Title: UL2
Tags: [“Deep Learning”, “NLP”, “LLM”]

The “All-Around Leaner” in AI: A Deep Dive into the UL2 Model

In the vast universe of Artificial Intelligence, Large Language Models (LLMs) are undoubtedly among the brightest stars. They can write poetry, code, and converse. But have you ever wondered how these models initially “learn” knowledge? Just as students have different learning methods, AI models also have various pre-training paradigms. However, different paradigms often have their own strengths and weaknesses. Against this background, the Google Research/Brain team proposed an innovative framework called UL2 (Unifying Language Learning paradigms), aimed at creating a more “all-around” AI learner.

Why Do We Need UL2? — The “Subject Bias” Problem in AI Learning

Imagine you have a classmate who is excellent at reciting textbook knowledge and can clearly remember historical events and scientific principles (corresponding to T5-like models that excel at understanding and classifying information). But when you ask him to be creative and write a novel, he might be at a loss. On the other hand, you might have a classmate who is imaginative and has a brilliant literary style, easily writing beautiful prose, but when asked to answer a math problem precisely, he might not be rigorous enough (corresponding to GPT-like models that excel at open-ended generation and in-context learning).

In the training of large language models, a similar “subject bias” phenomenon exists. Traditional language model pre-training methods either excel at learning knowledge through “fill-in-the-blank” tasks like the T5 series models and perform well in fine-tuning specific tasks, or excel at learning by “predicting the next text given the previous text” like the GPT series models, shining in open-ended text generation and few-shot learning. However, rarely does a model perform well across multiple types of tasks simultaneously to achieve universal effectiveness. UL2 was born to solve this problem, aiming to establish a unified language model that is universally effective across different datasets, tasks, and settings.

UL2’s Core Secret: Mixture-of-Denoisers (MoD)

The most core innovation of UL2 lies in its unique pre-training objective — “Mixture-of-Denoisers” (MoD). We can imagine MoD as a smart student who doesn’t use just one method to learn but flexibly employs multiple learning strategies based on the learning content and goals. In UL2, these “learning strategies” are embodied in three main denoising tasks:

1. R-Denoiser (Regular Denoising): Just like the “correct the typos in the sentence” or “fill in the ellipsis with suitable words” exercises given by elementary school language teachers. The model is asked to recover standard-length masked spans in the text. This task helps the model efficiently acquire a vast amount of knowledge and understand the local semantics of text.

2. S-Denoiser (Sequential Denoising): This is like asking you to complete the ending of a story or write a coherent paragraph following the previous text. In this mode, the model is asked to generate the subsequent text sequence based on a given prefix (or starting part). It emphasizes the sequentiality and coherence of text, making it very suitable for learning to generate fluent text.

3. X-Denoiser (Extreme Denoising): This is the most challenging way of learning. Imagine you only receive a few keywords or one or two sentences of an article but are asked to summarize and retell the content of the entire article. X-Denoiser requires the model to recover most or even all of the input text from a very small amount of information, which implies the model needs deeper understanding and stronger generation capabilities to generate coherent and longer text from limited context.

During the pre-training phase, UL2 mixes these three different intensities of denoising tasks according to a certain ratio. This “mixed teaching” exposes the model to various types of challenges during the learning process, thereby cultivating comprehensive and balanced abilities, mastering detailed knowledge while also being capable of creative generation.

Mode Switching: Wisdom of Teaching According to Aptitude

Another ingenious feature of UL2 is the introduction of the concept of “Mode Switching.” This is like an experienced teacher who knows how to guide students to adopt different answering strategies for different types of exams. In UL2, when the model is fine-tuned for downstream tasks, it can be actively told which ability cultivated by which denoising mode the current task favors by adding a special “paradigm token” (e.g., [R], [S], [X]).

For example, when facing a summarization task that requires precise information extraction and classification, the model might be prompted to use the skills learned under the R-denoising mode; while when open-ended dialogue generation is needed, it might switch to the direction S-denoising excels in. This dynamic mode switching allows UL2 to flexibly adapt to the needs of various tasks, fully utilizing the diverse skills acquired during the pre-training phase.

UL2’s Extraordinary Achievements and Application Prospects

Since its proposal, UL2 has demonstrated remarkable capabilities. A UL2 model with 20 billion parameters outperformed the GPT-3 model with 175 billion parameters at that time on the zero-shot SuperGLUE benchmark; in one-shot summarization tasks, its performance was double that of the T5-XXL model. This is like a team of 20 people in a class, cultivated through comprehensive learning methods, defeating a team of 175 people focused on single-item training in a comprehensive ability test, and being more efficient on specific tasks.

UL2 has shown excellent performance in multiple natural language processing tasks such as language generation, language understanding, information retrieval, long text understanding, question answering systems, few-shot learning, and even chain-of-thought prompting. Google has also open-sourced the 20 billion parameter UL2 model checkpoint and the instruction-tuned Flan-UL2 model. This means researchers and developers can use this powerful “all-around learner” to empower various practical applications, such as:

• Intelligent Customer Service: More accurately understanding user intent and generating more personalized and effective responses.
• Content Creation: Assisting or even automatically generating various forms of text such as news reports, novels, and scripts.
• Information Retrieval and Summarization: Quickly extracting key content from massive information and generating concise summaries.
• Scientific Research: Assisting researchers in understanding complex literature and conducting knowledge reasoning.

Even in 2025, UL2 is still used as one of the benchmarks for performance evaluation and compared with newer models, which is enough to illustrate its importance and influence in the field of AI language models.

Conclusion

The UL2 model, with its unified pre-training paradigm of “Mixture-of-Denoisers” and the flexible mechanism of “Mode Switching,” is like an all-around AI student who has shaken off the “subject bias” problem of traditional models. It not only demonstrates excellent performance but, more importantly, points out a new path for us to understand how to build more general and powerful AI language models. As AI technology continues to develop, concepts dedicated to “unified learning” like UL2 will become a key step in propelling artificial intelligence towards higher-level intelligence.

Title: VQ-VAE
Tags: [“Deep Learning”, “Machine Learning”]

Decoding “Discrete Aesthetics”: An Easy-to-Understand Guide to VQ-VAE

In the wonderful world of Artificial Intelligence, enabling machines to understand and create images, sounds, and even text is a dream pursued by countless scientists and engineers. Among them, Generative AI models play increasingly important roles. Today, we are going to talk about a very key and creative concept in the field of Generative AI — VQ-VAE.

You might find this combination of letters a bit unfamiliar, but don’t worry, we will use examples from daily life to take you easily into this AI algorithm full of “discrete aesthetics.”

Starting from “Compressed Files”: Autoencoder (AE)

Imagine you have a lot of high-definition photos taking up a vast amount of storage space. You hope to compress them to save space but still be able to basically restore their original appearance when used. This is the basic idea of an “Autoencoder” (AE).

An Autoencoder consists of two parts:

1. Encoder: It acts like professional compression software, transforming a complex original photo (high-dimensional data) into a shorter, more concise “compression code” or “summary” (low-dimensional latent variable) containing its main information.
2. Decoder: It acts like decompression software, receiving this “compression code” and attempting to restore it to the original photo.

The goal of training an autoencoder is to make the photo restored by the decoder as similar as possible to the original photo. In this way, the “compression code” produced in the middle represents the core features of the original photo.

Endowing with “Imagination”: Variational Autoencoder (VAE)

Ordinary autoencoders have a drawback when generating new content: they only restore the “compression codes” they have “seen.” If you give it a random “compression code” it hasn’t seen, it might be “confused” and not know how to generate a meaningful image.

To solve this problem, scientists introduced the “Variational Autoencoder” (VAE). The core improvement of VAE is that it doesn’t just compress data into a “summary,” but compresses data into a “probability description” of the “summary.” For example, if an ordinary autoencoder compresses a picture of a cat into “this is a cat,” VAE would say: “This is likely a black cat, but it could also be a white cat, or a tabby cat, and their features are distributed roughly like this.”

In this way, VAE encourages the “imagination space” (called “latent space”) where its “probability descriptions” reside to become regular and continuous. Thus, we can randomly draw a “probability description” from this regular “imagination space” and let the decoder “imagine” and generate a brand new, meaningful image.

However, traditional VAE sometimes produces blurry images when generating images. This is because its “imagination space” is continuous, and the model might blur the transition between different “concepts” during the generation process, just like colors on a palette transition infinitely smoothly, but sometimes we need clear, discrete blocks of color.

From “Continuous Palette” to “Precise Color Card”: The Emergence of VQ-VAE

This is the moment for today’s protagonist — VQ-VAE (Vector Quantized Variational Autoencoder) — to enter the scene! Building upon VAE, VQ-VAE introduces a revolutionary concept: Vector Quantization, which changes the model’s “imagination space” from continuous to discrete.

We can understand it with a vivid metaphor:
Imagine you are a painter.

• Traditional VAE is like giving you a continuous palette with infinite colors that can be mixed at will. Although theoretically any color can be painted, sometimes it is difficult to accurately capture and reproduce a specific, clear color, easily resulting in works with “hazy beauty.”
• VQ-VAE is like giving you a selected “color card book” or “pigment library”. This color card book contains a series of predefined, limited but very representative standard colors (e.g., pure red, pure blue, emerald green, azure, etc.).

In summary, the working principle of VQ-VAE is:

1. Encoder: Like AE and VAE, compresses the input image (or other data) into an internal representation.
2. Quantization Layer and Codebook: This is the most unique part of VQ-VAE.
   • Codebook can be understood as the aforementioned “color card book” or “pigment library.” It is a dictionary composed of a large number of different “standard concepts” or “color vectors” (called embedding vectors).
   • The internal representation generated by the encoder will perform a “nearest match” here. In other words, the model will find the “standard color” or “concept vector” from your “color card book” that is most similar (closest distance) to the encoder output to represent it. This process is “Quantization.”
   • Ultimately, what is passed to the decoder is no longer a continuous, blurry vector, but a clear, discrete “color card number” or “concept ID.”
3. Decoder: Receives the “standard color” corresponding to this “color card number” and then uses it to reconstruct the image (or other data).

It is like we use words to describe things; every word (like “cat,” “dog,” “tree”) is a discrete concept. VQ-VAE uses this discrete representation to make generated images clearer with sharper boundaries, avoiding the blurriness that traditional VAE might produce.

VQ-VAE also solves the problem of “codebook collapse” through ingenious training methods. Imagine your “color card book” has many colors, but you only use a few every time you paint. This leads to many pigments being wasted. The mechanism of VQ-VAE encourages the model to fully utilize all “standard colors” in the “color card book,” giving every “concept” a chance to be used, thereby ensuring the diversity and richness of generated content.

Practical Applications and Future Impact of VQ-VAE

The discrete latent space representation of VQ-VAE has brought many exciting applications:

• High-Fidelity Image Generation: VQ-VAE and its upgraded version VQ-VAE-2 perform excellently in generating high-quality, detail-rich images. They can decompose complex images into discrete codes like “visual vocabulary,” providing a powerful foundation for subsequent generative models (like Transformers). The famous AI image generation model DALL-E utilizes ideas similar to VQ-VAE to learn discrete representations of images, thus being able to generate various fantastic images based on text descriptions.
• Audio Generation: Besides images, VQ-VAE is also applied in the audio field. For example, OpenAI’s Jukebox uses VQ-VAE to compress raw audio into discrete codes and then uses these highly compressed representations to generate music of various styles, including vocals with lyrics.
• Combination with Other Models: VQ-VAE is often used in combination with models like Transformers. VQ-VAE encodes images or audio into discrete “sequences,” while Transformers excel at processing sequence data, thus better understanding and generating these complex modalities. It can even be combined with Generative Adversarial Networks (GANs) to generate more realistic images and audio.

Conclusion

As a technology that cleverly compresses data into a discrete latent space, VQ-VAE has brought a brand new “discrete aesthetic” to Generative AI. It not only solves the blurred generation problem in traditional VAE but also lays an important foundation for subsequent more complex generative models (like text-to-image models such as DALL-E). Through the analogy of a “color card book,” it is not difficult to understand that it is this transformation from infinite to limited, from continuous to discrete, that has allowed AI to take another solid step forward in its ability to understand and create this world. Its core ideas and mechanisms have also inspired countless subsequent generative models. With the continuous development of AI technology, models like VQ-VAE will continue to push the boundaries of our imagination regarding machine creativity.

Title: U-Net
Tags: [“Deep Learning”, “CV”]

Demystifying U-Net: How AI Acts Like a Puzzle Master to Precisely “Cut Out” Images

In the vast universe of artificial intelligence, technologies like image recognition and object detection have become commonplace. But have you ever wondered how we might achieve it if we need AI not only to identify what is in an image but also to precisely know the contour and scope of this “what,” just like flawlessly “cutting out” a specific object from the image with scissors? This technology is known as “Image Segmentation” in the AI field, and U-Net is the outstanding “puzzle master” that accomplishes this delicate task.

Especially in fields requiring extreme precision such as medical image analysis, U-Net (U-shaped Network) emerged out of nowhere. With its unique structure and superior performance, it has become a bridge connecting AI with the real world. It was originally proposed by researchers at the University of Freiburg, Germany, in 2015, specifically for biomedical image segmentation, and it performs excellently even with limited training data.

What is Image Segmentation? — AI’s Precise “Cutout” Technology

Imagine you have a family photo, and now you want to mark your grandfather, grandmother, father, mother, and yourself with different colors, instead of simply identifying that “there are people.” Image segmentation does exactly this: it assigns a class label to every pixel in the image. For example, in medical imaging, it can distinguish between tumor tissue, healthy tissue, and blood vessels; in autonomous driving, it can identify roads, vehicles, pedestrians, and lane markings.

U-Net’s Secret Weapon: Unique “U” Shaped Structure

U-Net is so named precisely because the shape of its network structure resembles the letter “U”. This “U” structure contains two core paths that work synergistically to complete the detailed segmentation of the image.

1. The Left Half: Encoder Path — Seeing the Forest and the Trees

Imagine you are an experienced detective who receives a complex street view photo with the task of finding all “red sedans” in the photo. What would you do?

First, you might take an overall look at the photo to quickly grasp some macro information: Oh, this is the city center, there is a traffic jam there, and there is a tall building in the distance. This process is like the left half of U-Net — the Encoder Path. Through a series of “convolution” and “downsampling” operations, it gradually reduces the size of the input image while extracting higher-level, more abstract feature information from the image.

• Convolution: Like a detective using a magnifying glass to check different areas of the photo, looking for specific patterns or clues (such as the shape and color of vehicles).
• Downsampling: Like gradually zooming out from a high-resolution large map to a low-resolution small map. Although the details are blurred, you can more easily see the overall layout and key macro information.

At this stage, U-Net learns to identify “big concepts” in the image, such as “there might be a car here” or “this area is the background.” It captures the contextual information of the image.

2. The Right Half: Decoder Path — Precise Positioning from Macro to Micro

The detective now knows roughly where the “cars” are, but where exactly are the boundaries? Which car is it? What is the contour of this car?

To answer these questions, the detective needs to switch to the right half of U-Net — the Decoder Path. The task of this path is to gradually restore the reduced feature map to the size of the original image while using the macro information learned in the encoder path for pixel-level precise classification.

• Upsampling: Like the detective taking the rough location from the small map and switching back to the high-resolution large map, gradually zooming in and positioning precisely. It gradually enlarges the size of the feature map to restore the detail information of the image.
• Convolution: After each upsampling, convolution operations are also performed to refine the reconstructed image details.

This stage focuses on precise positioning, restoring the “big concepts” identified in the encoder path to pixel-level fine segmentation results.

3. The Crucial “Bridge”: Skip Connections — Communication That Misses No Detail

At this point, you might think: in the encoder path, we sacrificed a lot of image details to see the “big picture.” So, when restoring details in the decoder path, will some important tiny features be missed or mistaken? This leads to U-Net’s most ingenious design — Skip Connections.

Imagine that while the detective was zooming out from the large map to the small map, although seeing the rough area, he also recorded some very critical unique details about the shape of the “red sedan,” such as the license plate number and unique headlight shape, in a small notebook beside him. When he zooms back in to find details, he will refer to the original details in these small notebooks to ensure no mistakes are made.

In U-Net, skip connections are like these “small notebooks.” They directly “skip” the intermediate layers and transmit the feature maps before each downsampling step in the encoder path to the corresponding upsampling layer in the decoder path. In this way, when the decoder path reconstructs image details, it can not only use the abstract semantic information obtained from deep layers but also directly access the rich spatial detail information preserved in shallow layers. This ensures that the segmentation result can both understand the overall content of the image and accurately identify the boundaries and shapes of objects, effectively solving the edge problem.

Advantages and Applications of U-Net

With its outstanding performance on small sample data and efficient performance, U-Net quickly rose to prominence in multiple fields.

• Medical Image Segmentation: This is U-Net’s “home turf.” It is widely used in brain MRI image segmentation, lesion detection, tumor identification (such as brain tumors, lung cancer, liver tumors, breast cancer, etc.), and cell-level analysis, greatly improving the efficiency and precision of medical research.
• Autonomous Driving: For autonomous vehicles, accurately perceiving the surrounding environment is crucial. U-Net can classify every pixel in the image as road, vehicle, pedestrian, lane marking, etc., providing a clear environmental view for the car to help with safe navigation and decision-making.
• Agriculture: Researchers use U-Net to segment crops, weeds, and soil, helping farmers monitor plant health, estimate yield, and improve the efficiency of herbicide application.
• Industrial Inspection: In automated factories, U-Net can be used for product defect detection, identifying flaws on the production line.

Evolution and Future of U-Net

As a foundational and powerful model, U-Net’s structure has been continuously borrowed and improved by subsequent researchers. For example, variants like UNet++ and TransUNet have further improved performance and generalization capabilities by introducing more complex connection methods, attention mechanisms, or Transformer mechanisms. Researchers are working hard to improve U-Net’s robustness and generalization ability when processing different types of image data.

New developments include:

• Model Optimization: Researching more efficient training algorithms to reduce training time and computational resource consumption.
• Hybrid Evolution: Combining U-Net with other advanced technologies, such as the Mamba state space model, through new architectures like Weak-Mamba-UNet empowered by Mamba, to improve the ability to model long-range dependencies.
• Improvements like Multi-scale mechanisms, Attention mechanisms, and Transformer mechanisms make U-Net even more powerful when facing complex segmentation tasks.

Summary

U-Net is like a “puzzle master”: it first masters the overall layout and macro semantic information of the image through “compression,” then gradually reconstructs image details through “expansion,” and cleverly uses “skip connections” to pass down original fine clues directly, ensuring that the final “cut out” image is not only correct but also precisely bounded. It is this design that allows U-Net to play an irreplaceable role in various image segmentation tasks requiring pixel-level precision, continuously driving innovation and development of artificial intelligence technology in fields like healthcare, industry, and autonomous driving.

U-Net Architecture Demo

Title: Transformer
Tags: [“Deep Learning”, “NLP”, “LLM”]

Deep Dive into the AI “Brain”: How the Transformer Model Understands the World

In today’s era of rapid artificial intelligence development, you may have heard of popular applications like ChatGPT and Midjourney, which can write articles, draw pictures, and even communicate like humans. Behind these amazing capabilities lies a technological cornerstone that cannot be ignored, and that is — the Transformer model. It acts as the “brain” of AI, completely changing the way artificial intelligence processes information, especially achieving revolutionary breakthroughs in the field of Natural Language Processing (NLP), and profoundly affecting other fields such as computer vision.

1. Farewell to Old Methods: Why Do We Need Transformer?

Imagine you are reading a long novel. Traditional AI models, such as Recurrent Neural Networks (RNN), are like readers with limited memory. They must read sequentially word by word, and by the time they read later parts, they likely forget the details of previous chapters, making it difficult to understand the coherence of the whole story. Convolutional Neural Networks (CNN), while excellent at processing images, are better at capturing local information and struggle with long-distance contextual associations.

This “forgetfulness” and “blind spots” were two major pain points for early AI in processing long text data. The emergence of the Transformer model was precisely to solve these problems, allowing AI to handle long sequence information like a well-read scholar with a photographic memory.

2. The Core Magic of Transformer: Self-Attention Mechanism

Transformer does not understand context by processing information sequentially. Instead, it adopts its core innovation — the “Self-Attention Mechanism”.

1. The “Focus” Rule at a Party: Self-Attention Mechanism

   Imagine you are attending a large party with many people communicating. Traditional models might ask you to remember what everyone said in turn. The self-attention mechanism is like having a “superpower” that allows you to hear all conversations instantly and immediately judge which people’s words are most relevant to the person you are currently listening to. For example, when you are listening to a friend tell a joke, you might pay more attention to the friend telling the story and those laughing along, while ignoring people discussing the weather in the corner.

   In the Transformer model, each word “pays attention” to all other words in the input sequence when being treated. It calculates the “relevance score” between each word and itself as well as all other words. The higher the score, the closer the association. In this way, when processing a word, the model can automatically weigh the influence of other words on it, thereby better understanding the contextual meaning of this word in the entire sentence.

2. Multi-Angle Analysis: Multi-Head Attention Mechanism

   Viewing a problem from only one angle might be biased. Transformer’s “Multi-Head Attention” mechanism is like convening multiple experts to analyze the same problem simultaneously. For instance, in a sentence, one “expert” might focus on analyzing grammatical structure, another “expert” might focus on the emotional color of words, and yet another “expert” focuses on subject-verb-object relationships. Each “expert” “pays attention” and analyzes from their own perspective, and finally, their analysis results are integrated to obtain a more comprehensive and in-depth understanding. This parallel processing and multi-dimensional analysis greatly enhance the model’s ability to capture complex relationships.

3. Time Sequencing Assistant: Positional Encoding

   Although Transformer can “see the whole picture at a glance,” it does not naturally understand the order of words like the human brain. For example, “I love you” and “You love me” contain the same words but express completely different meanings. To solve this problem, Transformer introduces a “Positional Encoding” mechanism.

   You can imagine it as sticking a special “label” next to each word, containing its position information in the sentence, just like a page number in a book. In this way, even if the model processes all words in parallel, it can know the sequence of each word through these labels, thereby avoiding semantic confusion.

4. Information Processing Factory: Encoder and Decoder

   The Transformer model typically consists of two main parts: the Encoder and the Decoder. They are like two factories on an information processing assembly line.

   • Encoder: Responsible for understanding the input sentence. It receives words with “Positional Encoding”, and then processes them layer by layer through multiple layers of self-attention mechanisms and feed-forward neural networks, transforming the input sentence into a highly concentrated “understanding” rich in semantic information. Like a translator who acts after thoroughly understanding the meaning of the original text.
   • Decoder: Responsible for generating the output sentence. It not only pays attention to the words it has already generated but also refers to the “understanding” output by the encoder to gradually generate the next most likely word. Like a translator expressing word for word in another language based on understanding.

3. Why is Transformer So Powerful?

The revolutionary nature of the Transformer model lies in the following significant advantages it brings:

• Parallel Processing, Fast Speed: Unlike RNN’s sequential processing, the self-attention mechanism allows the model to process all words in the input sequence simultaneously, greatly improving the efficiency of training and inference.
• Long-Distance Dependency, Super Memory: It can effectively capture the association between words far apart in the text, solving the problem that traditional models struggle to handle long text contexts.
• Strong Versatility, Wide Application: Originally designed for natural language processing, its versatility allows it to extend to multiple AI fields such as image recognition, audio generation, and even protein structure prediction.

4. Latest Applications and Outlook of Transformer

Since the paper “Attention Is All You Need” was proposed in 2017, the Transformer architecture has completely changed the trajectory of artificial intelligence development.

In the field of natural language processing, Transformer is the core of Large Language Models (LLMs) like ChatGPT, Gemini, and Llama. These models are capable of performing various complex tasks such as text generation, translation, and Q&A, greatly improving the level of human-computer interaction.

Besides text, Transformer has also entered the field of computer vision extensively, giving birth to models like Vision Transformer (ViT). They have achieved results comparable to or even surpassing traditional convolutional neural networks in tasks such as image classification, object detection, and image segmentation, bringing new possibilities for image generation (such as DALL-E) and video understanding. Recent progress even features research exploring new architectures like MoR (Mixture of Recursions), aiming to fuse the advantages of RNNs and Transformers to cope with the computational challenges brought by large models, potentially becoming a more efficient alternative to Transformer. In addition, Transformer is also exploring new applications in fields like multi-modal AI and automated decision-making. Projects like Google’s Earth AI are using Transformer to build interoperable GeoAI model families, integrating three core types of data: imagery, population, and environment, providing cross-domain real-time analysis capabilities for non-professional users.

However, Transformer is not without limitations. Current Transformer models still have room for improvement in logical reasoning, causal inference, and dynamic adaptation; they are better at “mimicking” than “understanding.” Nevertheless, the Transformer model is undoubtedly the most dazzling technology star in the current AI field. Its continuous evolution and cross-domain applications are driving artificial intelligence towards a more intelligent, efficient, and multi-functional future. In the future, Transformer technology is expected to be further optimized to handle more complex data types, achieve more efficient attention mechanisms, and be trained on a larger scale, thereby providing more accurate predictions and analyses.

Title: Transformer-XL
Tags: [“Deep Learning”, “NLP”, “LLM”]

Unveiling the AI Memory Master: How Transformer-XL Possesses “Extra Long Memory”

In the vast world of Artificial Intelligence, Natural Language Processing (NLP) technology plays a pivotal role. The smart speakers, translation software, and chatbots we use are inseparable from powerful language models. Among them, the Transformer model has completely revolutionized the NLP field since its birth in 2017 with its excellent parallel processing capability and understanding of context. However, even the powerful Transformer is like a “short-term memory” scholar, encountering bottlenecks when facing ultra-long texts. To solve this problem, researchers from Google AI and Carnegie Mellon University proposed an upgraded version in 2019 — Transformer-XL. The “XL” stands for “Extra Long”, and as the name suggests, it possesses “extra long memory” far exceeding its predecessors.

So, how exactly does Transformer-XL achieve this? Let’s delve into it with simple examples from daily life.

The “Shortcomings” of Traditional Transformer: Context Fragmentation and Fixed Memory

Imagine you are reading a long novel. If this book is split into countless small slips of paper of fixed length, and you can only see the content on one slip at a time, discarding it after reading, and the content on the next slip has no connection to the previous one, you would find it hard to understand the ins and outs of the whole story. This is precisely the challenge traditional Transformers face when processing long texts.

1. Fixed-Length Context: The original Transformer model can usually only process text paragraphs of a fixed length (e.g., 512 words or characters). When the article being processed is too long, it will “crudely” cut the article into equal-length segments and process them one by one. This means the model can only see this small piece of information “in front of its eyes,” and is “blind” to key information hundreds of words ago, which limits its ability to establish long-range dependencies.
2. Context Fragmentation: Due to this forced cutting of fixed length, it is very likely that a sentence or a complete meaning is abruptly cut in the middle and divided into two different segments. Each segment is processed independently, with no information flow between segments. This is just like when you read a novel, a sentence is cut in half, and the end of the previous page cannot connect with the beginning of the next page, leading to “fragmented” semantics, making it difficult for the model to understand the complete context.
3. Slow Inference Speed: When generating text or making predictions, traditional Transformer needs to re-process the entire current segment every time it predicts the next word. The computational load is huge, leading to slow inference speed.

Transformer-XL’s “Memory Magic”: Segment-Level Recurrence Mechanism

To overcome these limitations, Transformer-XL introduced two core innovations, giving it extra long “memory” and stronger “understanding power”.

1. Segment-Level Recurrence Mechanism

Let’s return to the example of reading a novel. If, after finishing a chapter, instead of completely forgetting it, you could summarize the “core points” of this chapter and keep them in your mind, and then review these points at any time when reading the next chapter, you would be able to better understand the coherence of the story.

Transformer-XL adopts a similar working principle. It does not “lose memory” after reading a segment. Instead, after processing a segment of text, it caches the “memory” (i.e., hidden states) generated by this text in the neural network. When it starts processing the next text segment, it brings the previously cached “memory” along as additional context information for the current segment.

This is like writing down the essence of every chapter you have read in a small notebook, and reviewing the notebook at any time when reading new chapters, thereby connecting “current” and “past” knowledge. This mechanism implements recurrence at the segment level, rather than the word level in traditional Recurrent Neural Networks (RNN). It allows information to flow across segment boundaries, greatly expanding the model’s effective receptive field (the scope of context it can “see”), thereby effectively solving the problem of context fragmentation and capturing longer-distance dependencies.

In this way, Transformer-XL combines the parallelism of Transformer and the recurrent memory characteristics of RNN to some extent. Research shows that it can capture dependencies 80% longer than RNNs and 450% longer than traditional Transformers.

2. Relative Positional Encoding

In traditional Transformers, to let the model understand the order of words, each word is given an “absolute positional encoding,” just like marking every word in a novel with its absolute page number and line number in the book. But when Transformer-XL introduced the segment-level recurrence mechanism, if we simply reuse the hidden states of the previous segment and continue to use absolute positional encoding, problems arise. Because in different segments, words at the same relative position have different “absolute page numbers.” If encoding starts from 1 for all of them, the model will be confused, not knowing which position of which segment it is processing.

To solve this problem, Transformer-XL introduced Relative Positional Encoding. This is like you no longer caring if a word is “line 10, page 300 of this book,” but caring that it is “the 3rd word in the sentence I am currently reading” or “10 words away from that important word I just read.”

The core idea of relative positional encoding is that when the attention mechanism calculates the degree of association between different words, it no longer considers their absolute positions in the entire text, but focuses on the relative distance between them. For example, the relative relationship of a word to its previous word or the two words before it, rather than their respective “absolute coordinates.” This method allows the model to consistently understand the distance relationship between words regardless of which segment it is in, maintaining the coherence of position information even as the context continues to extend.

Advantages and Applications of Transformer-XL

Combining the segment-level recurrence mechanism and relative positional encoding, Transformer-XL demonstrates significant advantages:

• Longer Dependency Modeling Capability: It can effectively learn and understand dependency relationships in ultra-long texts, solving the “short-term memory” problem of traditional Transformers.
• Eliminating Context Fragmentation: By remembering information from previous segments, it avoids semantic interruption caused by text cutting, making the model’s specific understanding of the text more coherent and profound.
• Faster Inference Speed: In the evaluation phase, since previous calculation results can be reused, Transformer-XL is 300 to 1800 times faster than traditional Transformers when processing long sequences, greatly improving efficiency.
• Superior Performance: In multiple language modeling benchmarks, Transformer-XL has achieved state-of-the-art results.

These advantages make Transformer-XL perform excellently in tasks involving long texts, such as:

• Language Modeling: Achieved breakthrough progress in character-level and word-level language modeling tasks, capable of generating more coherent and logical long-form texts.
• Legal Assistant: Imagine an AI legal assistant that needs to read hundreds of pages of contracts and answer questions about interrelated clauses. No matter how far apart these clauses are in the document, Transformer-XL can help it understand and process more accurately.
• Reinforcement Learning: Its improved memory capability has also found applications in reinforcement learning tasks that require long-term planning.
• Inspiring Subsequent Models: The innovative ideas of Transformer-XL also inspired many subsequent advanced language models, such as XLNet, which is improved based on Transformer-XL.

Conclusion

The birth of Transformer-XL marks an important step for AI in long-text understanding. Like a scholar with “extra long memory,” it breaks through the limitations of traditional models through ingenious segment-level memory and relative position awareness, allowing AI to understand our colorful language world more deeply and coherently. This technology not only promotes the development of the natural language processing field, but also lays a solid foundation for future AI applications that are smarter and closer to human understanding capabilities.

Title: Trust Region Policy Optimization
Tags: LLM

In the vast field of Artificial Intelligence, Reinforcement Learning (RL) plays a crucial role, allowing machines to learn how to make the best decisions through interaction with the environment and trial and error. Among the many algorithms in reinforcement learning, Trust Region Policy Optimization (TRPO) is a landmark algorithm. It cleverly solves the common instability problems in traditional policy gradient algorithms and lays the foundation for subsequent more efficient algorithms (such as PPO).

Reinforcement Learning and Policy Optimization: AI’s “Way of Learning”

Imagine you are teaching a child to ride a bicycle. At first, they might fall, but by adjusting their posture, pedaling force, and direction each time, they will gradually master balance and eventually ride smoothly. This is like reinforcement learning: an AI agent takes actions in a specific environment, the environment gives “rewards” (good job) or “punishments” (bad job) based on the actions, and the agent constantly adjusts its “policy” (how to act) based on these feedbacks, hoping to get more rewards.

In reinforcement learning, a “policy” is equivalent to a set of behavioral rules or decision-making schemes in the agent’s brain. The goal of Policy Optimization is to find the best set of policies so that the agent can take actions that are most conducive to achieving the goal in any situation.

Why Do Traditional Policy Optimization Methods Easily “Flip Over”?

Early policy optimization methods, such as the Policy Gradient algorithm, act like a child eager for success. Once it finds that a certain action brings a reward, it might adjust its policy drastically. For example, if an agent is learning to play a game and finds that taking a step to the left gets a high score, next time it might take a huge leap to the left, only to fall into a pit and lose the game because it deviated too far. This “running with big steps” update method easily leads to instability in the learning process, even causing the learned policy to fail completely, falling short of success. We call this “policy collapse” or “overshooting update.”

TRPO’s Core Idea: “Small Steps, Safety First”

To solve this instability, the TRPO algorithm came into being. Its core idea can be summarized as “safe policy updates with small steps.” It does not blindly pursue higher rewards but carefully ensures that the new policy is not “too far” from the old policy during each update. This area of “not too far” is the essence of TRPO — the Trust Region.

We can understand it with a few analogies from life:

1. Cautious Driving: Imagine a novice driver learning to reverse into a garage. The instructor will not allow them to turn the steering wheel sharply but will teach them to adjust the direction slightly and gradually. Each adjustment is within a “trust region” to ensure the vehicle does not lose control and hit obstacles. Although each step looks small, it eventually parks the car steadily. TRPO is like this cautious instructor, limiting the magnitude of the agent’s policy adjustment each time to ensure the stability and reliability of the learning process.

2. Sound Investment Strategy: If an investment strategy is adjusted too aggressively at once (for example, transferring all funds from stocks to cryptocurrency), it may bring huge risks. TRPO’s “trust region” is like allowing only small adjustments to asset allocation each time, ensuring the portfolio is optimized within a “safe range” to avoid damaging overall performance due to short-term fluctuations.

3. Gradual Fitness Training: Just like in weightlifting or running training, if you suddenly increase the weight or intensity too much, it is easy to get injured. TRPO’s “small steps” concept is like gradually increasing weight (only a little bit each time) or increasing running distance, letting the body adapt gradually, ensuring “steady progress without regression.”

How Does TRPO Implement the “Trust Region”?

Technically, TRPO introduces a core concept: KL Divergence (Kullback-Leibler Divergence) to measure the difference between the new and old policies. KL divergence can be understood as a “distance” that quantifies the degree of difference between two probability distributions. TRPO’s goal is:

• Maximize the reward obtained by the agent (optimization objective) during each policy update.
• Simultaneously, ensure that the KL divergence between the new policy and the old policy is less than a preset threshold (trust region constraint).

Simply put, when exploring new policies, the agent can move towards higher rewards, but it must not step out of this “trust region”; otherwise, big problems are prone to occur. This method, combining optimization objectives and constraints, allows TRPO to theoretically guarantee a monotonic improvement in performance with each policy update, avoiding the risk of “overshooting,” thus making the learning process more stable.

Pros and Cons of TRPO

Pros:

• High Stability: TRPO’s most important contribution is solving the instability problem of policy gradient updates, theoretically guaranteeing monotonic improvement in policy performance.
• Strong Theoretical Guarantee: The algorithm is supported by a solid mathematical theoretical foundation, ensuring its effectiveness.
• Applicable to Complex Tasks: Especially suitable for continuous control tasks requiring high stability, such as robot control.

Cons:

• Computationally Complex: TRPO requires calculating and approximating high-order derivatives (such as the Fisher Information Matrix) during actual implementation, which makes its computational cost very high, especially when dealing with large neural networks.
• Difficult Implementation: Compared to other algorithms, the implementation process of TRPO is more complicated, posing a higher threshold for developers.

TRPO’s Legacy: The Rise of PPO

Precisely because TRPO has the disadvantages of computational complexity and difficult implementation, researchers developed a simpler and more practical algorithm based on its ideas — Proximal Policy Optimization (PPO). PPO inherits the advantages of TRPO, i.e., limiting the magnitude of policy updates, but it achieves this in a simpler way — by integrating the constraint term directly into the objective function or using a “clipping” mechanism to approximately control the range of policy changes.

PPO’s effect is similar to TRPO, but it is significantly optimized in terms of computational efficiency and implementation complexity. Therefore, it has become the mainstream method widely used in the field of reinforcement learning, especially in large-scale neural network training. It can be said that TRPO is the “source” and “ideological enlightener” of PPO, and the concept of “trust region” it proposed laid an important foundation for the stable development of reinforcement learning.

Summary

Trust Region Policy Optimization (TRPO) is a landmark algorithm in the field of reinforcement learning. It introduces the concept of “Trust Region” and solves the problem of unstable updates in traditional policy gradient methods by limiting the difference between new and old policies. TRPO ensures that the agent follows “small steps, safety first” during the learning process, guaranteeing steady policy improvement. Although TRPO itself is often replaced by its simplified version PPO in practical applications due to high computational complexity, its core ideas and theoretical contributions have had a profound impact on the development of the entire reinforcement learning field and are an indispensable part of understanding modern reinforcement learning algorithms.

AI’s Creative Spark: Demystifying Top-k Sampling, Teaching Machines to Think “Lively”

Imagine you are chatting with a robotic friend who always answers your questions in the most standard and common way, such as: “The weather is nice today.” “I have eaten.” Although correct, doesn’t it sound a bit boring, or even mechanical? In the world of Artificial Intelligence generated text, we have faced similar dilemmas. To make AI speak more naturally, interestingly, and creatively, scientists have come up with various ingenious methods, one of the core technologies being “Top-k Sampling,” which we are going to explore today.

How Does AI “Think” About the Next Word? — The Secret of Probability

To understand Top-k sampling, we first need to know how AI (especially Large Language Models, LLMs) generates text. In fact, it doesn’t truly “think” or “understand” like humans, but predicts the next most likely word based on the massive amount of data it has learned.

You can imagine AI as a super forecaster. When you give it a beginning, like “The sky is…”, it will quickly “brainstorm” thousands of words that might appear next and assign a “probability score” to each word. For example, “blue” might be 0.7, “gray” might be 0.2, “green” might be 0.05, “dancing” might be 0.0001, and “mobile phone” is almost zero.

The simplest and crudest method is for AI to directly choose the word with the highest probability score every time. This is like going to a restaurant and only ordering the best-selling dish on the menu every time. This method is called “Greedy Search” in the AI field. Its advantage is efficiency and stability, and the generated text is usually grammatically correct and logically coherent. But the problem is also obvious: it tends to be very conservative, lacking surprises, leading to high repetition and a lack of diversity and creativity. Your robot friend would just keep saying “The weather is nice today, really nice, very nice.”

Top-k Sampling: Giving AI a Few More “Options”

To solve the “boring” problem, Top-k sampling emerged. Its core idea is simple: AI no longer just stares at the word with the highest probability, but randomly selects one from the “top k” words with the highest probabilities.

For example:

Continuing with our “The sky is…” example. Suppose the probability ranking of words predicted by AI is:

1. blue (0.7)
2. gray (0.2)
3. purple (0.05)
4. clear (0.03)
5. green (0.01)
   … (followed by countless words with lower possibilities)

If Greedy Search is used, AI will choose “blue” without hesitation.

But if Top-k Sampling, K=3 is set, AI will not directly finalize “blue”. It will first pick out the top 3 words with the highest probabilities, namely “blue”, “gray”, and “purple”. Then, it will re-distribute their “winning probabilities” among these 3 words and randomly draw one as the next word. In this way, AI might generate a more imaginative sentence like “The sky is purple” instead of the monotonous “The sky is blue”.

It’s like buying a lottery ticket. Greedy search is buying the most popular number every time. Top-k sampling is randomly picking one from the top K numbers with the highest historical winning rates to buy. Your probability of winning is still high, but the numbers you buy are more diverse, and occasionally give you a small surprise, such as a “clear” sky.

Advantages of Top-k Sampling: Balancing Creativity and Reasonableness

Top-k sampling is widely used because it cleverly finds a balance point between “creativity” and “logic” in AI-generated text.

1. Increasing Diversity and Fun: By introducing randomness, Top-k sampling allows AI-generated text to escape monotonous repetition, becoming more vivid, natural, and closer to human expression. It offers richer choices for tasks like creative writing, story generation, and poetry.
2. Avoiding “Gibberish”: Although randomness is introduced, since the selection range is restricted to the “top K most likely words”, AI can still ensure that the generated text is relatively reasonable and won’t suddenly pop out words that are completely out of context, effectively reducing the interference of low-probability words and improving the coherence of the generation results. This prevents AI from really choosing absurd statements like “The sky is a mobile phone”.

Beyond Top-k, What Other “Tricks” Are There?

In practical applications, besides Top-k sampling, there are some other interesting “companions”:

• Temperature: This is like AI’s “divergence regulator”. The higher the temperature, the bolder the AI is in choosing words; even words with lower probabilities have a chance to be selected, thereby increasing the creativity of the text, but potentially sacrificing some coherence. The lower the temperature, the more conservative the AI is, tending to choose the most likely words, making the output more deterministic and focused. Often, researchers combine Top-k sampling with temperature parameters to achieve better text generation effects.

• Top-p Sampling (Nucleus Sampling): If Top-k sampling is a fixed number of choices (K), then Top-p sampling is more flexible. It does not select a fixed number of words, but dynamically selects a set of words whose cumulative probability reaches a certain threshold (e.g., 0.9). This means that in some contexts, the sum of the probabilities of just 2-3 words might reach 0.9, while in other contexts, 10 words might be needed. Top-p sampling is considered a more elegant method than Top-k because it adapts better to different probability distributions and often performs better than Top-k in practice, generating more natural responses.

Latest Progress and Combined Applications

In current Large Language Models, such as the GPT series, Top-k, Top-p, and Temperature parameters are often used together. They collectively constitute the “hyperparameters” for fine-tuning AI text generation. Recent research and applications show that by reasonably adjusting these parameters, developers can find the optimal balance between coherence, diversity, novelty, and computational efficiency (Top-k sampling can effectively reduce computational complexity) in text generation. For example, in scenarios requiring high diversity like creative writing, a higher Top-p value (e.g., 0.95) can be set, combined with Top-k sampling to ensure content innovation. In scenarios like code generation that require high accuracy, lower parameters might be set to ensure rigor.

Top-k sampling in the AI field is like installing a “lively thinking” switch on the machine brain. It is not just a technical detail, but a key step in transforming machines from simple information transmitters into entities capable of creative expression and personalized communication. With the continuous evolution of technology, we have reason to believe that future AI friends will become more interesting and more like us humans.

The hot concept in the AI field, Toolformer, is like equipping a super brain that acts as an “armchair strategist” with a full “toolbox” for practical use, making it not only articulate but also capable of precise action. Proposed by Meta AI in early 2023, this technology greatly expands the capability boundaries of Large Language Models (LLMs), enabling them to solve real-world problems more effectively.

1. The “Achilles’ Heel” of Large Language Models: Knowledgeable but Sometimes “Unreliable”

Imagine you have a very knowledgeable friend who can write poems, articles, and stories, and even discuss profound topics with you. They are extremely well-read and know almost everything. Large Language Models (LLMs), like ChatGPT, are somewhat like this friend. By learning from massive amounts of text data, they have mastered powerful language generation capabilities and can engage in fluent conversation, writing, translation, and programming.

However, this knowledgeable friend also has some “weaknesses.” For example, if you ask, “What is 235 multiplied by 487?” they might give answer that seems reasonable but is actually incorrect, or make up some “facts” just to answer. Or, if you ask, “ What is the weather like today?” they cannot answer because their knowledge is frozen at the time of training and cannot access real-time information. This is because traditional LLMs can only reason and generate within text data and cannot actively acquire or process information outside of text, such as performing precise calculations, searching for the latest facts, or calling external functions. They are like scholars who can only read and write; no matter how extensive their knowledge, they cannot pick up a calculator to do math problems or go online to find the latest news.

2. Enter Toolformer: Equipping AI with a “Toolbox”

Toolformer emerged to make up for these deficiencies of LLMs. It’s not about making the LLM larger or memorizing more knowledge, but teaching the LLM how to proactively use external “tools” like a human when encountering tasks it is not good at or cannot complete.

Analogy: Smart Brain and Smartphone

This is like equipping that knowledgeable “armchair strategist” friend with a fully functional “smartphone.” This phone has various Apps (tools), such as:

• Calculator App: Specifically used for precise mathematical calculations.
• Search Engine App (like Google, Bing): To find the latest information and verify facts at any time.
• Translation App: For quick multi-language translation.
• Calendar App: To get current date and time information.
• QA System App: To access specialized knowledge bases for specific answers.

Now, when this friend is asked, “What is 235 multiplied by 487?” they will “realize” this is a calculation problem, open the “Calculator App,” input the formula, get the accurate result, and then tell you. When asked “What is the capital of France?”, they will “open” the search engine, input the question, read the result, and then give the correct answer. What Toolformer endows LLMs with is precisely this ability to “realize the need for a tool, choose a tool, use the tool, and integrate the tool’s results into its own answer.”

3. How Does Toolformer “Teach Itself”?

The most clever part of Toolformer is its “self-supervised learning” mechanism. It doesn’t rely on large amounts of human annotation to train the model on when to use tools, but lets the model learn through “self-exploration.”

Specifically, this process can be understood as follows:

1. “Scribbling”: During training, Toolformer gives the language model some text and “randomly” inserts some instructions to “use tools” (API call candidates) in this text. For example, in the sentence “Paris is the capital of France,” it might randomly insert a instruction like [Search(capital of France)] at some position.
2. “Trial and Evaluation”: The model executes these “tool instructions” and gets a result. Then, it compares: if the result obtained using this tool is more helpful for it to predict the subsequent text (e.g., generating the word “Paris” more accurately), then this tool call is considered “useful.” If it’s useless or even interfering, it’s discarded.
3. “Filtering and Learning”: In this way, Toolformer creates a dataset containing “useful tool calls” by itself, and this process does not require human intervention. The model learns from these “successful cases” in what context it should call what tool, what parameters to pass, and how to use the information returned by the tool.

It’s like that friend who got the smartphone; at first, they might not know when to use which App, but they keep trying. When they find that using the “calculator” can solve math problems and using the “search engine” can find real-time information, they will remember these experiences and know what to do next time they encounter similar problems.

4. Changes and Future Prospects brought by Toolformer

The emergence of Toolformer has brought positive impacts in many aspects:

• Improved Accuracy: Solves the “hallucination” problem of LLMs in mathematical calculations, fact-checking, etc., making AI answers more reliable.
• Access to Real-time Information: Gives AI models the ability to connect to the outside world, no longer limited by the timeliness of their training data, allowing them to access latest information and respond.
• Expanded Capability Boundaries: Enables LLMs not only to understand and generate language but also to perform complex tasks such as calculation, translation, and searching, making them more powerful general-purpose agents.
• Increased Efficiency: By using external tools, models can significantly improve performance on various tasks without increasing their own parameter size (keeping the “brain” lightweight).

Although Toolformer still has some limitations in design, such as currently finding it difficult to implement chain calls between tools (i.e., the output of one tool serves as the input for another), and needing to consider computational costs when deciding whether to call a tool, it, as one of the pioneering researches on “teaching language models to use tools,” has pointed out an important direction for the development of subsequent large language models.

The core idea of Toolformer—teaching AI to “leverage strength” rather than using “brute force”—has profound significance for the future development of AI. It inspired the rise of the “AI Agent” concept, transforming AI from a mere “information generator” to a “task executor.” Future AI will no longer be an isolated brain, but an intelligent assistant adept at calling various professional tools and interacting with the outside world, capable of integrating more deeply and flexibly into our daily lives and work.

Title: Transformer in Vision
Tags: [“Deep Learning”, “CV”]

AI concepts emerge one after another, and every technological leap opens a window to the future for us. Today, we are going to talk about a technology that has made huge waves in Artificial Intelligence, especially in the field of Computer Vision in recent years — Vision Transformer. It is like a new “super grading teacher” who understands and “grades” the world before our eyes in its own unique way.

1. Introduction: The Revolution from “Reading Text” to “Understanding the World”

In the world of Artificial Intelligence, making machines “see” images and videos, and even understand their content, has always been a core challenge. For a long time in the past, we relied on a technology called “Convolutional Neural Network” (CNN). Imagine that CNN is like a traditional grading teacher who is good at “local observation and gradual progress” when correcting papers. It scans line by line, paragraph by paragraph, and then summarizes the rules from local details.

However, in recent years, another “teacher” — Transformer, has made breakthrough progress in the field of Natural Language Processing (NLP), which is the field of making machines understand and generate text. With its unique “global perspective” and “attention mechanism”, it has completely changed the way machines read text. Now, this “text master” has begun to cross over to challenge the task of “visual understanding”, giving birth to the Vision Transformer we are discussing today. It no longer focuses only on local parts, but tries to “survey the whole situation” at once and “allocate attention” according to importance, bringing a brand new way of thinking.

2. The Traditional “Grading Teacher” of Computer Vision: Convolutional Neural Network (CNN)

To understand the uniqueness of Vision Transformer, let’s briefly review its “predecessor” — Convolutional Neural Network (CNN).
CNN processes images in a way that can be likened to a very meticulous and experienced “chef” processing ingredients:

1. Local Receptive Field: Just like a chef chopping vegetables will first process individual ingredients like shredded carrots and potato chunks, CNN also scans the image block by block and pixel by pixel, capturing details like local textures and edges. It has a “receptive field” that focuses only on the current small area.
2. Layered Abstraction: These local information are processed layer by layer, just like cooking and seasoning the chopped ingredients, gradually extracting higher-level features from simple lines to complex shapes, and then to the overall outline of objects.
3. Advantages and Limitations: CNN excels at inducing patterns from local features and performs well in many visual tasks. But its limitation lies in that it is difficult to directly capture the relationship between two elements in an image that are far apart but related. Just like a chef who finishes chopping vegetables, it is hard to immediately know what unique flavor will be produced after combining all the dishes; it requires step-by-step trial.

3. The New Generation “Grading Teacher”: Transformer Enters the Scene

The Transformer model was first proposed by Google in 2017, completely revolutionizing the field of Natural Language Processing (NLP). It abandoned traditional Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), and was built entirely based on a “global focus” technology called Self-Attention Mechanism.

Imagine you have a very complicated contract in front of you. The traditional way of reading is to read word by word, while Transformer’s attention mechanism is like scanning roughly before reading the contract, and then automatically judging which sentences are the most important and which are just auxiliary explanations based on the internal logical relationship between contract terms, allowing it to consider all text simultaneously and understand their interrelationships. This ability to “survey the whole and distinguish the primary from the secondary” makes it particularly effective when dealing with long text dependency problems.

So, when this “super grading teacher” who is good at “reading text” comes to the field of Computer Vision for “image recognition”, how does it work? This is the core idea of Vision Transformer (ViT): Treat images as segments of text.

4. How Does “Vision Transformer” Work?

The workflow of Vision Transformer (ViT) can be vividly likened to a teacher grading an “image exam paper” composed of many small cards:

1. Image “Patching”: First, a complete picture is cut into many small squares of the same size, which we call “Image Patches”. It’s like a complete test paper divided equally into many small cards, and each card has a small part of the image. For example, a 224x224 pixel image can be cut into 196 16x16 pixel small patches.
2. “Patches” into “Words” (Tokenization): Next, each image patch is digitized and converted into a special “Patch Embedding”. You can think of this as summarizing the image content on each small card into a short “label” or “code”.
3. Positional Encoding: Just having “words” is not enough; we also need to know the positional relationship of these “words” in the original picture. ViT adds a “positional encoding” to the vector of each image patch, just like stamping a “location stamp” on each small card, telling the model whether this card was originally in the upper left corner or the lower right corner of the picture. In this way, even if the image patches are shuffled, the model knows their original order.
4. “Self-Attention” Mechanism: This is the most core and magical part of the entire Vision Transformer. After entering the main body of the Transformer — the “Encoder”, the image patches (now “word vectors” with position information) are no longer processed in isolation. The model examines all image patches simultaneously and lets each image patch “pay attention” to all other image patches.
   • “Global View”: Unlike CNN’s local observation, the self-attention mechanism gives ViT a “global view” from the start, enabling it to directly establish relationships between any two pixel regions in the image, regardless of how far apart they are.
   • “Weight Allocation”: When the model processes a certain image patch, it calculates the strength of expectation between this image patch and all other image patches in the picture, and assigns different “attention weights” based on the correlation. For example, when identifying a picture of a cat, the model might find a strong correlation between the cat’s eyes and the cat’s whiskers, while the correlation between the cat’s eyes and a tree in the background is weak. The model will pay more “attention” to those strongly correlated image patches.
   • “Multi-Head Attention”: To understand the image more comprehensively, Vision Transformer usually adopts a “Multi-Head Attention” mechanism. This is like organizing a review panel, with multiple “grading teachers” examining the relationship between image patches from different angles (different “heads”). Some “heads” may focus on color, some on shape, and some on position, and finally synthesize everyone’s opinions.
5. Output and Application: After multiple layers of such “self-attention” and feed-forward neural network processing, the model learns the complex interrelationships and higher-level visual features between various parts of the image. Finally, these features are used for various visual tasks, such as image classification (identifying what the picture is), object detection (finding out what objects are in the picture), semantic segmentation (precisely depicting the boundary of each object), etc.

5. Why is “Vision Transformer” Powerful?

The emergence of Vision Transformer has brought many exciting advantages to the field of computer vision:

1. Capturing Long-Range Dependencies: Traditional CNNs struggle to capture features that are far apart but related in an image because they are limited by local receptive fields. Vision Transformer’s self-attention mechanism is naturally capable of handling such “long-range dependencies”, allowing for a better understanding of the overall structure and context of the image.
2. Stronger Generalization Ability: Since Vision Transformer has a weaker “inductive bias” (which can be understood as the model’s prior assumption about data structure) than CNN, this means it makes fewer assumptions about data and can learn more general visual patterns from large-scale data. Once the data volume is large enough, its performance often outperforms CNN.
3. Scalability: Transformer models show great potential when dealing with large-scale datasets and building large-scale models, which has huge advantages in image recognition, especially in pre-training large visual models.
4. Unification: It provides a unified architecture for image and text processing, which is of great significance for the development of future multi-modal AI (processing multiple data such as images, text, voice, etc., simultaneously).

Of course, Vision Transformer is not perfect. It usually requires very large datasets for training to unleash its full potential. For smaller datasets, traditional CNNs might perform better.

6. Applications in Daily Life

Vision Transformer and its derived models are quietly changing the way we interact with the digital world:

• Smartphone Albums: When you finish taking photos with your mobile phone, the album can automatically identify people, places, and things in the photos and manage them by category. This may be due to Vision Transformer.
• Medical Image Analysis: In the medical field, it assists doctors in analyzing X-rays, CT scans, or pathological slices to help detect diseases, such as identifying tumors or lesion areas.
• Autonomous Driving: Helping vehicles identify road signs, pedestrians, other vehicles, and various complex road conditions is key to the safe and reliable operation of autonomous driving technology.
• Security Monitoring: In crowded places, identifying abnormal behaviors, performing face recognition, and tracking suspicious targets to improve public safety.
• AI Painting and Content Generation: AI models like DALL-E and Midjourney, which can generate realistic images through text descriptions, also rely on the core Transformer architecture for deep understanding of images and texts.
• Video Analysis: Understanding video content, performing behavior recognition and event detection, such as analyzing athlete movements in sports events or monitoring equipment operating status in industrial production.

7. Future Outlook

Since its proposal in 2020, Vision Transformer has become an important research direction in the field of computer vision and is expected to further replace CNN as the mainstream method in the future.

The latest research and development trends include:

• Hybrid Architectures: Combining the advantages of CNN and Transformer, using CNN to extract local features and then using Transformer for global modeling to achieve better performance and efficiency. For example, Swin Transformer calculates self-attention within local windows by introducing a “shifted window mechanism”, which reduces computational complexity and optimizes memory and computing resource consumption.
• Lightweight and Efficiency: To use in mobile devices and edge computing scenarios, researchers are working hard to develop smaller and faster Vision Transformer models, such as MobileViT which combines lightweight convolution with lightweight Transformer.
• Broader Applications: In addition to traditional image classification, object detection, and segmentation, Vision Transformer is continuously exploring more fields such as 3D vision, image generation, and multi-modal understanding (vision-language combination), showing strong versatility. For example, MambaVision combines state space sequence models with Transformer, achieving performance improvements and reduced computational load in certain tasks.

The rise of Vision Transformer marks an important step for artificial intelligence on the road to “understanding the world”. With its unique global perspective and attention mechanism, it opens a new chapter for us to understand and process visual information. In the future, with the continuous evolution of technology, we have reason to believe that this “super grading teacher” will help AI better perceive and create the world.

The “Accelerator” on the Smart Chip: A Deep Dive into NVIDIA TensorRT

In today’s era of rapid technological development, Artificial Intelligence (AI) applications have penetrated every aspect of our lives, from facial recognition on smartphones and voice assistants to autonomous driving cars and medical image diagnosis. AI is changing the world at an unprecedented speed. However, as AI models become more complex and massive, a severe challenge arises: how to make these “smart brains” run faster and more efficiently? This is where NVIDIA TensorRT comes in, acting as the “Highway Designer” and “Savvy Butler” of the AI world, specifically responsible for speeding up AI models so they can respond quickly and work efficiently.

What is TensorRT? The “Highway Designer” for AI Models

Simply put, NVIDIA TensorRT is an optimization library and runtime environment specifically designed for deep learning inference. Developed by NVIDIA, its goal is to fully utilize the powerful parallel computing capabilities of their GPUs (Graphics Processing Units) to accelerate the inference process of neural network models in practical applications, significantly improving the response speed and operational efficiency of AI applications.

Analogy: Imagine training an AI model is like engineers working hard to “build” a state-of-the-art smart car, teaching it various driving skills. AI inference, then, is this car truly “hitting the road,” performing various tasks such as recognizing road conditions, avoiding pedestrians, and planning routes. TensorRT is not a tool for building cars; it’s more like a super-professional “Traffic Optimization Expert.” It doesn’t participate in car building (model training), but it can analyze the characteristics of this car (the trained AI model) and then specifically plan the optimal route, widen the roads, optimize traffic lights, and even set reasonable speed limits for it, allowing it to run faster, more efficiently, and safer on the designated road (NVIDIA GPU hardware).

What Magical Optimizations Does It Perform? The “Savvy Butler” of AI Models

So, how exactly does TensorRT achieve these “magical” optimizations? This starts with the two main stages of deep learning—Training and Inference. The training stage requires the model to constantly learn and adjust parameters, involving complex backpropagation and gradient updates. However, in the inference stage, the model parameters are fixed, and only forward computation is needed to get the result, so many aggressive optimizations can be performed that are impossible or inconvenient during training.

TensorRT is like a savvy butler. Before the master (AI model) goes out to “perform a task” (inference), it organizes everything methodically to maximize efficiency. It mainly optimizes through the following means:

1. Layer Fusions / Graph Optimizations — Integrating “Small Pieces” into “Big Chunks”

   • Butler Analogy: Imagine you want to cook, which involves “cutting vegetables,” “stir-frying,” and “washing the pot.” An ordinary chef might do it step by step, stopping after each action. A savvy chef (TensorRT) would realize that some adjacent actions can be combined, such as putting vegetables directly into the pot after cutting, or washing the pot immediately after frying a dish, thus reducing pauses and tool switching.
   • Technical Explanation: In neural networks, many operations (such as convolution layers, bias, activation functions) are performed sequentially. TensorRT can intelligently fuse these continuous and interrelated layers into a larger operation unit. The benefit of this is reducing the number of repeated data transfers between memory and computation cores, greatly reducing memory bandwidth consumption and GPU resource waste, thereby significantly improving overall calculation speed.

2. Precision Calibration & Quantization — From “Meticulous Crafting” to “Just Right”

   • Butler Analogy: Imagine you usually use 1-yuan, 5-jiao, and 1-jiao coins to buy things, precise to 1 jiao. But if the supermarket now only accepts 1-yuan bills, although less precise, the payment speed is faster, and for most goods, the difference is negligible.
   • Technical Explanation: Traditional deep learning models usually use 32-bit floating-point numbers (FP32) for calculation, which has very high precision. But for inference, such high precision is not always necessary. TensorRT supports reducing the precision of model weights and activation values from FP32 to 16-bit floating-point numbers (FP16) or even 8-bit integers (INT8).
     • FP16 (Half Precision): Uses less storage space and calculates faster, while usually maintaining good model accuracy.
     • INT8 (8-bit Integer): Further reduces storage requirements and computational overhead, significantly accelerating operations.
   • TensorRT finds the best balance between performance and accuracy through a “precision calibration” process, trying to maintain model accuracy while reducing precision. It’s like simplifying a very precise number (like 3.1415926) to “3.14” in certain scenarios, saving computational resources while keeping the result accurate enough.

3. Kernel Auto-Tuning — “Private Customization” for Hardware

   • Butler Analogy: Your smart car chooses different driving modes (Eco, Sport, Off-road) under different road conditions (city, highway, mountain road). TensorRT is like this highly intelligent system; it can automatically select the computing method and algorithm kernel most suitable for the characteristics of the currently deployed NVIDIA GPU hardware platform.
   • Technical Explanation: Different GPU architectures have different optimization characteristics. TensorRT can find the most efficient CUDA kernel implementation for each neural network layer and select it based on parameters such as layer size and data type. This ensures that the model runs at peak performance on specific hardware, fully unleashing the potential of the GPU.

4. Dynamic Tensor Memory — The Philosophy of “On-Demand Allocation”

   • Butler Analogy: An old warehouse might need to plan fixed positions for all goods in advance, even if some shelves are empty and cannot be flexibly utilized. A modern smart warehouse (TensorRT) can dynamically allocate storage space according to the actual quantity and shape of incoming goods, using it on demand to avoid waste.
   • Technical Explanation: During AI inference, the size of the data (tensor) processed by the model may not be fixed, especially for models processing variable-length sequences or dynamic shapes. TensorRT can dynamically allocate and manage tensor memory, avoiding unnecessary memory reservation and repeated application, improving memory utilization efficiency.

Why is TensorRT So Important? The “Efficiency Engine” of the AI Era

Through the series of optimizations mentioned above, TensorRT brings revolutionary performance improvements to deep learning inference, playing a pivotal role in the AI era:

• Performance Leap: Validated experience shows that inference speed with TensorRT-optimized models can be dozens of times faster than unoptimized versions, and even up to 36 times faster compared to pure CPU platforms. For example, for Generative AI Large Language Models (LLMs), TensorRT-LLM can bring up to 8x performance improvement.
• Real-time Guarantee: In application scenarios with extremely high latency requirements such as autonomous driving, real-time video analysis, intelligent monitoring, and speech recognition, TensorRT can significantly shorten the response time of AI models, ensuring the execution of real-time interaction and decision-making.
• Resource Utilization Increase: Through means like quantization, model size is smaller and memory usage is lower, meaning that less hardware resource is needed to run more complex AI models, or more tasks can be processed with the same resources.
• Broad Compatibility: TensorRT can optimize models trained by mainstream deep learning frameworks (such as TensorFlow, PyTorch, ONNX), allowing developers to focus on the innovation of the model itself without worrying about performance issues during deployment.

Latest Progress and Trends: Empowering Large Language Models

In recent years, the explosive development of Large Language Models (LLMs) has brought disruptive changes to the AI field. To cope with the huge computational volume of LLMs, NVIDIA specially launched TensorRT-LLM. It is an open-source library specifically designed to accelerate the latest Large Language Models of generative AI. TensorRT-LLM shines in large model inference acceleration, achieving significant performance improvements while drastically reducing Total Cost of Ownership (TCO) and energy consumption.

In addition, TensorRT itself is continuously updated and iterated. The current latest version is TensorRT 10.13.3, which constantly adapts to new network structures and training paradigms, and supports the latest NVIDIA GPU hardware to provide more powerful debugging and analysis tools, helping developers better optimize models. The TensorRT ecosystem is also becoming increasingly perfect, including tools like TensorRT Compiler, TensorRT-LLM, and TensorRT Model Optimizer, providing developers with a complete set of efficient deep learning inference solutions.

Conclusion: Unsung Hero, Empowering the Future

NVIDIA TensorRT is not an AI application directly facing ordinary users, but it is the unsung hero that allows AI technology to be popularized and run efficiently. It is like that “butler” who silently gives in the background and organizes everything methodically, allowing cutting-edge AI technology to integrate into daily life with the speed and efficiency we are accustomed to. As AI models become smarter and more complex, optimization tools like TensorRT will become even more indispensable, continuously empowering AI technology and driving human society towards a more intelligent future.