MindMap Gallery AI artificial intelligence knowledge
AI artificial intelligence knowledge involves knowledge of computer science, mathematics, statistics, philosophy, psychology and other disciplines, and is generally classified under the computer discipline.
Edited at 2024-11-04 14:03:22이것은 곤충학에 대한 마인드 맵으로, 곤충의 생태와 형태, 생식 및 발달, 곤충과 인간의 관계를 연구하는 과학입니다. 그것의 연구 대상은 곤충으로, 가장 다양하고 가장 많은 수의 동물이며 생물학적 세계에서 가장 널리 분포되어 있습니다.
이것은 어린이의 내부 동기를 육성하는 방법에 대한 마인드 맵입니다. 기업가를위한 실용적인 가이드, 주요 내용 : 요약, 7. 정서적 연결에주의를 기울이고, 과도한 스트레스를 피하십시오.
이것은 자동화 프로젝트 관리 템플릿, 주요 내용에 대한 마인드 맵입니다. 메모, 시나리오 예제, 템플릿 사용 지침, 프로젝트 설정 검토 단계 (What-Why-How), 디자인 검토 단계 (What-Why-How), 수요 분석 단계 (What-Why-How)에 대한 마인드 맵입니다.
이것은 곤충학에 대한 마인드 맵으로, 곤충의 생태와 형태, 생식 및 발달, 곤충과 인간의 관계를 연구하는 과학입니다. 그것의 연구 대상은 곤충으로, 가장 다양하고 가장 많은 수의 동물이며 생물학적 세계에서 가장 널리 분포되어 있습니다.
이것은 어린이의 내부 동기를 육성하는 방법에 대한 마인드 맵입니다. 기업가를위한 실용적인 가이드, 주요 내용 : 요약, 7. 정서적 연결에주의를 기울이고, 과도한 스트레스를 피하십시오.
이것은 자동화 프로젝트 관리 템플릿, 주요 내용에 대한 마인드 맵입니다. 메모, 시나리오 예제, 템플릿 사용 지침, 프로젝트 설정 검토 단계 (What-Why-How), 디자인 검토 단계 (What-Why-How), 수요 분석 단계 (What-Why-How)에 대한 마인드 맵입니다.
AI artificial intelligence knowledge
definition
artificial intelligence (artificial, artificial intelligence), referred to as AI
A comprehensive science that studies and develops theories, methods, technologies and application systems for simulating, extending and expanding human intelligent behavior
Knowledge involving computer science, mathematics, statistics, philosophy, psychology and other disciplines is generally classified under the computer discipline
Dimensions of intelligence
Cognitive abilities: understanding, learning, reasoning, memory, etc.
Adaptability: solving problems, coping with environmental changes, etc.
Autonomy: Complete tasks independently, make decisions independently, etc.
core elements
Computing power
GPU, ASIC (TPU, NPU), FPGA, etc.
algorithm
Machine learning, deep learning, reinforcement learning, transfer learning, etc.
data
Structured data, unstructured data, etc.
Data collection, data cleaning, data standards, data storage, etc.
school
Three major schools of thought
symbolism school
connectionist school
behaviorism school
Other schools of thought
Evolutionary school
Bayesianism
school of analogy
Main research methods
knowledge based approach
Expert system, knowledge map
learning based approach
Machine learning, deep learning
bionic-based approach
Behaviorism, Evolutionary Computation
Classified by intelligence level
Weak AI
Only specializes in a single task or a group of related tasks and does not have general intelligence capabilities
Strong AI
Have certain general intelligence capabilities and be able to understand, learn and apply them to a variety of tasks
Super artificial intelligence (Super AI)
Exceeds human intelligence in almost every aspect, including creativity, social skills, etc.
Development stage
budding stage
1940s-1956 Turing Test
birth period
Dartmouth Conference 1956
first wave
Symbolism 1956-1973
Second wave
Symbolism (expert systems) 1980-1990
third wave
1994-present machine learning, deep learning
machine learning
Supervised Learning
Algorithms learn from labeled data sets, i.e. each training sample has a known outcome
Unsupervised Learning
Algorithms learn from unlabeled data sets
Semi-supervised Learning
Combines a small amount of labeled data and a large amount of unlabeled data for training
Reinforcement Learning
Learn through trial and error which behaviors are rewarded and which behaviors result in punishment
neural network
Convolutional Neural Network (CNN)
Recurrent Neural Network (RNN)
Generative Adversarial Network (GAN)
Long Short-Term Memory (LSTM)
Transformer
deep learning
definition
Deep learning, specifically, deep neural network learning
It is an important branch of machine learning
Deep learning algorithms use more "hidden layers" (hundreds), making them more powerful and allowing neural networks to do more difficult jobs
frame
TensorFlow (Google)
Caffe(BVLC)
Keras(fchollet)
CNTK (Microsoft)
Torch7 (Meta)
PaddlePaddle (Baidu)
MindSpore (Huawei)
large model
definition
Machine learning models with large parameter scales and complex computing structures
The basic core structure of most large models is Transformer and its variants.
The large model that is often mentioned at present is mainly the large language model (Large Language Model).
process
pre-training
The process of training language models using large amounts of unlabeled data
It gives the model a certain degree of versatility and the ability to adapt to a variety of different downstream tasks.
fine-tuning
On the basis of pre-training, use annotated data (that is, data for specific tasks) to further train the model to adapt it to specific applications or tasks.
Classification
By use
General large model
Industry model
According to characteristics
large language model
Train with text data
visual model
Train with image data
Multimodal large model
Both text and images
by function
Analytical (decision-making)
Generative
Press to switch source
Open source large model
Closed source large model
business model
Subscription model
API service model
Platform service model
Customized service model
Advertising and promotion models
Data authorization model
AIGC (Artificial Intelligence Generated Content)
definition
Use artificial intelligence technology to automatically create or generate content
Generated content can include text, code, images, music, videos, and more.
category
generate text
GPT series, Wen Xin Yi Yan, Tong Yi Qian Wen, Pangu, Claude 3, Diffusion-LM, Chinchilla, etc.
Vincentian picture
DALL·E 2, Stable Diffusion, Midjourney, Pixeling Qianxiang, DreamGaussian, Baidu AI painting, Tongyi Wanxiang, etc.
Vincent Audio
MusicLM, ElevenLabs, Wondershare Filmora, Reecho, SkyMusic, Qinle Model, FunAudioLLM, MusicGen, etc.
Vincent Video
Sora, Stable Video Diffusion, Vidu, etc.
Main abilities
Computer Vision (CV)
Image recognition, visual recognition, face recognition, video recognition, text recognition, gait recognition...
speech recognition
Voice recognition, voiceprint recognition, speech synthesis, voice interaction...
natural language processing
Information understanding, text proofreading, machine translation, natural language generation...
embodied intelligence
Home service robots, medical care robots, hotel service robots, industrial robots...
Application areas
Industrial manufacturing
Automated production, intelligent quality inspection, equipment operation and maintenance, supply chain management...
medical health
Medical image analysis, gene sequencing, disease prediction, drug research and development, personalized treatment...
financial securities
Risk management, credit assessment, fraud monitoring, quantitative trading, market forecast...
Education and training
Personalized learning paths, intelligent tutoring, course recommendations…
Transportation and Logistics
Autonomous driving, route optimization, traffic analysis, emergency plans…
news media
Manuscript collection and writing, material creation, text polishing...
Games and entertainment
Character design, element generation, plot design, special effects production...
role and value
From a business perspective
AI can automate repetitive and tedious tasks, improve production efficiency and quality, and reduce labor costs.
AI can not only improve governance efficiency, but also bring new business models, products and services, stimulating the economy
From the government's perspective
AI can not only improve governance efficiency, but also bring new business models, products and services, stimulating the economy
From a personal perspective
AI can help us complete some tasks and improve our quality of life
From the perspective of all mankind
AI can also play an important role in disease treatment, disaster prediction, climate prediction, and poverty eradication.
difficulties and challenges
employment
May threaten a large number of human jobs and lead to massive unemployment
crime
AI is used to wage war and deceive (imitate voices or change faces to commit fraud)
privacy
Infringement of citizens’ rights (excessive collection of information, invasion of privacy)
fair
If only a few companies have advanced AI technology, it could exacerbate social inequities
AI algorithm bias may also lead to unfairness
rely
As AI becomes more and more powerful, it will also make people dependent on AI and lose their ability to think independently and solve problems.
confidence
The powerful creativity of AI may cause humans to lose the motivation and confidence to create.
Safety
Surrounding the development of AI, there are also a series of issues such as security (data leakage, system crash), moral ethics, etc.