50 AI Basics Q&A (Read to understand the entire AI industry)

1.  AI 

Artificial intelligence ( AI ) refers to the ability of computer systems to simulate human intelligence, including functions such as learning, reasoning, and decision-making. Typical applications include speech recognition and image processing.

AI is like giving a computer a "brain" that enables it to learn and solve problems like humans. For example, the voice assistant in your phone (such as Apple's Siri and Xiaomi's Xiao Ai) can not only understand "open WeChat", but also recommend a wake-up time based on your schedule. DeepSeek and other large models can help you write a novel outline by analyzing massive amounts of book data and imitating human writing patterns.

The core of AI is algorithm (mathematical rules) + data (learning materials) + computing power (computing speed), just like students need textbooks, teachers, and time to get high scores. (Analogy: AI = a system for cultivating top students)

2.   HPC

High performance computing ( HPC ) refers to the technology of using supercomputers or computing clusters to process complex computing tasks, and is often used in scientific fields such as climate simulation and genetic analysis.

HPC is like a "supercar" for scientific computing. A task that takes a day on a regular computer can be completed in a few seconds on an HPC. For example, the visual effects of the planetary engine in the movie "The Wandering Earth" took 10 years to render on a regular computer, but only one month on an HPC cluster.

The most powerful El Capitan supercomputer in 2024 will have a floating point computing speed of 1.742 exaflops (i.e. 1.742×10¹⁸ operations per second). If a single computer were to complete it at a speed of 1 operation per second, it would take about 54 billion years! It is mainly used for nuclear weapons research, energy security, climate change, grid modernization and drug discovery. (Metaphor: HPC = Scientist's time accelerator)

3. Differences between AI and HPC

AI focuses on simulating intelligent behaviors (such as learning prediction), while HPC focuses on high-speed numerical computing. AI commonly uses GPU (Graphics Processing Unit)/TPU (Tensor Processing Unit), while HPC mostly uses CPU (Central Processing Unit) clusters.

AI is like a painter who can create, and HPC is like an accurate calculator. AI uses GPU to draw a starry sky in the style of Van Gogh (such as Midjourney), while HPC uses CPU to accurately calculate the trajectory of the rocket. For example, weather forecast: AI uses historical data to guess whether it will rain tomorrow (probability), and HPC uses physical formulas to simulate the movement of clouds (precise values). The two are combined - NVIDIA uses AI to accelerate chip design, shortening the R&D cycle from 6 years to 6 months! (Life example: AI = art student, HPC = science student)

4. Three Elements of AI

Data (training materials), algorithms (calculation rules), and computing power (hardware support) are all indispensable.

●  Data : Like the ingredients in a recipe, Douyin recommends videos by analyzing the 100,000 records of your likes;

●  Algorithm : Equivalent to cooking steps, Tesla's autonomous driving uses "convolutional neural network (CNN)" to recognize traffic lights;

●  Computing power : Like a raging stove, Google used TPU chips to train GPT-4 in 3 days (a home computer would take 300 years).

All of them are indispensable: no data = a good cook cannot cook without rice; poor algorithm = burnt food; low computing power = stewing for three days. (Analogy: the three elements of cooking)

5. Principles of Generative AI Algorithms

An algorithm is a collection of steps to solve a problem, such as CNN (Convolutional Neural Network) for image recognition and RNN (Recurrent Neural Network) for processing sequence data.

Generative AI is like an "automatic story creation machine". For example, if you input "write a detective story", it will first conceive the characters (establish semantic relationships) like a writer, and then fill in the details (generate text). There are two core technologies:

●  Diffusion model : Like a painter gradually refining a fuzzy sketch, Stable Diffusion 3 produces a high-definition image through 50 steps of denoising;

●  Autoregressive model : Like a solitaire game, GPT-4 predicts the next most likely word each time (for example, “cat” followed by “catch mouse”).

The new breakthrough in 2024, the Consistency model, can compress 50 steps of generation into 1 step, just like a magician conjuring up a complete painting in an instant! (Example: Midjourney V6 generates an e-commerce poster in just 2 seconds)

6. Model Definition

A model is a parameterized system formed after an algorithm is trained with data. For example, GPT-4 is a neural network model for processing text.

The model can be understood as an AI skill package. For example, behind Photoshop's "one-click photo editing" function is a complex code, and the AI ​​model packages this capability into a tool that ordinary people can use:

●  Parameters : Like the proportions of spices in a recipe, GPT-4’s 1.8 trillion parameters determine the style of the generated text;

●  Structure : Like a factory assembly line, the Transformer model first segments words and then calculates the relationship between words;

●  Application : The Stable Diffusion model inputs “whales under the starry sky” and outputs the corresponding picture, just like a magic black box.

In 2024, the MoE model (hybrid expert system) will package different skills: activate "logic experts" when solving math problems, call "literary experts" when writing poetry, and increase efficiency by 5 times! (Analogy: Swiss Army Knife-style multifunctional model)

7. Framework Role

The framework is a toolbox for developing AI models (such as TensorFlow/PyTorch), providing preset functions and computational graph management.

AI frameworks are like Lego toolboxes. PyTorch provides a variety of pre-made modules (such as convolution blocks and attention blocks), and developers can build models like building blocks. For example, Tesla uses PyTorch to assemble an autonomous driving vision system:

1. Select the camera data processing module;

2. Splicing object detection network;

3. Use the automatic differentiation function to adjust the parameters.

Comparison with traditional programming: hand-made cars vs. assembling sports cars with Lego. In 2024, the new framework JAX supports "just-in-time compilation", which is like adding motors to building blocks, and the training speed is increased by 3 times! (Analogy: IKEA furniture in the programming world)

8. Supervised Learning

Use labeled data to train models, such as training classifiers with labeled images to predict the categories of new images.

This is like a teacher teaching students with answers. Give AI a large amount of "question + standard answer" paired data:

● Image classification: 100,000 “cat/dog pictures + labels” for AI to learn to recognize;

● Voice recognition: Millions of “voice + text” data are used to train Siri to understand commands.

In 2024, Tesla used supervised learning to train FSD (Full Self-Driving) V12: Every turn at an intersection has a human driving record as a reference answer. The disadvantage is that it relies on labeled data - it takes a team of 20 people a week to label 100 hours of speech! (Case: Medical AI diagnoses pneumonia by labeling X-rays)

9. Unsupervised Learning

Leverage unlabeled data to discover patterns, such as clustering algorithms to group similar users.

This is equivalent to letting AI discover patterns on its own. For example, if you are given 1,000 unclassified news articles, AI will automatically classify them into "sports/finance/entertainment" sections. The principle is to calculate the term frequency (TF) similarity (for example, articles containing "goals" and "scores" are grouped into one category).

In 2024, Google used unsupervised learning to analyze user search records and automatically identified 30 consumer preference groups. The advantage is that no manual labeling is required, but the disadvantage is that the classification is sometimes confusing - it may classify "football" and "war news" as "high-passion content." (Case: TikTok's early recommendation algorithm relied on unsupervised clustering)

10. Semi-supervised learning

Combine a small amount of labeled data with a large amount of unlabeled data for training to reduce labeling costs.

For example, a student uses 1 exercise book + 100 reference books to study on his own. A small amount of labeled data (10,000 labeled medical images) plus a large amount of unlabeled data (100,000 unlabeled images) is used to train the model. In 2024, MIT (Massachusetts Institute of Technology) used this method to develop a pathology diagnosis system:

1. Doctors annotated 100 cancer slides;

2. The model finds similar patterns from 100,000 unlabeled data;

3. The diagnostic accuracy is 15% higher than that of pure supervised learning.

This is equivalent to learning the basics from a teacher first, and then expanding the boundaries of knowledge on your own! (Analogy: a combination of cram school + self-study)

11. Reinforcement Learning

Learn through trial and error and interaction with the environment, such as AlphaGo optimizes its chess strategy through feedback from wins and losses.

Like training pets to complete difficult actions:

●  Reward mechanism : a dog is given snacks for successfully jumping through a circle (positive feedback), and the AI ​​scores points by pushing a tower in DOTA 2;

●  Trial and error learning : The pet avoids obstacles after hitting them (negative feedback), and the AI ​​autonomous driving simulates collisions tens of thousands of times to optimize the path.

In 2024, DeepMind's AlphaDev uses reinforcement learning to optimize sorting algorithms, increasing the speed of C++ library functions by 70%! (Case: Faster than code written by human programmers)

12. Common Model Types

CNN (image), Transformer (text), GNN (graph data), diffusion model (generation).

●  Language model : such as GPT-4o, which can write emails/debug codes, like an all-round secretary;

●  Image model : For example, Midjourney V6, input "cyberpunk cat" to generate a poster, comparable to a designer;

●  Scientific model : AlphaFold3 predicts protein 3D structure and accelerates new drug development;

●  Embodied model : Boston Dynamics Atlas robot model, which can perform backflips and autonomous cargo handling.

Trends in 2024: Model miniaturization (Llama3-8B can run on mobile phones) +  multimodality (GPT-4o supports real-time drawing of voice conversations).

13. Mainstream training framework

PyTorch (dynamic graph), TensorFlow (static graph), JAX (high performance computing).

●  PyTorch : Like Lego blocks, flexible and easy to assemble (Tesla FSD uses it to build a visual network);

●  TensorFlow : such as standardized pipelines, suitable for large-scale deployments (Google search ranking model);

●  JAX : Speed-enhanced version that supports automatic parallel computing (DeepMind training AlphaFold3).

In 2024, PyTorch 2.3 will support a mixed mode of dynamic graphs and static graphs, increasing training speed by 40%. (Analogy: combining manual and automatic transmission cars)

14. Model training process

Data preparation → model design → training (forward calculation + back propagation) → verification → deployment.

Analogy to a chef cooking:

1. Food preparation: cleaning the labeled data (e.g. removing blurry images);

2. Recipe: Designing neural network structures (ResNet/Transformer);

3. Cooking: GPU "high-fire" training (adjusting parameters to minimize the loss function);

4. Tasting: test accuracy of validation set;

5. Open a store: Deploy as API or APP function.

In 2024, AutoML (Automated Machine Learning) tools (such as Google Vertex AI) will achieve automated training - input data, 5 steps will become 1 step!

15. Fine-tuning

Based on the pre-trained model, use specific domain data for secondary training, such as using medical text to optimize the general language model.

Like custom-fitting a generic suit:

1. Base model: DeepSeek R1 (standard suit);

2. Domain data: inject legal provisions/case data (tailored to the needs);

3. After fine-tuning: Compliance contracts or diagnostic recommendations (tailor-made suits) can be generated.

In 2024, LoRA fine-tuning technology will only train 0.1% of parameters, reducing the time from 10 days to 3 hours! (Case: Doctors use ChatGPT to fine-tune medical assistants)

16. RAG Technology

Retrieval-Augmented Generation (RAG): Combines retrieval of external knowledge bases with generation models to improve answer accuracy.

It is equivalent to giving AI an external mobile hard drive:

●  Search : Search for the latest information (such as company financial reports/medical papers) in real time when asking questions;

●  Enhanced generation : Generate answers based on search results to avoid “making up stories”.

In 2024, Perplexity AI will use RAG to achieve real-time online question answering, with an accuracy rate 35% higher than pure GPT-4. (Analogy: being allowed to answer questions by flipping through a book during an exam)

17. Model Compression Technology

Methods to reduce model size include pruning (removing redundant parameters) and quantization (reducing numerical precision).

Let the large model "slim down" into the mobile phone:

●  Pruning : remove redundant neurons (such as removing uncommon classical Chinese parameters in GPT-4);

●  Quantization : 32-bit floating point number to 4-bit integer (HD image to emoticon package, the volume is reduced by 8 times);

●  Distillation : The small model imitates the output of the large model (students copying the notes of the top student).

Apple's A18 chip runs 4-bit quantized Llama3, and the iPhone can process document summaries offline. (Example: Generate PPT outline on mobile phone)

18. Model Quantization Principle

Convert 32-bit floating-point parameters to 8-bit integers to reduce memory usage and computing overhead, and increase inference speed by 2-4 times.

To switch the parameters from precise mode to flow saving mode:

●  FP32→INT8 : 32-bit decimals (0.12345678) are converted to 8-bit integers (12), reducing memory usage by 75%;

●  Dynamic quantization : retain high accuracy for key layers (such as attention mechanism) and significantly compress secondary layers.

In 2024, NVIDIA's TensorRT-LLM supports mixed precision quantization, and the inference speed of 70B models is increased by 3 times! (Analogy: adaptive image quality of video websites)

19. MoE Architecture

Hybrid expert system: Divide the network into multiple expert subnetworks, and each input only activates some experts to improve computing efficiency.

Let the model become an expert committee:

●  Task distribution : When inputting "Solve differential equations", only the mathematics expert module is activated;

●  Dynamic routing : Allocate computing resources based on the problem type, saving 60% energy compared to full computing.

In 2024, the Mixtral 8x22B model used MoE to achieve translation in 46 languages, surpassing GPT-4 in performance. (Case study: AI version of “specialization”)

20. Model Distillation

Let the small model imitate the behavior of the large model, such as training a smaller student model with the output of GPT-4.

Knowledge inheritance: Let the small model inherit the "internal skills" of the big model:

1. Teacher model: GPT-4 generates 10,000 question-answer pairs;

2. Student model: Alpaca 7B learns this data;

3. Effect: The small model reaches 70% of the teacher's capabilities and is 20 times smaller.

In 2024, the course distillation technology will be taught in stages (first learn the basics and then learn the advanced ones), and the performance of students' models will be improved by 15%. (Analogy: a crash course for famous teachers and apprentices)

21. DeepSeek core technology

The MoE architecture is used to implement a trillion-parameter model, combining reinforcement learning optimization and dynamic quantization technology.

DeepSeek is like the "Swiss Army Knife" of AI. Its core technologies include:

●  MoE architecture : split the model into multiple "experts" (such as mathematics/programming experts), and only activate relevant parts when processing tasks, saving 70% of computing power;

●  Dynamic quantization : Automatically switch precision during inference (FP16 for critical parts and INT4 for secondary parts), reducing memory usage by 60%;

●  Reinforcement learning optimization : The dialogue strategy is adjusted through user feedback, and the fluency is improved by 40% compared to GPT-3.5.

The DeepSeek-v3 model uses 671B parameters (37B activations) and outperforms all open source models.

22. AI Data Types

Structured data (tables), unstructured data (text/images), time series data (sensor streams).

●  Structured data : like Excel tables (patient age/blood pressure values), used to predict disease risk;

●  Unstructured data : such as CT scan images (pixel matrix), training tumor recognition models;

●  Time series data : Similar to the continuous waveform of an electrocardiogram, it can predict heart attacks.

In 2024, Meta used multimodal data fusion to combine voice recordings (unstructured) + heart rate (time series data) to diagnose depression with an accuracy rate of 89%. (Analogy: jigsaw puzzle)

23. Token definition

The basic unit of text processing. Chinese is often based on words/characters, while English is often split into subwords (such as "un+able").

Token is the “building block” of AI text processing:

●  English : “ChatGPT” is split into “Chat” + “G” + “PT” (subword encoding);

●  Chinese : “Artificial intelligence” can be split into “人工” + “智能” (by word) or single characters.

In 2024, Llama3's vocabulary will be expanded to 128K tokens, and the compression rate of Chinese will be increased by 40%. It only takes 6 tokens to input "I want to eat snail rice noodle"! (Example: 1 token ≈ 1 common English word)

24. Transformer Principle

Based on the self-attention mechanism, sequence data can be processed in parallel, breaking through the long-range dependency limitations of RNN.

Transformer is like an “efficient reader”:

1.   Word segmentation : split the sentence into tokens;

2.   Self-attention : calculate the relationship between words (e.g., "cat" and "catch mouse" are highly correlated);

3.   Parallel processing : Analyze all words at the same time (10 times faster than RNN word-by-word analysis).

In 2024, GPT-4o uses sparse attention to process 100,000 token long text in just 1 second! (Analogy: Speed ​​Reading Master)

25. Parallel Training Methods

Data parallelism (splitting data to multiple cards), model parallelism (splitting network layers), and pipeline parallelism (staged calculations).

●  Data parallelism : 10 machines learn different chapters at the same time and summarize them at the end (such as using 100 GPUs to train DeepSeek);

●  Model parallelism : disassemble the neural network (layer A on GPU1, layer B on GPU2) to train a trillion-parameter model;

●  Pipeline parallelism : Like a factory assembly line, when GPU1 is processing the first batch of data, GPU2 has already started the second batch.

In 2024, NVIDIA DGX H100 clusters used hybrid parallelism to train GPT-4 level models in 7 days. (Case study: Ant Moving Collaboration)

26. Mainstream application scenarios of AI

Intelligent customer service, autonomous driving, medical image analysis, recommendation systems, and industrial quality inspection.

●  Intelligent customer service : Taobao’s “Xiaomi” uses NLP (Natural Language Processing, NLP) to understand the “return process”, with a resolution rate of 90%;

●  Autonomous driving : Tesla FSD V12 uses visual models to identify lane lines in heavy rain;

●  Medical imaging : United Imaging AI system locates lung nodules in CT scans within 3 seconds with an error of <0.1mm;

●  Industrial quality inspection : CATL uses AI to detect battery defects, with the missed detection rate reduced to 0.01%.

Trends in 2024: AI lawyers (contract review), AI screenwriters (web drama script generation).

27. Heterogeneous computing

Integrate processors of different architectures (such as CPU+GPU+ASIC) for collaborative computing to improve energy efficiency.

Like the division of labor in a restaurant kitchen:

●  CPU : Chef (complex decisions, such as scheduling tasks);

●  GPU : Vegetable cutter (parallel processing of images/matrix operations);

●  ASIC : Oven (specialized tasks such as TPU accelerated AI inference).

In 2024, AMD MI300X will realize CPU+GPU unified memory, reducing data transfer time by 80%! (Analogy: kitchen flow optimization)

28. Mainstream AI chips

GPU (NVIDIA H200), TPU (Google's dedicated tensor processing unit), Huawei Ascend 910B.

●  NVIDIA H200 : 4.8 PetaFLOPS computing power, the core engine for training GPT-5;

●  Google TPU v5 : Optimized for Transformer, with inference speed 3 times faster than GPU;

●  Huawei Ascend 910B : supports domestic substitution, and Llama3 training efficiency is increased by 50%.

In 2024, Intel will launch Falcon Shores, a CPU+GPU fusion chip with an energy efficiency ratio of 50 TFLOPS/W. (Analogy: F1 racing engine)

29. Overfitting

The model over-memorizes the details of the training data, resulting in poor performance on new data, which can be alleviated by regularization or adding more data.

Overfitting is like a student memorizing test questions by rote and then getting confused when encountering new questions. Solution:

●  Data enhancement : add noise/rotate the image (a variant of the mock exam question);

●  Dropout : Randomly block neurons (force multi-angle thinking);

●  Early stopping method : stop training when performance no longer improves (to avoid getting stuck in a dead end).

In 2024, Google used Diffusion to enhance the generation of realistic synthetic data, reducing the overfitting rate by 60%. (Case study: AI version of the “sea of ​​questions” strategy)

30. The role of loss function

Quantify the gap between the predicted value and the true value to guide the direction of parameter adjustment, such as cross entropy for classification tasks.

The loss function is the “report card” of AI:

●  Classification task : cross entropy loss (determine whether the answer is right or wrong);

●  Regression task : mean square error (predicted house price error value);

●  Reinforcement learning : cumulative rewards (maximizing game scores).

In 2024, Meta proposed dynamic weighted loss to automatically adjust the weights of multiple tasks (such as optimizing translation accuracy and fluency at the same time). (Analogy: calculating the total score of multiple subjects in an exam)

31. Activation function

To introduce nonlinearity into neural networks, ReLU (max(0,x)) is often used to avoid gradient disappearance.

The activation function is like a "smart switch" that determines whether a neuron transmits a signal:

● ReLU: turns off when the input is negative (e.g. filtering dark areas in an image), outputs it as is when the input is positive;

● Sigmoid: compresses the value to 0-1 (similar to a scoring system) and is used to judge "yes/no" (such as spam classification).

In 2024, the Swish-GLA activation function was applied in Google Gemini, and the accuracy of processing long texts was improved by 12%! (Case: Let AI more accurately identify key paragraphs in medical reports)

32. Embedding

Mapping discrete data (such as words) into continuous vectors that capture semantic relationships, such as "king"-"queen" ≈ "male"-"female".

Embedding is to give words a "digital ID card":

●  Semantic encoding : The vector corresponding to “cat” is [0.2, -0.5, 0.7], which is close to the vector of “dog”;

●  Relationship mapping : “Beijing-China ≈ Paris-France” (vector subtraction reflects the capital relationship).

In 2024, OpenAI's text-embedding-3-large supports 8192-dimensional vectors, and the search accuracy is improved by 35%. (Analogy: GPS coordinates of words)

33. Why GPU is suitable for AI

It has thousands of computing cores and is good at parallel processing of matrix operations (neural network core computing mode).

GPU is like a "factory of 10,000 people", designed for parallel computing:

●  Core count : NVIDIA H200 has 18,432 CUDA cores, thousands of times more than a CPU;

●  Matrix acceleration : a single card can complete a million-level matrix multiplication in 1 second (CPU takes 10 minutes);

●  Memory bandwidth : HBM3 technology reaches 4TB/s, quickly feeding data to the computing unit.

In 2024, AMD MI350X graphics card will increase the speed of Stable Diffusion training by 3 times! (Case: AI drawing "turbocharged engine")

34. Transfer Learning

Leverage the underlying features of trained models to quickly adapt to new tasks, reducing data requirements and training time.

Transfer learning is like "knowledge reuse":

●  Basic skills : ImageNet pre-trained model learns to recognize edges/textures;

●  Rapid adaptation : Pneumonia can be diagnosed with a small amount of X-ray film and fine-tuning (training time is reduced from 1 month to 1 day).

In 2024, Microsoft's Phi-3 model will reach the level of GPT-3.5 through transfer learning using only 1% of the data! (Analogy: a crash course for generalists to experts)

35. Principle of Attention Mechanism

Dynamically assign weights to different parts of the input, such as focusing on relevant source language words when translating.

The attention mechanism is like a "smart spotlight":

●  Weight distribution : When translating “I love AI”, “I” focuses on “me” and “love” is associated with “love”;

●  Multi-head attention : Analyze from multiple angles of grammar, semantics, and sentiment at the same time (like 8 “lighting technicians” working together).

In 2024, GPT-4o uses sparse attention to process 100,000 token texts 50% faster! (Case: AI version of "reading ten lines at a glance")

36. Batch Normalization

Standardize the input of each layer to accelerate training convergence and reduce sensitivity to parameter initialization.

Batch Normalization is a “data stabilizer”:

●  Standardization : adjust the input of each layer to a mean of 0 and a variance of 1 (similar to the difficulty of a unified exam);

●  Accelerate training : reduce gradient explosion/vanishing and increase convergence speed by 2 times.

In 2024, DeepMind's BatchNorm++ supports dynamic adjustment, and the stability of training trillion-parameter models is improved by 40%. (Analogy: fitness coaches standardize movements)

37. Dropout

Randomly block some neurons to prevent over-reliance on specific features and improve the generalization ability of the model.

Prevent AI from "memorizing by rote". For example, during class review, the teacher randomly asks some students to close their eyes (blocking neurons) and forces others to fill in the gaps, so that the whole class can understand the knowledge points in the end. The upgraded version of Dynamic Dropout in 2024 will be smarter: for math questions, students with poor calculation skills will be blocked, and for Chinese questions, students will be replaced, so that the model can truly learn to draw inferences from one example. (Metaphor: random questioning method in class)

Dropout is like a “random pop quiz”:

●  During training : randomly block 20% of neurons to force the network to learn in multiple paths;

●  During inference : all members are activated, but the output is scaled.

In 2024, DropCluster was applied to graph neural networks, randomly deleting subgraph structures, and improving the accuracy of social network analysis by 18%! (Case: Anti-cheating learning method)

38. Importance of Learning Rate

Control the parameter update step size. If it is too large, the training will be slow. If it is too small, the training will be slow. Adaptive algorithms (such as Adam) are commonly used.

The learning rate is the “pace regulator”:

●  Too big : skip the optimal solution (such as running too far);

●  Too small : slow convergence (like a snail crawling);

●  Adaptive : Adam optimizer dynamically adjusts (take small steps uphill, take big steps on flat roads).

In 2024, Lion optimizer will reduce the number of iterations by 30% in Stable Diffusion training! (Case: AI version of "smart speed running shoes")

39. Data Augmentation Methods

Improve the robustness of the model by expanding the dataset by rotating/cropping/noise injection, etc. (Computer software is robust if it does not crash or freeze when errors, failures or attacks occur).

Data augmentation is a “virtual expansion of the army”:

●  Images : rotate/crop/noise (turn 1 cat picture into 100 variations);

●  Text : synonym replacement/sentence rewriting ("Hello" → "Hello");

●  Audio : change speed/add background sound.

In 2024, Diffusion will enhance the generation of realistic synthetic data, and the small sample training effect will be improved by 50%! (Case: AI gives itself questions)

40. Ethical issues in AI

Including data privacy (abuse of facial recognition), algorithmic bias (gender discrimination in recruitment systems), and attribution of responsibility (autonomous driving accidents).

AI ethics is a "technical brake pad":

●  Privacy leakage : facial data is maliciously used for deep fake videos (such as forging celebrity speeches);

●  Algorithmic bias : Recruitment AI prefers male resumes (caused by historical data bias);

●  Responsibility : Who is responsible for an autonomous driving accident: the car owner/manufacturer/code author?

The 2024 EU AI Act strictly prohibits real-time facial recognition, and companies that violate the law will be fined up to 7% of their global revenue! (Case: "Traffic rules" in the AI ​​industry)

41. Principles of Federated Learning

Multiple devices collaborate to train the model, the data is retained locally, and only parameter updates are exchanged to protect privacy.

Federated learning is like a "secret joint meeting": multiple hospitals use their own patient data to train AI models, but the data never leaves the local area. For example, training a cancer prediction model:

1. Hospital A uses local data to calculate model updates;

2. Encrypted and uploaded to the central server;

3. Integrate all updates to generate a global model.

In 2024, Apple will use federated learning to upgrade Siri. The user's voice data will be retained on the phone, but the model iteration efficiency will be increased by 60%. (Example: Data version of "Only exchange experience, not disclose privacy")

42. Generative Adversarial Networks (GANs)

The generator and the discriminator are trained adversarially to generate realistic data, such as Deepfake video synthesis.

GAN is like a "forgery vs. treasure appraiser showdown":

●  Generator : Learn to draw a realistic Mona Lisa (forger);

●  Discriminator : Identify authentic and fake paintings (treasure appraiser).

The two will compete with each other until the fake paintings are indistinguishable from the real ones. In 2024, ConsistencyGAN will be able to generate 4K images in a single step, 100 times faster than traditional GAN! (Case study: AI generates virtual anchors to sell goods live)

43. Knowledge Graph Application

Structured storage of entity relationships to support intelligent search (such as Google Knowledge Cards) and medical diagnosis assistance.

The knowledge graph is the “relational database” of AI:

●  Medical : connecting “symptoms → disease → medicine” (such as Tencent Miying assisted diagnosis);

●  E-commerce : building a “user → purchase → product” network (Taobao recommends related products);

●  Finance : Identify the “company→shareholder→risk” link (Ant risk control system).

In 2024, Google's knowledge graph will cover 5 billion entities, and the accuracy of search answers will increase by 40%. (Analogy: AI version of the "Six Degrees of Separation Theory")

44. AI chip storage and computing integration

Computation is completed within the storage unit, reducing energy consumption in data transfer and improving energy efficiency by more than 10 times.

Storage and computing integration is like "processing goods directly in the warehouse":

●  Traditional computing : data is moved between memory and processor (time-consuming and energy-consuming);

●  Storage and computing in one : Multiplication and addition operations are completed within the storage unit (energy efficiency is improved by 10 times).

In 2024, Samsung will release the HBM4-PIM chip with an inference speed of 500 TOPS, which is optimized for Llama3. (Case: AI chip "kitchen and restaurant merger")

45. AI compiler function

Optimize model code into hardware instructions (such as TVM) to improve operating efficiency on different chips.

AI compiler is like a "universal translator":

●  Hardware adaptation : convert PyTorch code to CUDA/ROCM instructions;

●  Performance optimization : Automatically select the best calculation path (such as splitting matrix multiplication into parallel subtasks).

In 2024, Intel's OpenVINO 2024 will support cluster compilation, increasing training speed by 70%. (Analogy: "translating" C++ code into machine language)

46. ​​Multimodal Models

It can process multiple types of data such as text/image/voice at the same time. For example, GPT-4V can analyze and describe the content of pictures.

Multimodal AI is an “all-round artist”:

●  Input : Can receive text ("design LOGO") + picture (reference sketch) at the same time;

●  Output : Generate vector diagram + style description document.

In 2024, GPT-4o will support real-time voice dialogue drawing. Just say "draw a flying panda" and a 3D model will be generated immediately. (Case: Cross-modal creation platform Runway upgrade)

47. AI Security Threats

Adversarial sample attacks (slight perturbations that mislead classification) and model stealing (copying API functions).

●  Adversarial attack : Put a specific sticker on the stop sign, causing the autonomous driving to misjudge it as a "speed limit sign";

●  Data poisoning : maliciously contaminating training data (such as injecting incorrect medical knowledge into ChatGPT);

●  Model stealing : Repeatedly query the API to copy models with the same functions.

In 2024, OpenAI will launch the Shield protection system, blocking 99.7% of adversarial sample attacks! (Case: "anti-virus software" in the AI ​​world)

48. AI-assisted drug development

Predict molecular properties (AlphaFold2 predicts protein structure) and shorten the new drug development cycle.

AI is a “molecular designer”:

1.   Target discovery : AlphaFold3 predicts protein structure;

2.   Virtual screening : using a library of 1 billion molecules to match target proteins;

3.   Toxicity prediction : Eliminate harmful drug candidates.

In 2024, Insilico Medicine used AI to design ISM1011 (anti-fibrosis drug), shortening the R&D cycle from 5 years to 18 months. (Analogy: a "time machine" for new drug development)

49. Embodied AI

AI agents physically interact with the real environment, such as robots learning grasping techniques through touch.

Embodied intelligence is "AI + physical body":

●  Perception : Boston Dynamics’ Spot robot dog uses lidar to avoid obstacles;

●  Decision-making : Choose a walking/jumping strategy based on the terrain;

●  Execution : The robotic arm accurately grasps objects of different shapes.

In 2024, Nvidia Project GR00T will enable humanoid robots to learn to fold clothes in 5 minutes! (Example: Home Robot "Transformers")

50. Future Trends of AI

Exploration of general artificial intelligence (AGI), integration of neural symbolic systems, and development of green and low-carbon training technologies.

●  More general : GPT-5 will integrate text/code/3D modeling to become a "digital Swiss Army knife";

●  More inclusive : The mobile phone runs a 70B parameter model (MediaTek Dimensity 9400 supports Llama3 full-speed reasoning);

●  More controllable : The EU forces AI-generated content to add invisible watermarks (such as photo EXIF ​​information).

Quantum AI breakthrough in 2024: IBM uses quantum computers to optimize logistics routes, with a computing speed 1,000 times faster than classical algorithms! (Analogy: AI enters the "quantum leap" era)