toolchain, making model fine-tuning and deployment much more convenient. This openness greatly accelerates the popularization and application of AI technology in various fields, allowing more people to participate in the wave of AI innovation.

III. What Can Gemma Do? From Daily Applications to Scientific Exploration

Gemma’s powerful capabilities are not discounted because of its lightweight nature. In multiple benchmarks, Gemma models, especially the 7 billion parameter version, have even surpassed other larger open-source models, such as Llama 2’s 7B and 13B models, and Mistral 7B, in tasks like mathematics, Python code generation, commonsense understanding, and reasoning.

This gives Gemma broad application prospects in numerous fields, such as:

• Intelligent Assistants: Can be deployed on personal devices to achieve localized intelligent Q&A, content creation, and information summarization.
• Code Generation and Assistance: Helps programmers write code, debug programs, and improve development efficiency.
• Education and Research: Serves as a powerful tool to assist students in learning and support researchers in data analysis and model building.
• Scientific Discovery: Excitingly, Gemma models have already shown great potential in practical scientific research. For example, a Google Gemma AI model once helped scientists discover a potential cancer treatment pathway. This is like a tireless assistant looking for breakthrough clues in complex biomedical data.
• Multilingual Communication: With enhanced support for multilingual capabilities, Gemma can better understand and generate content in over 140 languages, thereby promoting cross-cultural communication and application.

IV. Latest Progress of Gemma: Not Just Lighter, But Stronger and Smarter

Gemma is constantly evolving. Google has recently released a series of important updates, taking its capabilities to a new level:

• Multi-Parameter Family: In addition to the initial 2B and 7B, the Gemma series has expanded to more parameter scales, including versions like 1B, 4B, 12B, and even 27B, as well as Gemma 3n designed for specific scenarios. This means developers can flexibly choose the most suitable model size according to specific needs.
• Leap of Gemma 3: The latest version, Gemma 3 (released in March/April 2025), has made significant breakthroughs in AI capabilities. It adds support for visual language capabilities, able to understand and process image information, just like the brain receiving text and image signals simultaneously. Furthermore, Gemma 3’s mathematical reasoning capability has improved by 60%, and it can support over 140 languages, making it more valuable in fields like financial analysis, data science, and international communication. Google even claims Gemma 3 is “the world’s best single-accelerator model,” optimized to run efficiently on a single GPU or TPU.
• Hardware Optimization: Google actively cooperates with hardware manufacturers like NVIDIA to optimize Gemma’s performance on RTX personal computers, fully leveraging the powerful performance of Tensor Cores to make the model run faster and smoother.
• Continuous Innovation: In June and September 2025, Google also released updates like Gemma 2 (containing 9B and 27B parameter scale models) and VaultGemma (1 billion parameter model), constantly enriching Gemma’s ecosystem.

V. Conclusion: The Road to AI Democratization

The emergence of Gemma models marks an important trend in the field of artificial intelligence—“AI Democratization.” It is no longer the exclusive property of a few top laboratories or tech giants but is open to a broader community through lightweight and open-source forms. This is like complex tools that only professional engineers could use have now become daily tools that ordinary people can easily pick up.

Through Gemma, whether individual developers, small teams, or large enterprises, all can build their own intelligent applications with lower costs and higher flexibility. In the future, we can look forward to this “micro gem,” Gemma, blooming with even more brilliance in various industries, promoting AI technology to integrate more deeply into our lives, and allowing everyone to become a beneficiary of AI innovation.

Gaussian Splatting: Remaking the 3D World with Countless “Glowing Points”

In the digital age, we have become accustomed to enjoying lifelike 2D photos and videos on screens. But how can we make these flat images “come alive” and become scenes that can be freely explored in three-dimensional space? Imagine being able to observe a room from any angle on your computer as if you were there, or even walk in and feel its spatial details. This is the protagonist we are talking about today — Gaussian Splatting, an innovative technology that has taken the field of AI 3D reconstruction by storm.

1. Farewell to “Pixels”, Welcome “Fuzzy Little Balls”

The 2D pictures we see every day, no matter how high-definition, are essentially composed of tiny, square “pixels”. Each pixel has its own color, and together they form the picture. By analogy to the 3D world, traditional 3D models are usually stitched together from thousands of tiny triangular patches (polygons), just like folding a complex shape with many small pieces of paper.

Gaussian Splatting proposes a brand-new way of “building blocks”. It no longer uses fixed pixels or triangles but decomposes objects and scenes in 3D space into countless “3D Gaussian Spheres”, or you can imagine them as “fuzzy little balls“ or “colored cloud clusters“ with color, size, shape, and transparency. 3D Gaussian Splatting is a scene reconstruction and rendering technology based on Gaussian functions. It converts point cloud data in the scene into the form of Gaussian functions to achieve scene modeling and rendering. Specifically, each point is represented as a Gaussian function with mean and covariance. This function describes the color and brightness distribution around the point. By superimposing and rendering these Gaussian functions, high-quality 3D scene images can be generated.

Analogy: If traditional 3D models are like building houses with rigorous bricks, then Gaussian Splatting is more like spraying countless colored, translucent mist clusters in the air with an airbrush. These mist clusters float and overlap in space, and finally converge into a three-dimensional and realistic scene in the eyes of our observers.

2. The Secret of Gaussian Spheres: Not Just a Point

So, what is so special about these “fuzzy little balls”? Each Gaussian sphere is not just a simple point. It contains the following key information, allowing it to accurately “depict” every detail of the 3D world:

1. Spatial Position (XYZ Coordinates): Its specific position in 3D space, just like where this mist cluster “floats”.
2. Size and Shape (Scale and Covariance): It can be a round sphere, stretched into an ellipsoid, or even like a flattened leaf. This determines the spatial range and shape it “covers”. The covariance matrix defines the shape and direction of the Gaussian distribution, like a mold whose shape and angle can be adjusted.
3. Rotation (Quaternion): How this ellipsoidal mist cluster is placed in space: vertically, horizontally, or obliquely.
4. Color (RGB Values or Spherical Harmonics Coefficients): Its specific hue, whether red, blue, or green, can even present different colors depending on the viewing angle.
5. Transparency (Alpha Value): Its degree of transparency, whether completely opaque or looming like a veil.

Analogy: Imagine you are in a dark room holding countless mini flashlights with adjustable size, shape, color, and brightness. You throw these flashlights into every corner of the room; some are round, some are flat, some are bright, and some are dim. When they each emit light and depict all the objects in the room through overlapping, what you see is a three-dimensional scene composed of these “light clusters”.

3. How to “Splat” a Real World?

The reason why Gaussian Splatting is called “Splatting” is that the way it renders images vividly interprets this process. When we need to observe this 3D scene from a certain angle, the system will “splat” all visible and eligible Gaussian spheres onto the 2D image plane in front of us. This process is vividly called “Splatting”.

Specifically, it calculates how each Gaussian sphere is projected onto the screen from the current perspective and cleverly blends them according to their transparency, color, and depth order. Behind this is a set of efficient sorting and blending algorithms (such as Alpha Blending) ensuring that objects close to us correctly occlude distant objects, while semi-transparent objects reveal the scene behind them.

Analogy: It’s like an experienced painter who doesn’t draw the outline of an object first and then fill in the color, but directly splashes colored pigment clusters on the canvas. He knows which pigment cluster should be placed where, which should be transparent, and which should cover another. In the end, these pigment clusters overlap layer by layer to form the realistic painting we see.

4. How Does AI Learn to “Splat”?

The core reason why Gaussian Splatting can be so magical lies in its powerful learning ability. It doesn’t require you to manually place and adjust these Gaussian spheres but completes it automatically through Machine Learning.

1. Input Images: You only need to specific a series of photos or videos of an object or scene from different angles with an ordinary camera.
2. AI Learning: The AI system (usually based on complex optimization algorithms) analyzes these 2D images and tries to “guess” the initial hundreds or thousands of Gaussian spheres in 3D space. This process usually starts from sparse point cloud data (generated by Structure from Motion, SfM) to initialize the 3D Gaussian set.
3. Iterative Optimization: Next, the AI constantly adjusts the position, size, shape, color, and transparency of these Gaussian spheres. It generates an image rendered by the current Gaussian spheres and compares this rendered image with the original input image. Through Stochastic Gradient Descent with L1 and D-SSIM loss functions, the parameters of the Gaussian spheres are optimized. If there is a difference, the AI “knows” that its Gaussian sphere parameters are not accurate enough and need further adjustment.
4. Adaptive Density Control: In areas requiring more detail, AI automatically “splits” into more Gaussian spheres to make local performance finer; while in areas with fewer details, it deletes or merges unnecessary Gaussian spheres to optimize the model and training efficiency.

This process is like an apprentice painter holding a reference photo provided by the teacher (input image) and constantly adjusting his brush (Gaussian sphere parameters) on a blank canvas until the painting he draws is exactly the same as the reference photo, and can even draw realistic scenes from angles not present in the reference photo.

5. Why is Gaussian Splatting So Compelling?

Before Gaussian Splatting, NeRF (Neural Radiance Fields) was a popular technology in the field of 3D reconstruction. Although NeRF can also achieve amazing results, Gaussian Splatting brings significant improvements:

1. Fast Rendering Speed: This is one of its biggest advantages. Rendering a high-quality image with NeRF may take seconds or even longer, while Gaussian Splatting can achieve real-time rendering (over 30 frames per second, even up to 90 frames), which means you can shuttle through the scene very smoothly. This speed advantage gives it huge potential in VR/AR applications.
2. Faster Training Speed: The training time from raw images to generating usable 3D scenes is also greatly shortened for Gaussian Splatting, from hours to minutes or even tens of seconds, while maintaining competitive training times.
3. Higher Reconstruction Quality: It usually captures finer textures and geometric details, generating clearer and more realistic images. It maintains state-of-the-art visual quality while avoiding unnecessary calculations in empty spaces.
4. Enhanced Editability: Although still a research hotspot, the explicit discrete nature of Gaussian spheres makes scene editing (such as dynamic reconstruction, geometric editing, and physical simulation) easier.

Analogy: If NeRF is an exquisite oil painting that requires a supercomputer to slowly draw in the background, then Gaussian Splatting is like a master who has mastered sketching skills. He can draw a work that is equally exquisite and even richer in detail directly in front of your eyes with faster speed and fewer strokes, and you can also ask him to quickly modify a detail in the painting.

6. Application Prospects and Latest Progress

Once Gaussian Splatting was introduced, it quickly captured the attention of academia and industry. Its high-speed and high-quality characteristics have opened up broad application prospects in many fields:

• VR/AR Field: Providing highly realistic virtual environments and immersive experiences, users can freely explore reconstructed real-world scenes without waiting for long loading times. In AR navigation, virtual instructions can be superimposed on real streets with realistic effects.
• Digital Twins and Heritage Preservation: Quickly and accurately creating digital copies of the real world for urban planning, cultural relic restoration, and digital display of cultural heritage.
• Robotics and Autonomous Driving: 3D Gaussian Splatting can be used to build precise 3D environment models to help robots achieve navigation, mapping, and perception functions. In the field of autonomous driving, it can be used to build high-definition maps and perceive the surrounding environment.
• Film and Game Production: Greatly simplifying the creation process of 3D models, reducing costs, and improving efficiency, especially when generating digital assets from real scenes.
• E-commerce and Product Display: Consumers can “touch” and observe product details online from any angle, improving the online shopping experience.
• Human Body Modeling and Animation: Used to generate realistic virtual characters and animation effects.
• Simultaneous Localization and Mapping (SLAM): 3D Gaussian Splatting is reshaping SLAM technology, providing efficient and high-quality rendering of scenes, helping systems position themselves and build maps in unknown environments.

The latest research progress has further expanded the application scenarios of Gaussian Splatting. For example, researchers are exploring how to achieve reconstruction of dynamic scenes, capable of capturing and rendering moving objects or people, such as by modeling the changes in Gaussian property values over time, or converting 3D Gaussians to 4D Gaussians for slice rendering. In addition, the latest “DepthSplat“ model combines Gaussian Splatting with multi-view depth estimation, improving the performance of depth estimation and novel view synthesis. There is also research dedicated to combining Gaussian Splatting with Large Language Models (LLMs) to achieve editing of 3D scenes through natural language descriptions, making them more intelligent.

In summary, Gaussian Splatting is like introducing a new kind of “magic” into the field of 3D reconstruction. With countless “colored fuzzy little balls” that can be finely adjusted, it not only reconstructs the real world but also improves the way and efficiency of our interaction with the digital world. It brings us one step closer to “copying” and “experiencing” the real world, and this is just the beginning.

GShard: The Unsung Hero Behind Huge AI Models, How to Make Giant Intelligence “Fly”?

In today’s rapid development of artificial intelligence, we are delighted to see various powerful AI models emerging one after another. They can write poetry, paint, translate, and even think like humans. But have you ever wondered how these “jumbo” models with hundreds of billions or trillions of parameters are trained? Their size has far exceeded the capacity of a single computer, just like building a skyscraper soaring into the clouds, relying on only a few workers is a fantasy.

The GShard technology proposed by Google in 2020 is the “unsung hero” solving this super problem. It is like a wise engineer and project manager, making the training of massive AI models efficient, feasible, and automated.

1. “Specialized Team”: Understanding the Mixture of Experts (MoE)

To understand GShard, we first need to recognize the core idea it relies on—Mixture of Experts (MoE).

Imagine you have a large consulting firm with business covering law, finance, technology, medicine, and other fields. Countless customers come to your door with various questions every day. If you let a “jack-of-all-trades” handle all problems, he will soon break down, and may not be professional enough in each field.

The idea of MoE is like the operation mode of this company:

• Multiple “Experts”: There are many independent professional teams in the company, such as “Legal Expert Group”, “Financial Expert Group”, “Technology Expert Group”, etc., and each team only focuses on dealing with specific types of problems.
• Smart “Dispatcher”: There is a very smart “dispatcher” (called “Gating Network” or “Router” in AI) at the front desk of the company. When a customer comes with a question, the dispatcher will quickly assess the type of question and direct it to the most suitable one or two professional teams. For example, a question about a company’s IPO will be directly handed over to the “Financial Expert Group” and “Legal Expert Group”, while the “Medical Expert Group” will not be involved at all.

The benefits of doing this are obvious: the customer’s problem receives the most professional answer, and only a small number of experts are used each time, greatly saving the company’s human resources and improving efficiency.

In an AI model, each “expert” is actually a small neural network. When the model receives an input (such as a word in a sentence), the “dispatcher” will judge which “experts” are needed most to handle this input. In this way, a giant MoE model with trillions of parameters actually only activates and calculates billions or even fewer parameters when processing each input, achieving the effect of “large capacity, small computation.” This calculation method that only activates part of the model is called Conditional Computation.

2. “Automatic Division of Labor”: GShard’s Automatic Sharding Technology

After solving the problem of “how to use the expert team more efficiently”, there is still a bigger challenge: even if only some experts are activated each time, the total number of parameters of the entire giant model is still staggering. They simply cannot be stored in the memory of a single computer, nor can all calculations be completed on a single device. It’s like the steel and cement needed for a skyscraper; a single truck simply cannot carry it all, and it requires dozens, hundreds, or even thousands of trucks to transport at the same time.

This is GShard’s second core contribution: Automatic Sharding.

We can imagine a huge AI model as a massive project document, and training the model is making countless revisions and learning on this document. This document is too large for any single computer to open and process at once.

GShard plays the role of a “Wise Project Director”:

• Splitting Tasks: It can automatically and cleverly cut this huge “document” (model parameters) and “revision work” (computing tasks) into countless small pieces.
• Distributing to “Workshops”: Then distribute these small pieces of work to thousands of distributed computing devices, such as high-performance TPUs (Tensor Processing Units).
• Intelligent Coordination: The most impressive thing is that GShard does not require developers to manually write complex code to tell each device which data, which model parts to process, and how to communicate with each other. It provides a set of lightweight “annotation” methods. Developers only need to simply declare some key information, and GShard can automatically plan the best division of labor strategy like an experienced director, and even dynamically adjust during the training process to ensure that all devices work together efficiently, realizing data parallelism and model parallelism.

3. GShard’s “Superpower”: A Milestone of Efficiency and Scale

By cleverly combining Mixture of Experts (MoE) and Automatic Sharding technology, GShard achieved a milestone in 2020: it successfully trained a multilingual translation Transformer model with 600 billion parameters.

You should know that the OpenAI GPT-3 model, known as a “giant” at the time, had 175 billion parameters. The scale of the model trained by GShard far exceeded GPT-3. Even more shockingly, this 600 billion parameter model completed the translation task training from 100 languages to English in just 4 days on 2048 TPU v3 accelerators, and achieved translation quality far exceeding the best level at that time.

This is like a team of hundreds of people efficiently coordinating to complete the design, construction, and interior decoration of a skyscraper in just a few days, which is unimaginable under the traditional model. The secret of GShard lies in that MoE’s conditional computation makes it necessary to “wake up” only a small part of parameters each time, combined with automatic sharding, fully utilizing the parallel capabilities of distributed computing resources, thereby achieving a leap in efficiency for training hyper-scale models.

4. Far-reaching Impact of GShard

GShard is not just a technical detail; it has important milestone significance in the history of AI development. It combines the Mixture of Experts model with large Transformer models in depth for the first time and solves huge engineering challenges in actual training.

The emergence of GShard has laid a solid foundation for the subsequent training of hyper-scale models with trillions of parameters or even higher (such as Mixtral 8x7B, Switch Transformers, etc.) and deeply influenced the development trend of current Large Language Models (LLMs). Its core ideas such as automatic sharding and conditional computation have become standard paradigms for solving model scalability and training efficiency problems in the current AI field.

It can be said that GShard allows us to see the possibility of AI models breaking through single-machine limits and touching broader boundaries of intelligence. It not only demonstrates Google’s strong strength in systems engineering but also opens a door to the era of “Giant Intelligence” for the entire AI community.

title: Gelu Activation
date: 2025-05-04 09:42:41
tags: [“Deep Learning”]

The “Smart Gate” of AI: A Deep Dive into the Gelu Activation Function

In the wondrous world of artificial intelligence, especially deep learning, we often hear profound technical terms like neural networks, gradient descent, attention mechanisms, and so on. Today, we are going to talk about a “widget” hidden deep within neural networks that plays a crucial role—the Gelu Activation Function. It can be metaphorically described as the “smart gate” in a neural network, responsible for deciding the flow and intensity of information.

What is an Activation Function? — The Brain’s “Excitation Threshold”

Imagine our brain’s neurons. When we receive external stimuli (like seeing a flower), this stimulus signal is transmitted to the neuron. The neuron doesn’t just pass on every signal it receives; it has an “excitation threshold.” Only when the received signal strength meets or exceeds this threshold does the neuron get “activated” and pass the signal to the next neuron; otherwise, the signal is “inhibited.”

In the neural networks of artificial intelligence, the activation function plays a similar role. It is a mathematical function located after each layer of neurons in a neural network. Its main functions are:

1. Introducing Non-linearity: If a neural network didn’t have activation functions, no matter how many layers it had, the entire network would ultimately just be a simple linear model, only capable of processing linear relationships. Introducing non-linear activation functions is like giving the model a “magician’s” toolkit, allowing it to learn and recognize more complex, convoluted data patterns (like cats and dogs in images, or emotions in text).
2. Deciding Information Flow and Intensity: The activation function decides whether information should be passed on and at what intensity, based on the strength of the input signal.

Early activation functions included Sigmoid and Tanh, which compress signals into a specific range. Later, the ReLU (Rectified Linear Unit) activation function rose to prominence, gaining popularity for its simplicity and efficiency. ReLU works very directly: if the input signal is positive, it outputs it as is; if the input signal is negative, it outputs zero. This is like a “strict doorkeeper”: positive signals are allowed through, while negative signals are blocked entirely.

Enter Gelu: A “Smarter” Decision Maker

However, ReLU’s “black or white” decision-making style brought some problems, such as the “Dead ReLU” phenomenon (when a neuron’s output is always negative, it stays permanently off and cannot learn). To solve these issues, scientists have continuously explored more advanced activation functions, and Gelu (Gaussian Error Linear Unit) is a standout among them.

Gelu, short for “Gaussian Error Linear Unit,” has shown excellent performance in recent years and has become a standard configuration in many advanced neural network architectures, especially in Large Language Models (LLMs).

The biggest characteristic of the Gelu activation function is its “smoothness” and “probabilistic nature.”

You can understand Gelu this way: it is no longer a simple “on/off” switch, but rather an “intelligent dimmer with emotional coloring” or “a decision-maker that weighs pros and cons.”

• Smooth Transition: ReLU has an abrupt break at the zero point, like a cliff. Gelu, on the other hand, has a very smooth transition curve near zero. This is like a gentle ramp, allowing the neural network to adjust parameters more delicately during learning, avoiding the risk of “accidentally falling off the cliff,” thus making the training process more stable and efficient.

• Probabilistic Weighting: Gelu doesn’t just consider whether the input signal is positive or negative; it also probabilistically weights the signal based on its “magnitude” (i.e., its importance in the data distribution). This is like a “thoughtful filter”:

  • If the signal is very strong and positive (like very important positive information), it will be passed through completely with a high probability, possibly even slightly amplified compared to the original intensity.
  • If the signal is very strong but negative (like very clear erroneous information), it will be inhibited with a high probability, with the transmitted intensity being very small or close to zero, but not fully zero, retaining a sliver of “possibility.”
  • If the signal hovers around zero, ambiguous (like hearing some muttered whispers), Gelu will decide how much intensity to pass based on the signal’s degree of “uncertainty” in a smooth, probabilistic manner. It doesn’t brutally cut off negative signals like ReLU but allows some weak negative signals to pass.

This “probabilistic nature” and “smoothness” allow Gelu to better capture subtle patterns and more complex associations in data.

Why is Gelu Important? — The Unsung Hero of Large Models

The reason Gelu shines in the modern AI field is due to its excellent performance in the following aspects:

1. Promotes Learning of More Complex Patterns: Gelu’s smooth and non-monotonic characteristics allow neural networks to learn more complex non-linear relationships that old-fashioned activation functions found difficult to capture.
2. Improves Training Stability, Reduces Gradient Vanishing: Because its derivative is continuous everywhere, Gelu helps mitigate the common “gradient vanishing” problem in deep learning, allowing error signals to flow better during backpropagation, thereby accelerating model convergence.
3. Cornerstone of Transformer Models: Gelu plays a core role in state-of-the-art Transformer architectures, including the well-known BERT and GPT series models (which are the foundation of modern Large Language Models or LLMs). Its smooth gradient flow is crucial for the stable training and superior performance of these massive models.
4. Wide Application Scenarios: Besides Natural Language Processing (NLP), Gelu is also applied in Computer Vision (like ViT models), generative models (like VAEs, GANs), and Reinforcement Learning, among other fields. This means that whether it’s the intelligent chatbot you are using, the perception system of a self-driving car, medical image analysis, or financial prediction models, the figure of Gelu is likely active behind the scenes.

Conclusion

From simple “on/off” doorkeepers to today’s “smarter” and “more emotionally intelligent” “smart gates” like Gelu, the evolution of activation functions reflects the AI field’s endless pursuit of model performance and training efficiency. Gelu, with its unique smooth and probabilistic weighting mechanism, allows neural networks to understand and process complex information more profoundly, thereby driving the development of frontier AI technologies like Large Language Models. In the future, with the continuous advancement of AI technology, we may see more novel and powerful “smart gates” emerging, working together to build a smarter digital world.

In this day and age, Artificial Intelligence (AI) is like a powerful wave, profoundly changing our lives, from voice assistants on smartphones to recommendation systems; its presence is everywhere. Among the many AI concepts, “GPT“ is undoubtedly the brightest star in recent years. It not only frequently appears in news headlines but has also tangibly entered our daily routines, such as various intelligent chatbots you may have already encountered. So, what exactly is this somewhat mysterious “GPT”? Let’s peel off its technical coat and understand it using examples closest to life.

1. GPT: A Smart Brain That is Super Good at “Speaking”

First, let’s break down the abbreviation GPT:

• Generative: This is not an AI that only nods yes; it can actively create new content, such as writing articles, making up stories, or even writing code.
• Pre-trained: It doesn’t learn from scratch. Before being used by us, it has already read and digested massive amounts of text data, like a super straight-A student who has read all the books in the world in advance.
• Transformer: This is a specific neural network architecture that allows GPT to process and understand language more efficiently and accurately.

Simply put, GPT is a “Transformer” model that has been “Pre-trained” on massive amounts of data and can “Generate” brand new text content.

2. Daily Analogies: How Smart is GPT?

1. Super Upgraded “Predictive Text”:
   Does the input method on your phone intelligently predict the next word when you type? For example, if you type “The weather today is”, it might suggest “good”. GPT is the “ultimate form” of this function. It predicts not just one or two words, but the next entire paragraph, or even a complete article. Based on the beginning you provide, it writes fluently like a top writer, and the content matches the scenario you envisioned highly.

2. A Well-Read “Literary Giant” and “Encyclopedia”:
   Imagine that at the beginning of the universe, there was an extremely diligent student who was given the super power to read and memorize all books, documents, and web pages in the history of human civilization. Not just in English, but also in Chinese, French, Japanese, etc. This student read encyclopedias, novels, poems, news reports, technical papers, dialogue records… all accessible texts.
   GPT is this “student”. Through the “pre-training” stage, it digested almost all public text data on the Internet. It has no consciousness of “understanding” the world, but it has learned the statistical laws of language, the associations between words, how sentences connect, and common expressions for different topics. When it “reads” enough literary works, it can write poetry; when it reads enough code, it can program; when it reads enough dialogues, it can chat with you.

3. An “Editor” with a “Global View”:
   Traditional text processing AI might be like a proofreader who only looks at one word in front of them, finding it hard to understand the context. The core “Transformer” architecture in GPT endows it with an “Attention Mechanism”. This is like an experienced editor who, when looking at an article, not only focuses on the current sentence but can also quickly scan the full text simultaneously, understanding the correlation between different paragraphs and even words far apart. This “global view” allows GPT to maintain better context consistency and logic when generating text, making what it writes more coherent and natural.

3. How Does It “Learn” and “Think”?

Although GPT can generate amazing text, it does not have human thinking ability, feelings, or consciousness. Everything it does is based on statistical patterns and probabilities learned from massive data.

• Massive “Fill-in-the-Blank” Questions: During the pre-training process, GPT is fed a large amount of text, and then some words are deliberately covered. GPT’s task is to predict what the covered words are based on the context. By repeatedly answering such “fill-in-the-blank” questions, it gradually masters the structure, semantics, and common sense of language.
• “Next Word Prediction”: When you ask GPT to write a paragraph, it is essentially playing a prediction game: based on the content already generated and your instructions, predicting what the next most likely word is. Then using this word as the new context, continuing to predict the next word, cycle after cycle. This process is extremely fast, and when choosing words, it comprehensively considers grammar, semantics, logic, and all the knowledge it has learned.

4. Applications of GPT: From “Sci-Fi” to “Daily Life”

GPT technology has been widely applied in various aspects, changing our work and life:

• Intelligent Chatbots: The most intuitive application, capable of smooth, logical, and even creative conversations, answering questions, providing suggestions, and brainstorming.
• Content Creation: Writing articles, press releases, advertising copy, marketing emails, and even novels and scripts. Often, some online content you read might store the shadow of AI behind it.
• Programming Assistance: Helping programmers generate code, debug errors, and explain the functions of complex code.
• Personalized Learning: Acting as an intelligent tutor, providing customized learning content and answers for students.
• Language Translation and Summarization: Converting language more accurately and naturally, or automatically summarizing long articles into concise summaries.

5. Latest Progress and Future Outlook

GPT technology is still developing at a high speed. For example, the GPT-4o model launched by OpenAI has demonstrated stronger multimodal capabilities; it can not only process text but also directly understand and generate image, audio, and video content. This means that the future GPT may not just be a “super writer” but an all-round “digital brain” that can listen, speak, see, and write. In terms of training efficiency, researchers are working on enabling models to achieve better performance with less data and computing resources, such as optimizing model efficiency by improving algorithms and architectures.

Of course, rapid development is also accompanied by challenges. For example, “hallucinations” of AI-generated content (generating seemingly reasonable but actually incorrect information), potential bias (because training data may contain bias), and information security and ethical issues are all difficult problems that scientists and policymakers are striving to solve.

In summary, GPT technology is a milestone in the field of artificial intelligence. With its amazing language generation capabilities, it shows us the huge potential of AI to change the world. Understanding it is understanding the future we are stepping into.

In the field of Artificial Intelligence, Generative Adversarial Networks (GANs) are a fascinating technology capable of creating incredibly realistic data. For non-professionals, understanding this technology might seem a bit abstract, but through daily life analogies, we can easily uncover its mystery.

What is a Generative Adversarial Network (GAN)?

Generative Adversarial Networks (GANs) are a framework in the field of deep learning, proposed by Ian Goodfellow and others in 2014. Its core idea is to let two neural networks compete with each other, thereby continuously improving each other’s capabilities, and finally generating new data that is very similar to real data. Just like its name, “Generative” means it can create new things, while “Adversarial” refers to the competitive relationship between the two networks.

A Cat-and-Mouse Game: Generator and Discriminator

To understand how GANs work, we can imagine it as a “cat-and-mouse” game, or more vividly, a contest between a “counterfeiter” and a “banknote expert”.

1. The Counterfeiter (Generator):
   The goal of this network is to learn how to produce counterfeit money that looks like real money. At first, it might only produce crude counterfeits that can be spotted at a glance. But its task is to constantly learn and improve so that the counterfeit money it produces becomes more and more realistic, hoping to pass it off as real. In AI, the generator starts from random noise (like a pile of random scribbles) and tries to generate data such as images, sounds, or text.

2. The Banknote Expert (Discriminator):
   The task of this network is to identify authenticity. It has some real banknote samples (real data) in hand, and it also receives counterfeit money produced by the counterfeiter. The goal of the banknote expert is to accurately distinguish which are real banknotes and which are counterfeits. It will give each banknote a score; close to 1 means it is a real banknote, and close to 0 means it is a counterfeit.

Adversarial Training Process

These two networks are trained simultaneously and game against each other.

• The Generator is learning how to fool the discriminator so that its generated “counterfeit money” is mistaken by the discriminator for “real money”.
• The Discriminator is learning how to more accurately identify the “counterfeit money” made by the generator and not be deceived by it.

In this endless “cat-and-mouse” process, the counterfeiter will constantly improve its forgery technology to get away with it; while the banknote expert will also constantly hone its identification ability to not be deceived. Finally, when the banknote expert can no longer distinguish between real and fake banknotes, it means that the generator has reached a level of perfection in forgery, and it can now generate highly realistic new data.

Wonderful Applications of GAN

Since its inception, GAN has shown amazing potential in multiple fields:

1. Realistic Image Generation and Editing: One of the most famous applications of GAN is generating images that can pass for real. It can generate pictures based on text prompts, or modify existing pictures, such as converting low-resolution images to high-resolution, turning black and white photos into color, and even changing facial expressions or hairstyles, creating realistic faces, characters, and animals for animation and video. In video games and digital entertainment, it can create immersive visual experiences.
2. Data Augmentation and Synthesis: In machine learning, there is sometimes a lack of sufficient training data. GAN can generate synthetic data with the same properties as real-world data, thereby expanding the training set and helping other AI models learn better. For example, it can generate fraudulent transaction data to train fraud detection systems.
3. Missing Information Completion: GAN can accurately guess and complete missing parts of a dataset based on known information, such as predicting underground structure images, or generating 3D models from same 2D photos or scanned images.
4. “AI vs AI” Defense War:
   With the development of AI technology, technologies such as Deepfake have also been used by criminals for online fraud. GAN can play an important role in the field of cybersecurity by generating various fake data to train defense systems, enabling them to identify and resist more complex cyber attacks. For example, the Hong Kong Monetary Authority launched the GenA.I. Sandbox project in 2024, focusing on exploring “AI vs AI”, using AI technology to detect deepfake fraud and strengthen financial security lines. PAObank, a subsidiary of Ping An of China, has partnered with OneConnect to use AI facial recognition technology to verify user selfies in real-time and detect suspected forged or synthesized faces. This move aims to monitor and prevent potential fraud activities and enhance the bank’s risk management and fraud prevention capabilities.
   Another application is Tesla’s FSD (Full Self-Driving) system, which uses a “Neural World Simulator” trained by AI to generate highly realistic adversarial driving scenarios to test and improve the coping ability of its autonomous driving model.

Challenges and Latest Progress

GAN also faces some challenges during its development, such as training instability and mode collapse (the generator can only generate limited types of data and lacks diversity).

However, researchers have been constantly improving GAN algorithms and architectures. An exciting recent research result (January 2025) shows that by introducing a new loss function and adopting a modern architecture, a minimalist GAN model called “R3GAN“ has been able to solve past problems of training instability and mode collapse. This study found that after sufficiently long training, R3GAN’s performance on image generation and data augmentation tasks can even surpass some mainstream diffusion models, and it is smaller in model size and faster in speed. This progress heralds that GAN technology may usher in a new peak of development and re-demonstrate its strong competitiveness in the field of generative AI.

Conclusion

Generative Adversarial Networks (GANs), with their unique “adversarial learning” mechanism, have brought unprecedented creativity to artificial intelligence. It can not only generate amazing realistic data but also plays a key role in multiple fields such as image processing, data augmentation, and even network security. With the continuous evolution of technology, the future of GAN is full of infinite possibilities, and it will continue to drive AI towards a smarter and more creative future.

Hello! In the vast world of Artificial Intelligence (AI), there are many cutting-edge concepts. The “GES” you mentioned is not a standard, widely known abbreviation for an AI concept. However, based on current hotspots and development trends in the AI field, especially the revolution in information acquisition methods, I guess you might be referring to “Generative Engine Optimization” (GEO), or a specific technology with a similar pronunciation that has not yet become popular.

Considering that you want a popular science article for non-professionals that is easy to understand and uses daily life analogies, I will focus on analyzing the concept of “Generative Engine Optimization (GEO)” for you. It represents a new paradigm of how information is discovered and trusted in the era of generative AI, which is closely related to our past internet usage habits and is very worth exploring.

The “New Navigator” in the AI Era: Generative Engine Optimization (GEO)

Imagine that before you go out every day, you might have been used to looking at a map (like Google Maps) to plan your route and find the best path and destination. This is what “Search Engine Optimization” (SEO) in our traditional Internet era did—it helped websites stand out in numerous search results to be “seen” by you.

However, with the rise of generative artificial intelligence (such as ChatGPT, etc.), our way of acquiring information is undergoing an “earthquake-like” change. Now, you may no longer just look at a map, but ask an omniscient “smart guide” (generative AI) directly: “How do I get from point A to point B? Where are the delicious and quiet restaurants?” This “smart guide” will directly give you a clear answer, or even a complete plan integrating various information and suggestions, instead of just giving you a bunch of links to click and filter yourself.

“Generative Engine Optimization” (GEO) is to enabling your information and content to be quickly and accurately “absorbed” by this “smart guide”—that is, the generative AI model—and become the trusted and cited “high-score answer” when it answers user questions.

From “Being Seen” to “Being Trusted”: The Difference Between GEO and SEO

To better understand GEO, let’s review its “big brother”—SEO.

• Traditional Search Engine Optimization (SEO): Just like you open a small shop, to let more people know about you, you will decorate the storefront beautifully, write eye-catching shop names and main businesses on the signboard, and even distribute flyers at the door. On the Internet, this corresponds to optimizing website content keywords, improving webpage loading speed, obtaining external links, etc. The goal is to make your website rank high on search engine result pages, thereby gaining more “click-through rates.” The core of SEO is to let users “see” you.

• Generative Engine Optimization (GEO): Now, the situation has changed. Your customers are no longer searching aimlessly by themselves but will directly ask their “smart guide.” This guide not only cares if your shop name is loud enough but cares more about whether your shop is genuine and your service is reliable. It will “investigate” your product quality, customer reviews, your history and reputation, and even whether your explanation of the professional knowledge of the goods sold is clear and thorough.

  The core of GEO is to make generative AI “trust” your information and use it as a reliable “citation source” to answer user questions. This means that your content no longer solely pursues “being clicked,” but pursues “being cited,” becoming a part of the AI’s “worldview.”

The “Winning Secret” of GEO: How to Win AI’s “Trust”?

So, how can you make your information stand out in the AI era and become the preferred “citation source” for AI guides? GEO has several key “winning secrets”:

1. Authority and Professionalism (“Expert Certificate and Good Reputation”):
   Your information must be authoritative, professional, and accurate. Just like seeing a doctor, people trust doctors with years of experience and professional qualifications more. For AI, content written by field experts, with reliable data sources and fact-checked, is more likely to be considered authoritative information. AI models will prioritize content that is clearly structured, has fresh data, and is endorsed by third parties, rather than just brand self-descriptions or marketing articles.

2. Structure and Clarity (“Clear Instruction Manual”):
   AI models like “clean, trustworthy, and structured” data and information. Think about it: which is easier to understand, a messy, patched-together manual, or a manual with clear titles, clear paragraphs, and highlighted points? The same goes for AI. Explaining core topics clearly, answering questions directly at the beginning, and using structured formats such as lists, tables, and FAQs (Frequently Asked Questions) all help AI better understand and extract your information.

3. Objectivity and Freshness (“Real-time News and Fair Reporting”):
   AI pursues objective and latest information. A report that is updated in a timely manner and reflects the latest progress and viewpoints is more valuable than old materials from years ago. AI models do not sort by “popularity” but assess by “usability.” This means that your soft articles and marketing content will not be cited; only precise, professional, objective, and fresh content can stand out.

4. Explainability and Transparency (“Why do it this way, I can tell you”):
   This is a significant challenge facing generative AI; many models are called “black box models,” and their decision-making processes are hard to understand. GEO encourages content creators to provide more background information, reasoning processes, and data sources, so that when AI generates answers, it can also transparently explain the source and basis of its information. It’s like when you recommend a dish, you not only tell others it’s delicious but can also name its ingredients, cooking methods, and taste characteristics, making it more convincing.

The Real Impact of GEO: Reshaping the Information World

The emergence of GEO is profoundly changing the way we acquire information and how businesses market themselves.

• For Content Creators: It is no longer about blindly pursuing traffic and clicks, but returning to the value of the content itself, producing high-quality, trustworthy, and clearly structured in-depth information.
• For Businesses and Brands: Traditional advertising and SEO still have their value, but in the AI-dominated information flow, winning AI’s “trust” will become the new high ground of competition. For example, a startup doing compliance automation created structured topic pages (such as “What is SOC 2 Automation”, “Implementation Timeline”, “Common Misconceptions”) and was cited by large models 8 weeks later. Even though website traffic did not change significantly, demo request volume rose by 30%. This shows that in the AI era, the conversion efficiency brought by “being cited” and “being trusted” is higher.
• For Ordinary Users: We will get more direct, precise, and authoritative answers, and no longer need to filter through search results like looking for a needle in a haystack.

In summary, Generative Engine Optimization (GEO) is the new rule of information dissemination in the AI era. It reminds us that today, as artificial intelligence becomes increasingly smart, returning to the essence of content—providing valuable, trustworthy, and easy-to-understand information—is the key to winning the future. Just like your “smart guide” can give you the best advice provided that these suggestions come from a trustworthy “internal knowledge base,” GEO is exactly what helps your information become an import member of this knowledge base.

I hope this popular science article on “Generative Engine Optimization (GEO)” helps you better understand this important concept in the field of AI.

Fréchet Inception Distance (FID): The “Sharp Eye” for AI Generated Image Quality

With the rapid development of artificial intelligence technology, the ability of AI to generate images has become increasingly powerful. Whether it is faces, landscapes, or artistic paintings, they have reached a level of realism that can pass for genuine. However, as viewers, we can judge the quality of a picture with the naked eye, but for the AI model itself, how does it know that the image it generates is realistic enough and diverse enough? This requires an objective “referee” — Fréchet Inception Distance (FID).

FID is a key metric widely used to evaluate the quality of images generated by generative models (especially Generative Adversarial Networks or GANs, and Diffusion Models). Simply put, the lower the FID value, the closer the AI-generated images are to real-world images, indicating higher quality and better diversity.

Why is it So Hard to Judge AI Image Quality?

In the field of image generation, assessing the quality of generated images solely by pixel-to-pixel comparison is far from enough. Imagine you took two almost identical photos with a camera, but one shook slightly, blurring just a tiny bit. If you compare them pixel by pixel, you will find a huge difference between the two photos because the brightness value of each pixel has changed. But from human perception, they are still the “same photo,” just with slightly different quality. For AI, an image with completely different pixels can look very realistic, which is what we want.

Traditional image evaluation methods, such as calculating the Mean Squared Error (MSE) of pixels between two images, are like asking a child to recite two pages of a text, failing them even if they get one word wrong. But this ignores the more important “meaning groups” and “understanding.” For highly complex image generation tasks, this approach is too harsh and inaccurate. We need a measurement standard that can understand the “content” and “style” of the image.

FID: A “Art Critic” with Unique Insight

The ingenuity of FID lies in that it no longer compares images pixel by pixel, but measures the similarity between real images and generated images from the level of feature distribution. We can compare the calculation process of FID to an experienced art critic evaluating a batch of real paintings and a batch of AI-created paintings.

Step 1: Feature Extractor — Inception Network as the “Art Critic”

First, we need a tool that can understand the “connotation” of the image. FID borrows the Inception V3 network developed by Google. This network is like a senior art critic who has seen countless paintings. Through learning massive amounts of real images, it has already formed an understanding of high-level semantic information such as image content, structure, texture, and color.

When we show an image to the Inception network, it doesn’t tell you which pixels make up the picture but extracts a series of “feature vectors.” These vectors are equivalent to the critic’s “style description” or “artistic essence summary” of a painting, such as “This painting depicts a sunny beach, with bright colors, unconstrained brushstrokes, and full of holiday atmosphere.” Whether the picture is real or AI-generated, it summarizes it in the same way, forming a high-dimensional “artistic portrait” or “fingerprint.”

Step 2: Style Portrait — Building Statistical Models of “Art Genres”

After obtaining the “artistic portraits” of a large number of real paintings and AI paintings, we do not compare them one to one. Instead, we perform statistical analysis on these two batches of paintings separately.

This is like an art critic summarizing the characteristics of two “art schools” after appreciating hundreds of real paintings and hundreds of AI paintings:

1. Realism School: What is the “average style” of their works? How is the “style diversity” of the works? Some are realistic, some are abstract; how great is this degree of diversity?
2. AI School: What is the “average style” of AI works? How is its “style diversity”?

Mathematically, these “artistic portraits” are assumed to follow a multivariate Gaussian distribution. We calculate the Mean (Average Style) and Covariance Matrix (Style Diversity) for each school. The mean represents the center position of the batch of images in the feature space, while the covariance matrix describes the range of variation and correlation of these features, that is, their diversity.

Step 3: Measuring Distance — Fréchet Distance Measures “Imitation Skill”

Finally, we use the Fréchet Distance to measure the difference between these two “art genres.” The Fréchet Distance measures the distance between two Gaussian distributions. It figuratively answers the question: “How much ‘effort’ is required to ‘transform’ the average style and style diversity of the Realism School to the average style and style diversity of the AI School?”

If the “average style” of the AI ​​School is very close to the Realism School, and its “style diversity” also highly aligns with the Realism School, then the “effort” required is very small, and the FID value will be very low. This indicates that the AI-generated images are highly consistent with real images in terms of overall style and diversity, and the generated quality is better. The smaller the FID value, the closer the quality and diversity of the generated images are to real images; 0 is the theoretical best value.

Why is FID So Good?

1. Closer to Human Perception: FID does not simply compare pixels but uses a pre-trained deep learning network to extract semantic features. These features represent the high-level semantic information of the image better than raw pixel values, making the evaluation results of FID more consistent with human visual judgment.
2. Measuring Overall Distribution: It compares the feature distribution of two image sets, not just individual images. This is crucial for generative models because the goal of generative models is to learn and replicate the overall distribution of real data, not just to generate a few realistic pictures. FID effectively captures both image quality and sample diversity.
3. More Robust: FID is sensitive to quality degradation like blur and noise in images, better reflecting subtle defects in generated images.

Limitations and Future Outlook of FID

Although FID is currently one of the most widely used and standardized metrics for assessing image generation models, applied to evaluate advanced models including StyleGAN and Stable Diffusion, it also has some limitations:

• Gaussian Distribution Assumption: FID assumes that feature vectors follow a Gaussian distribution, which may not be completely accurate in some cases, thereby affecting the accuracy of the assessment.
• Large Sample Size Requirement: FID requires a sufficient number of image samples to perform stable and accurate estimation (usually at least 10,000 images are recommended), which can be computationally expensive and time-consuming for high-resolution images.
• Not Completely Perfect: In some specific cases, FID may not be completely consistent with human judgment.

Because of these limitations, researchers are constantly exploring new evaluation metrics and methods. For example, some have proposed using the embedding features of the CLIP (Contrastive Language–Image Pre-training) model to replace Inception features to calculate distances, so as to better evaluate the generation effect of text-to-image models. In addition, KID (Kernel Inception Distance), CMMD, VQAScore, and combined metrics like Precision/Recall are also being studied and applied, aiming to evaluate the performance of generative models more comprehensively from different dimensions. While FID excels at assessing “whether the image is real,” metrics like CLIP Score focus more on assessing “whether the image is semantically consistent with the input text description.”

In summary, Fréchet Inception Distance (FID), as the “sharp eye” for measuring the quality of AI-generated images, provides us with an objective, effective assessment tool highly correlated with human perception through its unique feature extraction and distribution distance calculation methods, greatly promoting the development of the image generation field. Although it is not flawless, it remains one of the most reliable indicators for judging the quality of AI “paintings” today.

The Eye of Intelligence: A Deep Dive into Faster R-CNN, How AI “Sees” the World

Imagine walking into a room and instantly recognizing a cup on the table, a remote control on the sofa, and a painting on the wall. This ability to “identify” and “locate” objects in the environment is effortless for humans, but obtaining it has been a huge challenge for Artificial Intelligence. In the field of computer vision, a milestone technology called Faster R-CNN has endowed AI with this “sharp eye,” enabling it to quickly and accurately identify various objects in an image and frame their positions.

Faster R-CNN (Faster Region-based Convolutional Neural Network) is one of the most classic and influential algorithms in the field of Object Detection. It not only reached the top level of accuracy at the time but also achieved a breakthrough in speed, making real-time object detection possible. To understand the ingenuity of Faster R-CNN, let’s start with its “predecessors.”

1. From “Needle in a Haystack” to “Preliminary Screening”: The Birth of R-CNN

Before Faster R-CNN appeared, for AI to recognize objects in pictures, it was like looking for a needle in a haystack. It needed to try countless possible “box” regions in a picture, and then send the content of each box for analysis to determine whether there was an object inside and what object it was.

R-CNN (Region-CNN) is an early representative of this idea. Its workflow can be roughly analogized as:

1. “Audition” Regions: First, using a traditional image processing technique called “Selective Search,” like a diligent scout, it draws about 2,000 candidate regions (Region Proposals) that may contain objects on the image. You can imagine drawing thousands of boxes of different shapes and sizes on a photo, guessing where things are.
2. “Review One by One”: Then, it crops these 2,000 candidate regions one by one, adjusts them to a uniform size, and sends them into a powerful Convolutional Neural Network (CNN) for feature extraction. This CNN is like an experienced appraiser who can extract highly abstract “features” from image areas, such as edges, textures, shapes, etc.
3. “Classification Judgment”: Finally, the extracted features are sent to a classifier (usually a Support Vector Machine, SVM) to judge what object is in this area (such as a cat, a dog, or the background), and another regressor is used to correct the position of the box so that it frames the object more accurately.

The Pain Point of R-CNN: Although this method is effective, it is inefficient. Because it needs to perform CNN feature extraction on 2,000 candidate regions separately, this leads to a huge amount of calculation and very slow speed; a single image may take tens of seconds to process. It’s like 2,000 people lining up, and everyone has to go through a complex physical examination from start to finish. You can imagine the efficiency.

2. Speed Up! Making “Screening” and “Review” More Efficient: Fast R-CNN

To solve the slow speed problem of R-CNN, the subsequent Fast R-CNN made major improvements. Its core idea is: since each candidate region needs to go through CNN to extract features, why not let the entire image do CNN feature extraction only once?

You can compare Fast R-CNN to:

1. “Overview, One Scan”: It first inputs the entire image into the CNN, “scanning” the image once like a scanner to generate a “Feature Map” containing all visual information. This feature map is like a highly condensed image summary containing feature information of all areas of the original image.
2. “Smart Cropping, Sharing Results”: Then, the candidate regions generated by “Selective Search” no longer need to be cropped from the original image but are directly mapped to this feature map. A layer called RoI Pooling (Region of Interest Pooling) is used to extract fixed-size feature vectors of the corresponding regions from the feature map. This process is like “snipping” only the summary area of the corresponding news from a complete newspaper summary and standardizing the size for subsequent analysis. This avoids repeating CNN calculations for each candidate region.
3. “Multitasking Expert”: The extracted features are then sent to the fully connected layer for classification and bounding box regression. Fast R-CNN uses a multitasking loss function that can simultaneously predict object categories and precise bounding box positions, and replaces the SVM classifier in R-CNN with a neural network, realizing end-to-end training.

The Bottleneck of Fast R-CNN: Although Fast R-CNN greatly improves speed, it still relies on external “Selective Search” to generate candidate regions. This “Selective Search” process itself remains time-consuming and becomes the efficiency bottleneck of the entire system. This is like the efficiency of each person’s examination in the physical examination process has improved, but the process of taking a number and lining up (generating candidate regions) is still as slow as a snail.

3. Disruptive Innovation: Faster R-CNN’s “Insight”

At this point, the long-awaited protagonist Faster R-CNN appears! Its biggest innovation lies in completely saying goodbye to the traditional time-consuming “Selective Search” and introducing a brand-new Region Proposal Network (RPN) based on deep learning. This means that the step of generating candidate regions is also completely integrated into the neural network, achieving true End-to-End learning and detection.

We can compare Faster R-CNN to an intelligent system with “insight”:

1. “Insight into the Whole, Refining the Essence”: First, the picture also passes through a shared CNN network (usually powerful pre-trained models like VGG, ResNet, etc.) to extract the “feature map” of the entire image. This remains that highly condensed image summary.
2. “Smart Assistant, Pre-judging Targets”: This feature map is then sent to the RPN. The RPN is like an experienced “smart assistant.” It does not blindly generate all possible regions like “Selective Search.” Instead, it scans the feature map using a sliding window. Based on preset Anchor Boxes (preset boxes of different sizes and aspect ratios), the “smart assistant” can predict which areas are most likely to contain objects and perform a preliminary bounding box adjustment on these potential object areas. At this stage, it only judges whether the area is an object (yes or no, foreground or background), not what specific object it is.
   • Anchor Boxes: Can be understood as a batch of “template boxes” preset on the feature map. They have different sizes and aspect ratios, covering all possible locations and shapes where objects may appear on the image. RPN will predict the precise location of the object based on these templates.
3. “Unified Standard, Detailed Review”: After the RPN screens out some high-quality candidate regions, these regions again pass through the RoI Pooling layer to extract fixed-size feature vectors from the shared feature map. It’s like unifying the “specifications” of the potential target areas picked out by the smart assistant, making it convenient for the next step expert to examine carefully.
4. “Senior Expert, Precise Positioning”: Finally, these standardized feature vectors are sent to a classifier and bounding box regressor (called the Fast R-CNN Detector). Like a senior expert, it finally determines what object is in each area (specific category) and fine-tunes the bounding box more precisely to get the final detection result.

Why is it called “Faster”?
The key lies in the RPN. It turns the traditionally time-consuming region proposal process into an end-to-end trainable neural network. This means that the work of RPN and the entire detection network can share the features extracted by the same CNN, and both can be trained simultaneously, forming a unified and efficient system. In this way, the speed of generating candidate regions is increased from seconds to milliseconds, enabling the entire object detection model to achieve near-real-time speeds.

4. Applications and Future of Faster R-CNN

Since its proposal in 2015, Faster R-CNN has rapidly become the cornerstone of the object detection field. Its innovative architecture and excellent performance have made it shine in numerous practical applications.

• Autonomous Driving: Identifying pedestrians, vehicles, and traffic signs is key to the safe operation of autonomous cars. Faster R-CNN and its subsequent improved models can accurately perceive surrounding objects in complex and changing driving environments.
• Security Surveillance: Automatically detecting abnormal behaviors, recognizing faces, and tracking suspicious persons or items in surveillance videos greatly improves the intelligence level of security systems.
• Medical Image Analysis: Assisting doctors in detecting tumors and lesions in medical images such as X-rays, CTs, and MRIs, improving the accuracy and efficiency of diagnosis.
• Industrial Inspection: Automatically detecting product defects and counting on production lines, improving the automation and quality control level of industrial production.
• Robotics and Drones: Helping robots and drones identify objects in the environment for obstacle avoidance and grasping operations.

Although a series of faster or more powerful object detection models such as YOLO, SSD, and DETR have emerged since Faster R-CNN, Faster R-CNN remains an important benchmark for evaluating the performance of new algorithms. Research in 2024 and 2025 continues to optimize Faster R-CNN, such as integrating Vision Transformers as backbones, adopting deformable attention mechanisms, and improving multi-scale training and feature pyramid designs to further enhance its performance. Its philosophy and architecture have had a profound impact and are an indispensable part of understanding modern object detection technology.

In summary, Faster R-CNN is like opening a window for machines, allowing them to not only “see” images like humans but also “understand” what is in the images and where they are. This is undoubtedly a colorful stroke on the road of artificial intelligence development.