In the vast world of Artificial Intelligence, Large Language Models (LLMs) are like shining pearls, and their power largely stems from a mechanism called “Attention”. However, like any powerful technology, “Attention” also faces challenges of efficiency and resource consumption. Today, we will delve into an ingenious solution — Flash Attention, which accelerates and optimizes the attention mechanism like “lightning”.

1. Understanding the “Attention” Mechanism: Focus of Memory

To understand Flash Attention, we first need to understand the object it optimizes — the traditional attention mechanism.

Imagine you are reading a long novel. When you read a certain word, to fully understand its meaning, your brain automatically reviews the words read before, or even predicts the words that might appear later, to establish context and judge which words are most critical to the understanding of the current word. For example, when you read the word “Apple”, if “Jobs” was mentioned before, you might think of “Apple Inc.”; if “fruit stand” was mentioned before, you would think of “a kind of fruit”.

In large AI models, the “Attention” (more precisely, “Self-Attention”) mechanism does something similar. When the model processes a word in a sentence (sequence), it looks at all other words in the sequence simultaneously and calculates the importance score (or “attention weight”) of each word for the current word. The higher the score, the closer the relationship between that word and the current word, and the more important it is for understanding the current word. Then, the model performs a weighted sum of the information of all words according to these weights to obtain a new representation of the current word after considering the entire context.

To use an analogy:

• Each word is like a character or an event in the novel.
• Calculating attention weights is like your brain judging the importance of these characters or events to the current plot while reading.
• Weighted summation is like you finally understanding the content of a chapter, and this understanding integrates the influence of the behaviors and events of all important characters.

This mechanism allows the model to capture long-distance dependencies and is the key to the success of the Transformer model (the foundation of large language models).

2. The “Bottleneck” of Traditional Attention: Challenges of Memory and Speed

Although the “Attention” mechanism is powerful, it has a significant drawback: computational volume and memory consumption are proportional to the square of the sequence length.

What does “proportional to the square” mean?
Using the novel example again:

• If your novel has only 100 words, you need to do about 100 x 100 = 10,000 “attention” interactions (each word pays attention to all other 100 words).
• But if the novel has 1,000 words, the number of interactions becomes 1,000 x 1,000 = 1,000,000.
• If the novel has 10,000 words (a short story), the number of interactions will be 10,000 x 10,000 = 100,000,000!

You will find that when the length of the novel (sequence) increases slightly, the workload (computational volume) your brain needs to do and the relationships (memory consumption) it needs to remember will grow explosively.

In computers, this mainly manifests in two aspects:

1. Computation time is too long: The complexity of $O(N^2)$ means that when processing long sequences, the training and inference speed of the model will become very slow.
2. Memory occupation is too large: Huge memory is needed to store the attention weight matrix between all words. When training large models, this will quickly exceed the limited video memory capacity of the GPU, causing the model to be unable to process very long texts. Although the High Bandwidth Memory (HBM) of the GPU is large, the access speed is relatively slow; while the Static Random Access Memory (SRAM) inside the GPU is extremely fast, its capacity is very small. The traditional attention mechanism frequently transfers data between HBM and SRAM, leading to low efficiency (high “data movement” cost).

It’s like you have a huge library (HBM) and a very small but fast desk (SRAM). The traditional attention mechanism requires borrowing and returning a large number of books from the library repeatedly for every word processed, and your desk simply cannot hold all the books. Frequent trips to the library greatly reduce your work efficiency.

3. Flash Attention: Lightning-fast Magic

Flash Attention was born to solve these two core pain points of the traditional attention mechanism. It was proposed by researchers at Stanford University in 2022. Its core idea is to significantly improve calculation speed and reduce memory consumption through a series of ingenious optimizations without changing the calculation results of the attention mechanism.

The optimizations of Flash Attention focus on two main aspects:

3.1. Tiling / Blocking: Breaking up the Whole, Local Optimization

Imagine you still have to read that long novel, but now you are a smart reader. You no longer try to remember the relationships of all words at once, but adopt a more efficient strategy:

1. Batch Processing: You divide the novel into several small chapters or paragraphs.
2. Local Focus: When you read a small paragraph, you bring all the words (Query, Key, Value) of this paragraph to your desk (SRAM) at once. Then, you complete all attention calculations (calculating weights, weighted summation) within this small paragraph.
3. Minimal Information Return: You don’t need to remember the details between all words in this paragraph, only the final, condensed context representation of this paragraph, and some necessary summary information (such as the maximum value used for subsequent normalization) need to be temporarily stored.

Flash Attention processes attention calculation in “blocks” like this. It divides the input sequence and the intermediate Key and Value matrices into small blocks for calculation in the GPU’s SRAM (extremely fast but small capacity). The biggest benefit of doing this is that it reduces the amount of data transfer between the slower HBM and SRAM, avoiding the inefficient operation of writing the entire huge attention matrix to HBM and reading it back in traditional methods.

3.2. Kernel Fusion & Online Softmax: Calculate on the Fly, Reduce Storage

Another key innovation of Flash Attention lies in the use of “Kernel Fusion” and “Online Softmax”.

• Kernel Fusion: Traditional attention calculation usually involves multiple independent GPU operations (such as matrix multiplication, Softmax, another matrix multiplication). Each independent GPU operation requires loading data from HBM, calculating, and then writing the result back to HBM. Flash Attention fuses these operations into a single GPU Kernel, which means that once data is loaded into SRAM, all calculation steps can be completed continuously without frequent interaction with HBM. It’s like preparing a big meal; instead of putting ingredients back in the fridge after cutting each one, or putting a dish back after cooking it, you bring all ingredients to the cutting board at once and complete all cutting, frying, and stewing in one go, greatly improving efficiency.

• Online Softmax Normalization: This is the core of Flash Attention’s memory optimization. In the attention mechanism, to ensure that attention weights are a probability distribution (sum is 1), Softmax normalization is required. The traditional method calculates the entire attention matrix L first, and then normalizes it. This L matrix is very large and consumes a lot of memory.
  Flash Attention does not need to store the complete attention matrix L. It cleverly uses the properties of the Softmax function to only store necessary statistical information for each block (such as the maximum value and the sum of exponentials) in an “online” manner during the block calculation process, and then recomputes the normalization factor using these statistics at output time. This means it avoids writing the huge intermediate attention matrix to HBM, thereby drastically saving memory.

To use an analogy:
The traditional method is: You score the importance of all paragraphs in the novel (a huge matrix), write all these scores on a large piece of paper (HBM), and then read back from this paper to ensure that the total score of each paragraph is normalized to 1.
Flash Attention is: You score in segments. After scoring a segment, you only note down the highest score and total score of this segment (a small amount of statistical information). When you finally need to know the final importance of a word, you quickly recombine and calculate the accurate normalized score of that word based on these previously noted statistics, without needing to store that huge scoring matrix. This is a “calculate on the fly” strategy, sacrificing a tiny bit of re-computation overhead in exchange for huge gains in memory and data transfer.

4. Flash Attention 2s: Further Optimization

Following Flash Attention, the research team launched Flash Attention 2. Based on the first generation, it further optimized the parallelization strategy to better utilize the multi-processor characteristics of modern GPUs. Major improvements include:

• Finer-grained Parallelization: Decomposing attention calculation tasks into smaller sub-tasks and distributing them more evenly to multiple computing units of the GPU.
• Optimizing Input/Output Splitting: When processing long sequences, the allocation of Query, Key, and Value blocks among different GPU threads is improved, further reducing the memory wall effect.

These optimizations make the performance advantage of Flash Attention 2 even more significant on extremely long sequences, enabling higher throughput in large model training.

5. Impact and Application: The Accelerator for Large Models

The emergence of Flash Attention is of great significance:

• Significantly Improve Training and Inference Speed: According to official data, Flash Attention can increase the training speed of Transformer models by 2-4 times and inference speed by up to 3 times. Flash Attention 2 can achieve nearly 8 times throughput improvement.
• Drastically Reduce Memory Occupation: Memory usage is optimized from $O(N^2)$ to $O(1)$ relative to sequence length, which means models can process longer text sequences without encountering memory bottlenecks. This is crucial for tasks like long text understanding and few-shot learning.
• Unlocking Larger and Stronger Models: Due to speed and memory optimizations, researchers and developers can now train and deploy large language models with larger context windows, thereby enhancing the model’s understanding and generation capabilities. Current mainstream large language models like the GPT series and LLaMA series have widely integrated Flash Attention or its variants to achieve high-performance computing.

It can be said that Flash Attention and its subsequent versions are a crucial infrastructure technology on the development path of large language models. It works silently behind the scenes, yet acts like a powerful accelerator, driving AI technology to continuously break boundaries, allowing us to build smarter and more efficient AI models.

Exploring the “Falcon” of the AI Field: An In-depth Analysis of the Falcon Large Language Model

In the vast starry sky of Artificial Intelligence, Large Language Models (LLMs) are undoubtedly among the brightest stars. They are like “digital brains” with extraordinary wisdom, capable of understanding and generating human language, and even creating and reasoning. Among many LLMs, one name is resounding louder and louder, and that is the Falcon series models developed by the Technology Innovation Institute (TII) of the United Arab Emirates. With its excellent performance and spirit of openness, it is soaring high in the AI world.

What is Falcon? — A Wise Sage Who Is Well-Read and Articulate

Imagine an old professor who is learned, experienced, and knowledgeable about everything in the world. He can not only answer any of your questions but also write beautiful poems, logically rigorous papers, and even engage in lively and interesting conversations with you. This is the image of the Falcon Large Language Model in the digital world.

Technically speaking, Falcon is a series of generative large language models based on the Transformer architecture, designed to understand and generate human language. Its core goal is to advance AI technology to make it more accessible, efficient, and powerful.

The Uniqueness of Falcon — Three “Killer Features”

The reason why Falcon stands out in the fiercely competitive AI field is due to several “killer features” it possesses:

1. Openness and Sharing Spirit: The “Open Source Library” of the AI Field

Many top AI models developed by commercial companies are usually closed-source, like a private library that only paying members can enter. Falcon, on the other hand, chose the path of open source, especially its 7B (7 billion parameters) and 40B (40 billion parameters) models, which are released under the Apache 2.0 license. This means that any individual, research institution, or company can use, modify, and use them for commercial purposes for free.

Analogy: This is like a technology company freely publishing their most advanced blueprints and technical manuals, allowing engineers all over the world to innovate and improve upon them. This move has greatly promoted the democratization of AI and global collaboration.

2. Outstanding Wisdom and Capability: “Extremely Knowledgeable Giant Brain”

The Falcon model family has various sizes, ranging from smaller 1.3B, to 7B, 40B, and to the giant model with up to 180B (180 billion) parameters.
Taking Falcon 180B as an example, it is currently one of the largest and most powerful open-access LLMs. Its performance is comparable to Google’s PaLM 2 model, and it even surpasses GPT-3.5 in some benchmarks, approaching the level of GPT-4.

Analogy: Different Falcon models are like professionals with different levels of wisdom. The 1.3B model might be a knowledgeable undergraduate, the 7B model an experienced master, the 40B model an accomplished doctor, and the 180B model a master super-professor. This “super-professor” not only has an amazing memory (large parameters) but also superior understanding, capable of handling very complex tasks.

It is trained on a massive high-quality dataset called RefinedWeb using TII’s custom tools and unique data pipeline, which contains trillions of tokens. This is like the “super-professor” reading a massive, carefully selected, and organized digital library to absorb almost all human knowledge and communication patterns.

3. Advanced Internal Structure: “Efficient Thinking Engine”

The Falcon model adopts the Transformer architecture and has made several innovations on this basis. For example, it utilizes Multi-Query Attention or Multi-Group Attention technology, as well as Rotary Positional Embeddings.

Analogy: These complex names sound a bit esoteric, but you can imagine them as particularly efficient and optimized thinking circuits in the “super-professor’s” brain. Multi-Query Attention is like the professor being able to process multiple related questions simultaneously without interfering with each other, greatly improving thinking efficiency; Rotary Positional Embeddings enable the professor to better understand the relative positional relationship between information, ensuring context coherence and accuracy. These improvements allow Falcon to process information faster, more efficiently, and with fewer computing resources.

Applications of Falcon — Your All-round Digital Assistant

As a powerful large language model, Falcon is capable of a wide range of tasks:

• Intelligent Writing Assistant: It helps you write emails, reports, articles, and even poems and scripts.
• Multilingual Translator: Supports multiple languages for efficient and accurate translation.
• Information Summarization Expert: Quickly and accurately summarizes long documents and meeting minutes.
• Intelligent Q&A Robot: Answers various questions and provides information query services.
• Code Generation and Assistance: Assists programmers in generating code and debugging programs.
• Sentiment Analyst: Understands the emotional tendency behind the text.

Analogy: Imagine you have a versatile “Swiss Army Knife” that can help you write reports, translate documents, chat with you, answer questions, and even help you write code. Falcon is such a digital tool that can play a huge role in multiple industries such as customer service, software development, and content creation.

Latest Progress and Outlook — A Future Pioneer in the AI Field

The Falcon series models are evolving at an astonishing speed:

• Falcon 3 Series: The Technology Innovation Institute (TII) recently released the Falcon 3 series, which is the latest iteration of its open-source large language model series. A highlight of Falcon 3 is its efficiency, capable of running on lighter infrastructure, even efficiently on laptops.
• Multimodal Capabilities: Falcon 3 also introduces excellent multimodal capabilities, meaning it can process not only text but also understand and process images, and even support video and audio data in the future. The Falcon 2 11B VLM model has already achieved image-to-text conversion capabilities, taking a significant step in multimodal aspects.
• Specialized Models: To meet specific needs, Falcon has also launched models like Falcon Arabic (optimized for Arabic) and Falcon-H1 (a hybrid model combining Transformer and Mamba architectures, focusing on efficiency).

Analogy: This is like the “super-professor” not only reading text books but now also watching pictures, listening to sounds, and even watching videos to learn and understand the world. And he is becoming more and more “approachable”, able to display his talents on ordinary devices without needing a supercomputer.

• Falcon Foundation: To further promote AI open-source development, the Advanced Technology Research Council (ATRC) and TII jointly announced the establishment of the Falcon Foundation. This foundation aims to build an open and sustainable ecosystem to support the development of the Falcon series large language models, similar to the success model of the open-source operating system Linux.

Conclusion

With its openness, powerful performance, efficient architecture, and continuous innovation, the Falcon large language model is reshaping the landscape of the AI field. It not only brings cutting-edge technical breakthroughs but also allows these powerful AI capabilities to be utilized by a wider range of people through open source, thereby accelerating the popularization and innovation of global AI. The story of Falcon is a vivid portrayal of the AI field constantly breaking limits and pursuing sharing and progress.

What is FP16 Quantization? — Letting AI Models “Travel Light”

Imagine that when we use computers for mathematical calculations, we need to accurately represent various numbers, especially numbers with decimals (floating-point numbers). The most common one is “Single Precision Floating Point,” or FP32 (Floating Point 32-bit), which uses 32 “grids” to store a number. It can represent a very large range and very small details very accurately, just like a very detailed recipe, precise to many decimal places.

However, AI models, especially the popular Large Language Models (LLMs) in recent years, have billions or even trillions of parameters. When they perform calculations, every parameter and every intermediate result is a number. If all are represented by such a “super detailed recipe” like FP32, it will bring a huge storage and calculation burden. It’s like a chef managing thousands of super detailed recipes at the same time, which not only takes up kitchen space (video memory) but is also very slow to read and process (calculation speed).

FP16, the full name “Half-precision floating-point,” is the “magic tool” to solve this problem. It only uses 16 “grids” to store a number. You can think of it as a “simplified recipe,” no longer so precise to many decimal places, but only retaining key information, just like we usually say “add a small spoonful of sugar” or “about a bowl of rice.” This simplification of number representation is the core idea of FP16 quantization.

Why is FP16 So Important? — The Secret of “Fast and Economical”

FP16 quantization is favored by the AI field mainly because it brings three significant advantages:

1. Faster Calculation Speed, Like a “Lightning Chef”
   When a computer processes numbers in FP16 format, since each number takes up less space, the amount of data transmission is greatly reduced. More importantly, modern GPUs (Graphics Processing Units), especially dedicated hardware like NVIDIA’s Tensor Cores, have been specially optimized to perform 16-bit operations much faster than processing FP32. This is like an experienced chef who can quickly estimate the approximate amount for dishes that do not require extreme precision, thereby greatly speeding up cooking. Tests based on NVIDIA show that using FP16 can increase model running speed by 4 times, shortening the time to process 500 images from 90 seconds to 21 seconds.

2. Halved Memory Usage, Making Models “Light as a Swallow”
   Numbers in FP16 format take up only half the memory space of FP32. This means that AI models can occupy less video memory when running. For those large AI models with huge parameters, often tens or hundreds of GBs (such as large language models), adopting FP16 can significantly reduce their required storage space and memory consumption. This allows us to run larger models on limited hardware resources (such as personal computer graphics cards, edge devices, or mobile devices), or use larger data batches during training, thereby improving training efficiency.

3. Lower Energy Consumption, Becoming Part of “Green AI”
   The reduction in calculation volume and the improvement in memory access efficiency will naturally bring lower energy consumption. This is undoubtedly a good thing for AI data centers with huge energy consumption. At the same time, for deploying AI applications on resource-constrained terminal devices such as mobile devices, reducing energy consumption is also crucial.

The “Cost” of FP16: Challenges of Precision and Stability

There is no free lunch. Although FP16 quantization brings many benefits, it also comes with a major “cost”—precision loss.

Since FP16 uses fewer bits to represent numbers, the range of values it can express is smaller than FP32, and the precision of values (mantissa bits) is also reduced. This may lead to “overflow” (numbers too large to represent) or “underflow” (numbers too small to represent) problems in scenarios requiring extremely precise calculations. For the training process of AI models, especially gradient updates which require high numerical stability, the precision loss of FP16 may affect the convergence speed and final accuracy of the model.

This is like a chef simplifying a recipe. If the amount of certain key spices is not grasped accurately, although cooking is faster, the final taste of the dish may be affected.

Ingenious Solution: Mixed Precision Training

To achieve a perfect balance between efficiency and precision, AI researchers invented “Mixed Precision Training.”

This method is very smart: it does not “cut across the board” like FP16, but cleverly combines the advantages of FP16 and FP32. In mixed precision training, most calculations (such as forward propagation of the model and gradient calculation in backward propagation) will use the more efficient FP16 format. But for those precision-sensitive key operations, such as model parameter updates (weight updates) and loss function calculations, the high-precision format of FP32 will continue to be used.

This is like a shrewd head chef: for most work such as cutting vegetables and preparing ingredients, adopt the efficient “approximate” method; but at the critical moment of final seasoning and serving, use precise measuring tools to ensure the perfection of the final taste. This strategy can maximize the acceleration advantage of FP16 while ensuring the numerical stability and accuracy of the model through FP32. Currently, mainstream deep learning frameworks, such as PyTorch and TensorFlow, provide built-in support for mixed precision training.

Applications and Future Outlook of FP16

FP16 quantization (especially in mixed precision mode) has been widely used in various fields of AI:

• Accelerating Large Model Training: The training time for models requiring massive computing resources, such as large language models and image recognition models, can be significantly shortened.
• Optimizing Model Inference Deployment: When deploying trained models to various devices (such as mobile phones, edge AI devices on autonomous vehicles), FP16 allows models to run faster and consume fewer resources.
• Real-time AI Applications: In scenarios requiring instant response, such as real-time video analysis and voice assistants, the acceleration capability of FP16 is crucial.

Of course, besides FP16, there is also the BF16 (bfloat16) format launched by Google, which has the same number of exponent bits as FP32, thus ensuring a similar numerical range to FP32, but slightly lower precision than FP16. It is also a choice to balance efficiency and precision. Even with the advancement of technology, the industry is now exploring lower-precision quantization methods, such as INT8 (8-bit integer) and INT4 (4-bit integer), which can further compress model size and increase speed, but how to effectively control precision loss remains a research hotspot.

In summary, FP16 quantization is a very practical optimization technology in the AI field. By reducing the precision of number representation, it successfully brings faster calculation speed, lower memory usage, and higher energy efficiency to AI models, allowing AI technology to serve our lives more widely and efficiently. It’s like finding the most “economical and practical” calculation method for AI models, achieving “green” and “inclusive” while ensuring “intelligence.”

“Fairness-Aware Training” in AI: Making Intelligence More Just

Imagine you apply for a loan, but the AI system gives you a worse interest rate or rejects you outright because of your skin color or gender, without any reasonable justification. Or, you submit a resume, but an AI recruitment tool automatically filters you out because your name is not “mainstream”. This is not science fiction, but the potential “bias” and “injustice” that Artificial Intelligence (AI) may bring in its rapid development. To avoid such a future, the AI field has proposed a key concept — “Fairness-Aware Training”.

What is “Fairness-Aware Training”?

Simply put, “Fairness-Aware Training” is about enabling AI systems to act like impartial judges or teachers during their learning and decision-making processes—maintaining neutrality, not discriminating against any specific group or individual, and striving to eliminate bias as much as possible even when facing complex historical data, thereby providing fair results.

We can analogize this to a teacher evaluating students in a school. A good teacher would not let a student’s family background, appearance, or place of birth affect their grading. They strive to ensure that the assessment criteria for all students are consistent and fair, and pay attention to those who may be disadvantaged due to external factors (such as lack of good learning resources), giving them equal opportunities to learn and demonstrate their abilities. AI’s “Fairness-Aware Training” is precisely about playing such a role of a “good teacher” in the world of artificial intelligence.

Where Does AI Bias Come From? — The “Past and Present” of Intelligence

Why does AI generate bias? This is not because the AI system is “evil by nature”, but because it is like a fast-growing child whose values and behavioral patterns depend mainly on the “food” (data) it eats and the “environment” (algorithms) it grows up in.

1. “Unhealthy Recipes”: Data Bias
   AI systems learn and predict by analyzing massive amounts of historical data. If this training data itself carries historical bias or imbalance, AI will “follow suit”. For example, if in AI’s “teacher”—the training data—doctors are always male and nurses are always female, then when AI is asked to generate stories about doctors and nurses, it will automatically set doctors as male and nurses as female, even if you try to correct it multiple times. Similarly, if an AI model used for loan approval is trained primarily on historical loan data containing significant discrimination against certain minority groups, it may continue to perpetuate this discrimination, unfairly rejecting eligible loan applicants. This is like a child who has only seen books about male doctors and female nurses; when he grows up, he might default to thinking doctors are male and nurses are female.

2. “Imperfect Upbringing”: Algorithmic Bias
   Even if the data looks “clean” enough, improper algorithm design or optimization goals can introduce bias. For example, if an AI algorithm only pursues overall prediction accuracy during optimization without considering performance differences between different groups, it may lead to very low prediction accuracy for certain minority groups, resulting in unfairness. It’s like a chef who, even with balanced ingredients, uses a cooking method (algorithm) that focuses on only one type of taste, resulting in dishes that still fail to satisfy the preferences of all diners. Some biases may also stem from errors in labeling data, measurement errors, or unbalanced data classification.

How is “Fairness-Aware Training” Implemented? — AI’s Road to “Correction”

To address these issues, “Fairness-Aware Training” primarily adopts strategies at different stages of the AI system to help AI “distinguish right from wrong” and achieve fairness.

1. “Carefully Selecting Ingredients”: Data Pre-processing Stage
   This is the most fundamental step. Before the AI system learns, the training data needs to be strictly screened, checked, and balanced. This includes:

   • Ensuring Data Diversity and Representativeness: Avoiding situations where data for a certain group is scarce or a specific group is overrepresented in the dataset. For example, a facial recognition system trained primarily on white male data will have significantly lower accuracy when recognizing faces of other skin colors or females.
   • Eliminating Historical Bias: Carefully scrutinizing the data for traces of past social discrimination and attempting to correct them. It affects scenarios like banks training their loan approval AI; they cannot merely rely on past discriminatory loan approval history but need special processing to ensure applicants from different backgrounds have equal assessment opportunities.

2. “Customizing Cooking Recipes”: In-processing Stage
   When designing and training AI algorithms, “fairness” is incorporated as an important factor. This means that AI no longer pursues only so-called “high accuracy”, but finds a balance between accuracy and fairness.

   • Adding Fairness Constraints: Adding constraints during the core calculation process of the algorithm to force AI to consider the impact across different groups when making decisions. For example, researchers are exploring methods like adversarial training to improve model fairness by generating specific use cases, thereby balancing multiple sensitive attributes simultaneously.
   • Fair Representation Learning: Enabling the model to identify and prevent bias information associated with sensitive attributes (such as gender, race) from being encoded into the model’s representation when learning data features.

3. “Post-Dish Seasoning”: Post-processing Stage
   Even after the AI model is trained and starts working, we can still inspect and adjust its output results to ensure fairness.

   • Fairness Evaluation: Continuously monitoring the performance of the AI system on different groups and correcting it promptly once signs of bias are found.
   • Adjusting Decision Thresholds: Fine-tuning the decision thresholds of AI based on the characteristics of different groups to achieve overall fairness. This is like marking exams; if it is found that scores for a certain group are generally low, besides checking if the questions are fair, one can also examine if the grading standards need fine-tuning.

“Fair AI” is Closely Related to Our Daily Lives

“Fairness-Aware Training” is not just a technical issue; it profoundly affects our daily lives:

• Financial Services: In fields like loans and insurance, fair AI can ensure everyone gets equal financial opportunities, avoiding behaviors like “big data price discrimination” where algorithms are used to discriminate against specific groups on price.
• Recruitment and Selection: When applying AI in recruitment, tools undergoing fairness-aware training can avoid perpetuating historical biases, ensuring candidates are evaluated solely based on skills and qualifications, rather than other protected characteristics.
• Healthcare: In AI-assisted diagnosis and treatment recommendation, fairness is crucial. It ensures different patient groups receive accurate and appropriate medical services, without being neglected due to geography, economics, or other factors.
• Content Recommendation and Creation: In news recommendation, social media content distribution, and even generative AI for artistic creation, fairness-aware training can reduce the generation of stereotypes and provide diverse and inclusive content.

Even in education, with the widespread use of AI tools, we must also be wary of cultural biases that models trained on Western data may bring, ensuring the accuracy and relevance of AI educational content.

Future Outlook: Fairness and Intelligence Walking Together

Fairness-Aware Training is a continuous improvement process requiring the joint efforts of technical experts, ethicists, social scientists, and policymakers. Latest research shows that technological advancements, such as decentralized AI and blockchain technology, also have the potential to enhance AI fairness by providing higher transparency and preventing data tampering.

However, we must also clearly recognize that purely technical means are often difficult to completely eliminate bias, especially for fields like “Generative AI” where output quality involves subjective judgment. This requires us to focus not only on the technical details of AI but also on the setting of human values and ethical rules behind it. As some experts worry, when AI capabilities surpass humans across the board to form so-called “superintelligence”, ensuring its objective function aligns with human interests and making it fundamentally incapable of harming humans will be an unprecedented challenge.

Ultimately, making AI inclusive, trustworthy, and truly beneficial to all mankind cannot be achieved without the cornerstone of “Fairness-Aware Training”. Future artificial intelligence must not only have a high IQ but also a high EQ, understanding fairness and respect.

The “God-Speed Learning Method” in AI: FO-MAML — Enabling AI to “Learn by Analogy”

In today’s rapidly developing field of artificial intelligence, we often marvel at AI’s ability to complete various complex tasks. However, traditional AI models typically require “massive data” to master a skill, much like a student who needs to solve tens of thousands of similar problems to master a problem-solving method. But in the real world, we often don’t have that much data. For example, teaching AI to recognize a rare animal with only a few pictures, or letting a robot complete a new task in a new environment with only limited attempts.

To solve this “Few-Shot Learning” problem, scientists proposed “Meta-Learning“, the core idea of which is to let AI learn “how to learn“ rather than just learning a specific task. We can compare meta-learning to cultivating a “top student”: we don’t teach him specific knowledge points directly, but train him to master efficient learning methods, such as how to summarize and how to draw inferences from one instance. In this way, no matter what new subject he encounters, he can get started quickly and master it efficiently. This is exactly the goal of meta-learning — to equip AI with the ability to quickly adapt to new tasks.

FO-MAML, which stands for “First-Order Model-Agnostic Meta-Learning“, is an efficient variant of the MAML (Model-Agnostic Meta-Learning) algorithm. To understand FO-MAML, we must start with MAML.

MAML: Finding the “Best Starting Point” for Learning

Imagine you are an experienced chef with deep skills in making various dishes. Now, asked to learn a brand new recipe, you might only need to glance at the steps and taste it twice to master it quickly. This is because you have mastered a large amount of culinary “meta-knowledge”, such as knife skills, heat control, seasoning matching, etc. You don’t need to learn how to cut vegetables or boil water from scratch; you already have the “best starting point“ for cooking.

The idea of MAML is similar. It does not directly train a model to complete a specific task (such as recognizing cats), but trains the model to find a “super good“ initial parameter setting (like the chef’s deep foundation). With this good set of initial parameters, when the model needs to complete a brand new task (such as recognizing a “new species” like a pangolin), it only needs a small amount of data and very few adjustments (that is, a few gradient updates) to quickly adapt and perform well.

The training process of MAML can be understood as two loops:

1. Inner Loop (Task Adaptation): The model performs a small amount of learning and adjustment for a specific task using a small amount of data. Just like the chef adjusts the heat and seasoning according to the specific needs of the new dish.
2. Outer Loop (Meta-Learning): The model evaluates its performance after the adjustment in the inner loop, and then in turn uses this to optimize its “initial parameters”. The goal is to find a set of initial parameters that allows the model to achieve optimal performance in various different tasks with only a small number of adjustments. This is like a chef reflecting on and optimizing his basic skills after trying many new dishes to make them more adaptable to different cuisines.

The “Model-Agnostic” nature of MAML means that it is a universal framework that can be applied to different types of neural networks, such as Convolutional Neural Networks (CNNs) for image recognition or Recurrent Neural Networks (RNNs) for natural language processing.

FO-MAML: A Lighter and Faster “God-Speed Learning Method”

Although MAML is powerful, it has a significant drawback: the computational cost is very high. In the outer loop, to find that “best starting point”, MAML needs to calculate the so-called “second-order derivatives“.

“First-Order” vs. “Second-Order”: Slope and Curvature

We can use “going down a mountain” as an analogy.

• When you are standing on a hillside and want to rush down the mountain as fast as possible, the most direct way is to take a step in the steepest direction. This “steepest direction” is the information told by the first-order derivative. It tells you the downward trend and direction of your current position.
• But if you want to plan the route for the next few steps more precisely, you also need to know the “curvature” of the hillside — that is, whether the hillside is getting steeper or flatter, and whether there are sudden potholes or bumps. This information about “trend changes” is provided by the second-order derivative. It allows you to predict the future trend more accurately and plan your route.

MAML is the method that strives for perfection, calculating second-order derivatives to precisely plan every step of the “learning direction”. Although this can find a theoretically very good “best starting point”, it is very complex and time-consuming to calculate, especially on large deep learning models.

FO-MAML (First-Order MAML) was born to solve this problem. It adopts a more “pragmatic” strategy: it simply abandons the calculation of second-order derivatives and only uses first-order derivatives to guide the optimization of the model.

This is like when you go down a mountain, acting based on intuition rather than spending a lot of time calculating the precise curvature, simply walking step by step according to the steepest direction under your current feet. After each step, you re-evaluate the steepest direction at the current position and continue to step. Although it may not be as precise as careful calculation, it wins in speed and small computational volume. Surprisingly, practice has proven that for many tasks, the performance of FO-MAML is almost as good as the computationally complex MAML, and even achieved similar excellent performance on some datasets.

Advantages and Applications of FO-MAML

This “dimensionality reduction strike” of FO-MAML brings significant advantages:

• High Computational Efficiency: By avoiding complex second-order derivative calculations, the training speed of FO-MAML is greatly improved, and the memory required is also less, making it more attractive in resource-constrained scenarios or scenarios requiring rapid iteration.
• Simpler Implementation: The code implementation is relatively simpler than MAML, lowering the threshold for using meta-learning methods.
• Performance Not Discounted (In Most Cases): Although it is an approximate method, in many few-shot learning tasks, FO-MAML can achieve performance comparable to MAML.

Meta-learning algorithms like FO-MAML and MAML are mainly applied in:

• Few-Shot Image Classification: For example, training a model to recognize new objects or animal species with only a few pictures.
• Reinforcement Learning: Allowing robots to quickly learn new strategies through a small amount of trial and error when facing new environments or tasks.
• Personalized Recommendation: Quickly adjusting recommendation models based on very few new user interaction data to provide content that better fits user interests.

Summary

FO-MAML represents an innovative idea in the AI field of “trading precision for speed without losing efficiency”. By simplifying complex mathematical calculations, it makes the cutting-edge technology of meta-learning — “letting AI learn how to learn” — more practical and easier to promote. In real-world scenarios where data is scarce, algorithms like FO-MAML endow AI with stronger adaptability and generalization capabilities, allowing AI to quickly “draw inferences from one instance” like humans when facing new knowledge and challenges, thereby promoting the continuous development of artificial general intelligence.

FLAN-T5: The “All-Round Top Student” of the AI World

The field of AI is evolving rapidly, and one concept that has garnered significant attention is FLAN-T5. For non-professionals, these technical terms might seem deep and unfathomable. Don’t worry, this article will use the most vivid analogies to help you easily understand FLAN-T5.

What is FLAN-T5? The “All-Round Top Student” in AI

Imagine there is a “Language University” in the AI field that cultivates various “students” who process language. FLAN-T5 is a particularly outstanding “all-round top student” in this university. This student is not only knowledgeable but, more importantly, very good at understanding and executing various “instructions”. No matter what task you ask him to do, he can do his best to complete it quickly and well.

The full name of FLAN-T5 is “Fine-tuned LAnguage Net - Text-to-Text Transfer Transformer”. Sound complex? We can break it down into two core parts to understand: the T5 model (Text-to-Text Transfer Transformer) and the FLAN fine-tuning method (Fine-tuned LAnguage Net).

1. T5 Model: The “Universal Translator” of the AI World

First, let’s get to know “T5”. The T5 model is a unique language processing framework proposed by Google. Its core idea is to unify all natural language processing tasks into a “Text-to-Text“ format. This means that whether it is translation, summarization, question answering, or any other language task, for T5, the input is a piece of text, and the output must also be a piece of text.

For example:

• Input: “Translate ‘Hello’ to French.”

• Output: “Bonjour.”

• Input: “Summarize the core idea of this article: [A long article].”

• Output: “[Summarized core idea].”

• Input: “What is the direction of the Earth’s rotation?”

• Output: “The Earth rotates from west to east.”

You can think of T5 as a very smart “translator”, but what it “translates” is not just different languages, but it translates all language tasks into a unified pattern that it can understand and process. It’s like a super chef who can process all ingredients (inputs for various tasks) into a unified “pre-prepared” form, and then cook delicious dishes (task outputs).

2. FLAN Fine-tuning: The “Master of Instructions” in the “Boot Camp”

Although the T5 model is powerful, it initially learned language rules and knowledge only by reading massive amounts of books (massive text data), just like a university graduate who has sufficient knowledge reserves but lacks practical experience and clear guidance.

The “FLAN“ part is a special “intensive training camp” for T5, which we call “Instruction Tuning“.

Traditional Fine-tuning is like letting this university graduate enter a company and receive professional training specifically for a specific position (such as a contract reviewer). He will become very good at contract review, but if he is suddenly asked to write a market analysis report, he may be helpless.

Instruction Tuning, on the other hand, is completely different. It’s like preparing a thick “All-Round Assistant Work Manual” for this graduate. The manual does not contain deep professional knowledge but contains hundreds or thousands of different “instructions” and corresponding “standard examples”, such as:

• Instruction: “Summarize the core viewpoints of this news for me.” → Example Answer: “The core viewpoint of this news is…”
• Instruction: “Write an email in a friendly tone to decline Mr. Li’s meeting invitation.” → Example Answer: “Dear Mr. Li, thank you very much for your invitation…”
• Instruction: “Tell me a joke about programmers.” → Example Answer: “Why do programmers prefer dark mode? Because light attracts bugs…”

By reading and imitating this “Work Manual”, this “student” learned:

• Understanding Instructions: Seeing “summarize” knows to make a summary, seeing “translate” knows to convert languages.
• Drawing Inferences: Even if encountering a brand-new instruction not in the manual, he can give a reasonable answer based on past experience and understanding of instructions.

FLAN fine-tunes the model on a super-large scale dataset with over 1,800 different task instructions (Instruction Tuning), giving the T5 model extremely strong generalization and instruction-following capabilities. In this way, once the model is trained, it can be directly used in almost all natural language processing tasks, achieving the goal of “One model for ALL tasks“.

Superpowers of FLAN-T5: Why is it so powerful?

The power of FLAN-T5 stems from T5’s “universal translator” physique plus FLAN’s “instruction master” training:

1. Super Strong Task Generalization: FLAN-T5 can handle a wide variety of tasks, such as text summarization, machine translation, Q&A, sentiment analysis, and even text correction and content creation. You can give it an instruction and ask it to complete almost any language task you can think of. It’s like that “all-round top student”, who has good learning methods, so he can cope with whatever exam questions come.
2. “Zero-Shot” and “Few-Shot” Learning: This means that for a brand-new task, FLAN-T5 can achieve good results even if it has never seen relevant examples, relying on its understanding of instructions and generalization capabilities (Zero-Shot Learning). If you give it a few more examples, its performance will be even better (Few-Shot Learning). Imagine a top chef, even for a new dish he hasn’t cooked, as long as you give him the recipe (instruction), he can make it, or even if he has done it once or twice (few samples), he can do it perfectly.
3. Excellent Performance: After FLAN instruction fine-tuning, the T5 model has significantly improved performance on various tasks, even surpassing human performance in some benchmarks.

Latest Progress and Applications of FLAN-T5

Since the release of FLAN-T5, it has received widespread attention from the industry and continues to develop. Currently, FLAN-T5 shows huge application potential in many fields:

• Content Creation and Writing Assistance: It can understand prompts, generate coherent and creative text, and help users create articles, emails, etc.
• Intelligent Customer Service: Extract information from the knowledge base and generate accurate answers based on user inquiries, improving service efficiency and user experience.
• Education Field: Assist students in learning through Q&A forms, text summarization, etc.
• Text Correction: Perform grammar and spelling correction on input text to improve text accuracy and readability.

FLAN-T5 and its related instruction fine-tuning methods have greatly promoted the development of Large Language Models (LLMs), enabling AI models to better understand human intent and serve various practical applications in a more flexible and general way. With the continuous evolution of technology, AI models like FLAN-T5 will become more lightweight, support multimodal integration (combining visual, voice, and other information), and provide a higher degree of personalization and cross-language support. The future application prospects are limitless.

The “Fuel” of the AI World: Understanding FLOPs in Simple Terms

In the vast universe of Artificial Intelligence (AI), we often hear terms like “computing power” and “calculation volume”. They are like the foundations supporting skyscrapers, determining how far AI models can go and how powerful they can become. Beneath these foundations lies a core unit of measurement called FLOPs. It is not only the key to measuring the “strength” of AI models but also drives the entire AI field forward in its constant evolution.

What exactly is FLOPs? Why is it so important for AI? For non-professionals, we can understand it vividly through some daily life analogies.

I. FLOPs: The “Floating Point Recipe” and “Speedometer” of the AI World

When we mention FLOPs, we are actually referring to two related but slightly different concepts:

1. FLOPs (Floating Point Operations): The lowercase “s” stands for plural, referring to the “number of floating-point operations”. It basically means the total number of such mathematical operations required in a single AI calculation task (such as letting AI recognize a picture). It measures calculation volume (workload).
2. FLOPS (Floating Point Operations Per Second): The uppercase “S” stands for “Per Second”, referring to the “number of floating-point operations per second”. It means how many floating-point operations computer hardware can complete in one second. It measures computing speed or hardware performance.

To make it easier to understand, throughout this article, we mainly focus on the uppercase FLOPS to explain its significance in measuring computing power (hardware performance).

Daily Analogy: A Complex Cooking Feast

Imagine you are preparing an extremely sumptuous, multi-step dinner. This dinner requires a lot of chopping, stirring, weighing, heating, and other operations.

• Floating Point Operations: It’s like every specific operation in the recipe, such as “mix 2.5 grams of salt into 1.5 liters of water”, or “mix flour and water in a ratio of 3.14:1”. These operations involve decimals and are relatively precise calculations. AI models, especially neural networks, perform a large number of addition, subtraction, multiplication, and division operations involving decimals when processing data. This is floating-point operation.

• FLOPs (Total Operations): It is the total number of all chopping, stirring, weighing, and other operations required to complete this dinner. A more complex dish (such as an AI model with a huge number of parameters) requires more total operations. For example, for a large model like GPT-3, the FLOPs for a single inference can reach about 200 billion.

• FLOPS (Operations Per Second): It is how many such operations you (or your kitchen helper) can complete in one second. If you are a Michelin chef who can chop several slices of vegetables and stir sauces several times in a second, then your FLOPS is very high, and your cooking efficiency is very fast. Conversely, if your movements are slow, FLOPS is low. Computer CPUs, GPUs, and other hardware, the number of floating-point operations they can complete in one second is their FLOPS indicator.

So, simply put, FLOPs (lowercase s) tells you “how much workload is needed” to complete the task, while FLOPS (uppercase S) tells you “how fast your tools can complete the work”.

II. The “Core Engine” Role of FLOPs in the AI Field

AI, especially deep learning, essentially involves massive floating-point operations in its training and inference processes. Whether it is image recognition, speech recognition, or large language models (such as ChatGPT), they are inseparable from huge calculation volumes.

1. Measuring the “Appetite” and “Efficiency” of AI Models

FLOPs is a basic indicator for measuring the computational complexity of machine learning models.

• Model Complexity: A more complex AI model, such as a Large Language Model (LLM) with huge parameters, will require a very high total number of floating-point operations (FLOPs) when processing a task. This is like a dish with more procedures requiring more total operations.
• Model Efficiency: Lower FLOPs usually mean that the model runs faster and requires less computing power. This is especially important for resource-constrained devices (such as mobile phones and edge AI devices). Researchers often strive to design models with lower FLOPs but still powerful performance. For example, architectures like EfficientNet are dedicated to reducing computational costs without sacrificing performance.

2. Assessing Hardware “Horsepower” and “Speed”

Computer CPUs, especially Graphics Processing Units (GPUs) or dedicated AI chips (such as TPUs) used for AI, are powerful because they can perform floating-point operations at extremely high FLOPS.

• Training Models: Training large AI models is like teaching a student to learn massive amounts of knowledge. This requires extremely powerful FLOPS hardware to complete in a reasonable time. The large-scale hardware computing power (TFLOPS level) in data centers is key to training models.
• Inference Application: After the model is trained, letting it actually “work” (such as recognizing a picture or answering a question), this process is called inference. Inference also requires computing power, but usually requires lower FLOPS than training, focusing more on low latency and high throughput. AI applications on mobile devices (such as face recognition) need to choose models with lower FLOPs to ensure they run quickly and without consuming too much power under limited hardware FLOPS.

III. FLOPs Units: From Single Digits to “Cosmic Level”

To represent huge floating-point operation counts and speeds, FLOPs are often expressed in the following units:

• Single FLOPs: One floating-point operation.
• KFLOPS: Kilo Floating Point Operations Per Second ($10^3$).
• MFLOPS: Mega Floating Point Operations Per Second ($10^6$).
• GFLOPS: Giga Floating Point Operations Per Second ($10^9$).
• TFLOPS: Tera Floating Point Operations Per Second ($10^{12}$). Many high-performance AI chips calculate computing power in TFLOPS. For example, the Apple M2 GPU has a performance of 3.6 TFLOPS, while the RTX 4090 offers 82.58 TFLOPS.
• PFLOPS: Peta Floating Point Operations Per Second ($10^{15}$).
• EFLOPS: Exa Floating Point Operations Per Second ($10^{18}$).
• ZFLOPS: Zetta Floating Point Operations Per Second ($10^{21}$).

Latest information shows that the peak computing power of GPUs has exceeded 3000 TFLOPS (FP8), while certain AI-specific ASICs (such as Huawei Ascend 910) can reach 640 TFLOPS at FP16 precision. This huge computing power allows AI model training to complete the training of trillion-level models within a “month” level timeframe.

IV. FLOPs and AI Development: Computing Power is Productivity

“Computing power is the ‘core engine’ of the artificial intelligence era.” It is both the “engine” for model training and the “transmission” for inference deployment. Without powerful computing power, no matter how exquisite the algorithm or how huge the data is, it can only stay at the theoretical stage.

• The Era of Large Models: With the rise of large language models like GPT-3 and GPT-4, the number of parameters of AI models has grown exponentially, and the demand for computing power for training and running them has reached unprecedented heights. For example, OpenAI may have used 25,000 A100 equivalent cards to train GPT-4, with a total computing power close to $2.1 \times 10^{25}$ FLOPs. This huge computing demand has directly promoted the development of AI-specific chips such as GPUs and high-performance computing clusters.
• Computing Power Race: Currently, major technology companies are launching a “computing power arms race” globally, scrambling to launch AI chips and servers with higher FLOPS. For example, NVIDIA dominates the AI chip market, and its GPUs have become the “absolute main force” for AI training with powerful parallel computing capabilities and the CUDA ecosystem. AMD, Google TPU, etc., are also constantly exerting force, and even cloud computing giants are developing their own chips to cope with huge computing power demands.
• Efficiency Optimization: In addition to pursuing higher FLOPS, how to run AI models more efficiently with limited computing power has also become key. Conditional Computation such as MoE (Mixture-of-Experts) architecture, by activating part of the “expert” networks in the model, can significantly reduce the computational cost (FLOPs) of a single inference while the total number of parameters remains unchanged. This is like in the same kitchen, not all chefs cook every dish at the same time, but chefs who are good at different dishes collaborate according to the needs of the dishes, greatly improving overall efficiency.

V. Conclusion

Just as the steam engine drove the First Industrial Revolution and electricity drove the Second Industrial Revolution, powerful computing power, especially AI computing power measured by FLOPs, is becoming the “new engine” driving the development of artificial intelligence and even the entire digital economy. Understanding FLOPs means understanding one of the most fundamental power sources in the AI world. It tells us that every progress in AI is inseparable from the support of thousands, even astronomical numbers of floating-point operations behind it. With the continuous breakthrough of computing power technology, the future of AI will also have infinite possibilities.

The “Litmus Test” of the AI World: F1 Score — The Art of Balance

In the vast universe of Artificial Intelligence (AI), we often hear how “smart” various models are, capable of recognizing images, translating languages, and even diagnosing diseases. But how “smart” is a model exactly, and how do we know? This requires some “touchstones” to measure. Today, we are going to talk about the F1 Score, which is a very important and ingenious “touchstone” in the AI evaluation system. It not only tells us how well the model is doing but also helps us discover potential “bias” problems.

To make this seemingly complex concept understandable to non-professionals, we will use small stories and concrete examples from life to unveil the mystery of the F1 Score.

1. Why Can’t We Just Look at “How High the Score Is”?

Imagine you are an experienced fisherman who wants to use a new smart fishing net to catch fish in a lake. You cast the net and pull up a pile of things. You are very happy because you caught a lot of fish! But if I ask you: “Is this fishing net effective?” How would you answer?

Most intuitively, you might say: “I pulled up 100 items, 95 of which are fish and 5 are weeds! Simply perfect!” This is what we often call high “Accuracy”. But is this enough?

Let’s define “fish” as the “target” (positive sample) we want to find, and “weeds” as the “non-target” (negative sample) we don’t want to find.

• Scenario 1: There are only 100 fish and 0 weeds in the lake. You caught 95 fish and 5 weeds. The accuracy seems high.
• Scenario 2: There are 100 fish and 10,000 weeds in the lake. You still caught 95 fish and 5 weeds. Your accuracy is (95+9995) / (95+5+10000) = 99.9%! Wow, super high! But you actually only caught 9.5% of the fish in the lake (95/100). Although the accuracy is extremely high, does your fishing net have too many “fish slipping through the net”?

This example reveals a problem: when our “target” is rare (e.g., few fish in the lake, many weeds), or “non-target” is very rare, pure “Accuracy” might deceive us. A model might “pretend” to be accurate simply because it correctly identifies most “non-targets” as “non-targets”, while performing mediocrely on real “targets”.

2. Precision and Recall: The Fisherman’s Dilemma

To evaluate our fishing net more precisely, we need to introduce two core concepts: Precision and Recall.

Precision: What proportion of things caught are really fish?

Continuing our fisherman story. You cast the net and pull up 100 things, of which 95 are fish and 5 are weeds. Your precision is:
Precision = Number of real fish caught / (Number of real fish caught + Number of weeds misidentified as fish)
Precision = 95 / (95 + 5) = 95%

This means that 95% of the things you pulled up are indeed the fish you wanted. The higher the precision, the more “accurate” your fishing net is, and the less junk it falsely reports. For an AI model, high precision means that most of the things it “says are yes” are indeed correct.

A vivid analogy: Looking for a celebrity in a crowd, if I say “the one wearing sunglasses is a celebrity”, and it turns out that 9 out of 10 people wearing sunglasses are really celebrities, this means my “Precision” is high.

Recall: What proportion of all fish in the lake did you catch?

Now let’s look at another angle. Suppose there are a total of 100 fish in the lake, and your fishing net caught 95. Then your recall is:
Recall = Number of real fish caught / (Total number of fish actually in the lake)
Recall = 95 / (95 + 5 (fish not caught)) = 95%

This means that you caught 95 out of 100 fish in the lake. The higher the recall, the “fuller” your fishing net’s ability to catch fish, and the fewer fish slip through the net. For an AI model, high recall means that it discovers as many existing targets as possible.

A vivid analogy: If there are 10 celebrities in this crowd and I found 9 of them, then my “Recall” is high.

The “Seesaw” of Precision and Recall

These two indicators often restrict each other. If you want to improve recall, for example, if you make the mesh of the fishing net very large, or cast the net many times, you may catch more fish, but you may also catch more weeds, causing precision to decrease. Conversely, if you want to improve precision, for example, only catch big fish that you recognize at a glance, you may miss many small fish, causing recall to decrease.

For example:

• Spam Classification:
  • If the model is very “careful” and only marks emails that are 100% sure to be spam (high precision), then the spam in your inbox may decrease, but some real spam may slip through (low recall).
  • If the model is very “aggressive” and marks all suspicious emails to avoid missing any spam (high recall), then your normal emails may be misjudged as spam (low precision), causing you to miss important information.

3. F1 Score: Finding Balance Between Precision and Recall

So, is there a way to “package” these two mutually restrictive indicators, Precision and Recall, into a single number that reflects both the “accuracy” and “completeness” of the fishing net?

This is when the F1 Score comes into play!

The F1 Score is the Harmonic Mean of Precision and Recall. Its calculation formula is:

F1 Score = 2 * (Precision * Recall) / (Precision + Recall)

Wait, what is “Harmonic Mean”? Don’t worry, we don’t need to delve into mathematical principles. You just need to know that unlike the common arithmetic mean (such as (A+B)/2), the harmonic mean penalizes extreme values (such as one very high and one very low) more.

What does this mean?
If your precision is high (say 90%) but recall is low (say 10%), the F1 score will be relatively low.
F1 = 2 * (0.9 * 0.1) / (0.9 + 0.1) = 2 * 0.09 / 1 = 0.18

Conversely, if both precision and recall are high and close (say both are 90%), the F1 score will also be high.
F1 = 2 * (0.9 * 0.9) / (0.9 + 0.9) = 2 * 0.81 / 1.8 = 0.9

So, the F1 score is like a manifestation of the “Bucket Theory”: it values your “short stave” more. Only when both precision and recall are relatively high will the F1 score be high. It encourages the model to find an optimal balance point between the two.

4. Where is the F1 Score Needed?

The F1 score is crucial in many scenarios in the AI field, especially when data is very imbalanced (like the situation where there are few fish and many weeds in the lake mentioned earlier).

Disease Diagnosis (such as Cancer Screening)

AI models are used to judge whether a medical image shows a certain disease.

• High Precision is important: If precision is low, the model misjudges healthy people as sick, which will lead to unnecessary anxiety and further checkups, wasting medical resources.
• High Recall is more important: If recall is low, the model misses real patients, which may delay treatment and cause serious consequences.
  In this case, we need an indicator that balances both but tends towards recall. The F1 score can help us find a model that strikes a balance between diagnostic accuracy and not missing diagnoses.

Financial Fraud Detection

In a normal transaction, fraud is extremely rare.

• High Recall: Detect all frauds in time to avoid company losses. However, too low precision will lead to a large number of normal transactions being mistakenly blocked.
• High Precision: Reduce false reports and avoid refusing normal customers’ transactions for no reason, affecting user experience.
  The F1 score helps us evaluate the model here so that it can catch most frauds without overly interfering with normal business.

According to the latest information, the F1 score is widely used in the field of Natural Language Processing (NLP), such as in text classification, named entity recognition, and machine translation tasks, where it is often mentioned when evaluating model performance. This is because many NLP tasks also face the problem of class imbalance, or assessing the model’s recall ability and precision ability is equally important.

5. Summary: F1 Score — A Qualified Balancer

The F1 score is not a panacea, but it is a very practical evaluation indicator. It teaches us:

• Don’t just look at the surface, look behind the data. Pure “accuracy” may hide problems.
• Understand task goals and weigh the importance of different indicators. In some scenarios, precision is more important, and in others, recall is more important.
• Seek balance, not extremes. Often, a model that achieves a good balance between precision and recall is more valuable than a model that performs extremely well on one indicator and terribly on another.

Next time you see how amazing a “ninety-something percent accuracy” an AI model has achieved, ask one more question: “What is its F1 score?” This will help you understand the true performance of the AI model more comprehensively and deeply, making you a wiser AI observer.

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**References:**
[1] F1 score is a performance metric commonly used in various natural language processing (NLP) tasks. For instance, in named entity recognition (NER), where the goal is to identify and classify named entities (like person names, organizations, locations) in text, the F1 score is often used because it balances the precision (how many identified entities are correct) and recall (how many actual entities were identified) of the model's predictions. The F1 score is particularly valuable when dealing with potential class imbalance, or when both false positives and false negatives have significant costs, making it a robust choice for evaluating the nuanced performance of NLP models.
[2] In the realm of medical AI diagnosis, the F1 score is critical for evaluating models, particularly in the detection of rare diseases. For example, when an AI model is developed to identify a rare cancer from medical images, correctly identifying positive cases (true positives) is crucial (high recall to avoid missing actual cases), but misidentifying healthy individuals as having cancer (false positives) can lead to unnecessary distress and further invasive tests (high precision to avoid false alarms). The F1 score provides a balanced assessment that ensures the model is not only accurate in its positive predictions but also comprehensive in identifying most existing cases, which is vital in high-stakes medical applications where both types of errors carry significant consequences.

Exponential Moving Average: The “Memory Master” of AI

In the fascinating world of artificial intelligence, data is its lifeline, and effective analysis and processing of data is the key to AI success. Today, we are going to talk about “Exponential Moving Average” (EMA), a “memory master” who contributes silently behind the scenes but is crucial. It helps AI models better understand trends, filter noise, and make wiser decisions.

Starting from “Arithmetic Mean”: Traces of Memory

To understand EMA, let’s start with the “arithmetic mean” we are all familiar with. Imagine you measure the average height of a class of students every day. The simplest way is to add up the heights of all students and divide by the total number of students. This is like Simple Moving Average (SMA).

SMA also has applications in the AI field. For example, if you want to track the price trend of a stock, you can calculate the average closing price of the past 10 days. Every day, you add the latest price, kick out the oldest price, and recalculate the average.

Daily Analogy: Your Monthly Average Expenses.
If you want to know your average spending this month, you add up all spending this month and divide by the number of days. If you want to see the average spending of the past 5 days, then every day you include the latest spending and “forget” the oldest day’s spending. What you calculate is the simple moving average spending of the past 5 days.

Limitations of SMA: Indiscriminate “Amnesia”
Although SMA is simple and intuitive, it has a drawback: it treats all data points equally. Whether it is spending 5 days ago or yesterday, it is given the same weight. This means that if you had a particularly large expense yesterday, or prices suddenly rose, SMA’s reaction would be relatively sluggish because it is averaged out by those “old” data. It lacks sensitivity to “latest information” and cannot reflect quickly when trends change.

Meet EMA: A “Biased” Average

Now, let’s introduce “Exponential Moving Average” (EMA). It is also an averaging method, but it has an important feature: It “favors” the latest data, giving them higher weight; while for past data, the weight decays exponentially over time. In other words, EMA is an average with “memory”, but its memory is “strong for the near and weak for the far”.

Daily Analogy: Your Academic Grades.
Imagine your final grade. Some teachers will simply average all your homework and exam scores (this is like SMA). But a more common practice is that recent exam scores often have higher weights and represent your current knowledge level and learning status better, while the weights of several quizzes at the beginning of the semester will be much lower. For example, the final exam accounts for 50%, the midterm exam accounts for 30%, and daily homework accounts for 20%. EMA’s calculation method is similar to this “biased” grade calculation method, which believes that “freshly baked” data has more reference value.

How EMA Works (Simplified):
There is a key parameter in the EMA formula called “smoothing factor” or “decay rate”, usually denoted by $\alpha$ (alpha) or $1-\beta$ (1-beta). This factor determines the weight distribution of the latest data and historical data.

Simply put, every time a new EMA value is calculated, it combines two parts:

1. Current latest data value (such as the stock price of the day, the latest student grade).
2. The EMA value calculated at the previous time point (representing the weighted average of all previous historical data).

New EMA = ($\alpha$ * Current Latest Data) + ((1 - $\alpha$) * Previous EMA Value)

Here, the value of $\alpha$ is usually a decimal between 0 and 1, such as 0.1, 0.01, or even smaller (often close to 1 in AI, such as 0.999). The larger $\alpha$ is, the more sensitive EMA is to the latest data and the faster it changes; the smaller $\alpha$ is, the smoother EMA is and the less sensitive it is to short-term fluctuations.

EMA as a “Behind-the-Scenes Hero” in AI

EMA is not just a statistical concept; it plays a vital role in artificial intelligence, especially deep learning. It is the “internal organ” of many efficient AI algorithms.

1. Core of Optimizers:
   When training a neural network, we need to constantly adjust the model’s parameters (such as weights and biases) to improve its performance. This adjustment process is completed by an “optimizer”. Many advanced optimization algorithms, such as Adam, RMSprop, and Momentum, cleverly use the idea of EMA.

   • Momentum: It calculates the exponential moving average of the gradient, so that the parameter update does not only depend on the current gradient but also considers the previous update direction. This is like a ball rolling down a hill; even if it encounters a small pit, it can continue to move forward, avoiding being stuck by small local obstacles.
   • RMSprop and Adam: These optimizers go a step further based on Momentum. They not only perform EMA processing on the average value of the gradient (first moment estimation) but also perform EMA processing on the square of the gradient (second moment estimation). In this way, they can adaptively adjust the learning rate for each parameter, making the model more stable and efficient during the training process. For example, the Adam optimizer calculates adaptive learning rates for each parameter by tracking the exponentially decaying averages of past gradients (first moment) and past squared gradients (second moment).

2. Smoothing and Stabilization of Model Weights:
   In the later stages of deep learning model training, the model’s weights may oscillate back and forth near the optimal solution, making it difficult to stabilize. Using EMA technology can perform a weighted average on the model’s weights, making weight updates smoother, thereby obtaining a more stable model with stronger generalization ability. This is called the “Exponential Moving Average model”. This smoothing process can improve the robustness of the model on test data, i.e., the model’s performance on new data. In practical applications, a “shadow variable” is usually maintained to store the EMA-processed parameter values, and the decay rate (usually close to 1, such as 0.999 or 0.9999) controls the speed of model updates; the larger it is, the more stable it tends to be.

3. Time Series Analysis and Prediction:
   EMA itself is a classic time series data analysis method, widely used in fields such as financial market forecasting and commodity price trend analysis. By embedding EMA into deep learning models such as Recurrent Neural Networks (RNN) or Long Short-Term Memory networks (LSTM), more complex nonlinear models can be built to better capture the dynamic changes of time series data and improve the prediction accuracy and stability of the model.

Latest Progress and Future Outlook

• Deepening Application of AI in Financial Forecasting: In recent years, AI technologies, including EMA and its derivative algorithms, have been widely used in moving average analysis in the stock market. Machine learning algorithms can automatically identify and optimize the parameter settings of moving averages to improve prediction accuracy. Deep learning models can process large amounts of historical transaction data and learn the parameter combinations that best reflect real market trends.
• Optimizing EMA Application: EMA is often applied at the end of training to obtain model weights that are more stable and have stronger generalization capabilities. In the early stages of training, the model adapts to data changes quickly, and using EMA at this time may lead to excessive smoothing, so some studies suggest delaying the application of EMA until the later stages of training.
• Integration with Other AI Technologies: The combination of EMA with other AI technologies, such as ViT models combined with attention mechanisms, can improve the performance of tasks such as image classification. In addition, combined with other technical indicators or Natural Language Processing (NLP) technology to analyze news reports and social media sentiment, AI can provide more comprehensive market insights.

Although AI technology has brought revolutionary changes to the application of EMA, it also reminds us that any model has its limitations, and excessive reliance on AI may lead to errors in judgment.

Summary

Exponential Moving Average (EMA) is like a wise “memory master”. It understands the principle of “living in the moment”, giving more attention to the latest information while not ignoring past experiences. This unique way of processing information makes it an indispensable tool in the field of AI. From optimizers training neural networks to smoothing model parameters and analyzing time series data, EMA silently improves the efficiency and intelligence level of AI systems. With the continuous development of AI technology, the application scenarios and effects of EMA will continue to be explored and researched in depth.

Exponential Moving Average Demo

The “Smoke and Mirrors” of AI: A Brief Analysis of FGSM

In today’s world where artificial intelligence, especially deep learning models, is becoming increasingly popular, we often marvel at their outstanding performance in tasks such as image recognition and voice processing. However, these seemingly powerful AI models can sometimes be deceived by “small tricks” that are almost imperceptible to our naked eyes. One of the classic “smoke and mirrors” techniques is what we are going to introduce in simple terms today — Fast Gradient Sign Method, abbreviated as FGSM.

I. What is FGSM? Where is the AI’s “Achilles’ Heel”?

Imagine you have a very smart assistant who can accurately identify various objects. You give it a picture of a panda, and it immediately tells you it’s a “panda”. But if someone makes extremely tiny, almost invisible changes to the photo, your assistant might suddenly get “confused” and firmly tell you it’s a “gibbon”! And you look again and again, still feeling that it is clearly a panda.

These special inputs produced by “small tricks” are called Adversarial Examples in the AI field. They are carefully constructed data that are almost indistinguishable from original data to humans but can cause AI models to make wrong judgments. FGSM is a classic and efficient method for generating such adversarial examples.

Why does AI have such a “soft rib”? Early on, people thought this might be related to the nonlinearity or overfitting of the model, but later research found that the “linear” nature of neural networks in high-dimensional space is the main reason. Simply put, when the model makes a judgment, it “thinks” along a certain “direction”, and FGSM uses this “thinking direction” of the model to lead the model’s “thinking” to the wrong direction through minor adjustments.

II. How Does FGSM Perform “Smoke and Mirrors”? (Taking Image Recognition as an Example)

To understand the principle of FGSM, we can use an analogy from daily life:

[Analogy 1: The “Cheat Sheet” for Exam Cheating]

Suppose your AI model is a student taking an exam, and it needs to identify whether a picture is a “cat” or a “dog”. Through learning (training), it has mastered various characteristics of “cats” and “dogs”.

Now, you want it to see a “cat” as a “dog”. You can’t directly remove the cat’s ears or add a dog’s nose (this is equivalent to a huge change in the image, which the human eye can also see). You have to think of a “smart” way. FGSM is like quietly writing a line of extremely tiny “cheat sheet” notes with a pencil in a corner of the test paper, which the teacher usually can’t find but happens to “remind” the student to associate with “dog”. This “cheat sheet” is the perturbation added by FGSM.

How is this “cheat sheet” generated? The core idea of FGSM can be broken down into three keywords: Gradient, Sign, and Fast.

1. Gradient: Identifying the Model’s “Sensitive Points”

   • Daily Analogy: Imagine you are climbing a mountain and want to reach the top as fast as possible. Every step you take, you look at which direction has the steepest upward slope. This “steepest upward direction” is the gradient.
   • In FGSM: For the AI model, it calculates the “sensitive points” and “sensitive directions” that have the greatest impact on the classification result. This “sensitive point” is the pixel in the image, and the “sensitive direction” is the gradient of the Loss Function with respect to the input image. The loss function measures the “degree of error” of the model’s prediction. The goal of the model is to minimize the loss function. While the goal of FGSM is the opposite, it wants to increase the loss function, that is, to make the model make mistakes. By calculating the gradient, we can know which pixels of the image to change, and in which direction, to most effectively increase the model’s error.

2. Sign: Determining the Direction of “Cheating”

   • Daily Analogy: You found the direction with the steepest slope (gradient). If you want to go down the mountain, you go in the opposite direction. When you only want to know whether it’s uphill or downhill, and don’t care how steep the slope is, you only need to know the direction (positive or negative).
   • In FGSM: FGSM only cares about the “direction” of the gradient, not its “magnitude”. It takes the sign of the gradient. This means that for each pixel, if the gradient is positive, we slightly increase the value of this pixel; if it is negative, we slightly decrease it. The advantage of doing this is that it can maximize the increase in loss while ensuring that the perturbation added to the image is minimal and uniform.

3. Fast: Efficient Generation in One Step

   • Daily Analogy: Exam time is limited. You can’t spend too much time thinking about how to write the “cheat sheet”. It is best to write it quickly and use it quickly.
   • In FGSM: The “fast” of FGSM lies in that it only needs one step to generate adversarial examples. It is not like some other more complex attack methods that require multiple iterative adjustments. Through one gradient calculation and sign extraction, it can obtain a tiny perturbation, add it directly to the original image, and thus generate an adversarial example.

The generation formula of FGSM can be simplified as:
Adversarial Example = Original Image + (ε * Gradient Sign)
Where ε (epsilon) is a very small value used to control the size of the perturbation, ensuring that the human eye cannot perceive it.

[Classic Case: Panda Turns into Gibbon]
A famous example is that an AI model is 99.3% confident that a picture of a panda is a panda. After adding a tiny perturbation that is almost imperceptible to the human eye through FGSM, the model is 99.9% confident that the same picture is a gibbon.

III. What Does FGSM Mean?

The emergence of FGSM reveals an important security risk of current AI models:

• Model Vulnerability: Even the most advanced deep learning models currently can make completely wrong judgments due to tiny, imperceptible changes in input data.
• Security Risks: In application scenarios with extremely high security requirements such as autonomous driving, medical diagnosis, and financial fraud detection, adversarial examples may be maliciously used, leading to serious consequences. For example, sticking tiny stickers on traffic signs can make self-driving cars misidentify signs.
• Promoting Research: As a simple and effective attack method, FGSM has stimulated a large amount of research on the robustness (anti-interference ability) of AI models. Researchers are actively exploring how to make AI models resist such “smoke and mirrors”, such as through Adversarial Training, that is, incorporating adversarial examples into the model’s training data so that the model learns to identify and resist these attacks.

IV. Latest Progress and Future Challenges

Although FGSM is simple, it is the cornerstone of all adversarial attack and defense research. In recent years, researchers have developed more complex attack methods based on this, such as Iterative FGSM (I-FGSM), PGD, etc., which usually generate stronger adversarial examples by iteratively applying the idea of FGSM. At the same time, defense methods for adversarial examples are also constantly progressing, from modifying model architectures to introducing new training strategies.

In summary, FGSM is like a mirror, reflecting the vulnerabilities behind the powerful capabilities of AI models. Understanding FGSM deeply is not only for defending against attacks but also for better understanding the nature of AI, so as to build safer, more reliable, and trustworthy intelligent systems. The struggle between AI’s “smoke and mirrors” and “anti-smoke and mirrors” will be a long-term and important topic in future AI development.