Academic
  • Introduction
  • Artificial Intelligence
    • Introduction
    • AI Concepts, Terminology, and Application Areas
    • AI: Issues, Concerns and Ethical Considerations
  • Biology
    • Scientific Method
    • Chemistry of Life
    • Water, Acids, Bases
    • Properties of carbon
    • Macromolecules
    • Energy and Enzymes
    • Structure of a cell
    • Membranes and transport
    • Cellular respiration
    • Cell Signaling
    • Cell Division
    • Classical and molecular genetics
    • DNA as the genetic material
    • Central dogma
    • Gene regulation
  • Bioinformatics
    • Bioinformatics Overview
  • Deep Learning
    • Neural Networks and Deep Learning
      • Introduction
      • Logistic Regression as a Neural Network
      • Python and Vectorization
      • Shallow Neural Network
      • Deep Neural Network
    • Improving Deep Neural Networks
      • Setting up your Machine Learning Application
      • Regularizing your Neural Network
      • Setting up your Optimization Problem
      • Optimization algorithms
      • Hyperparameter, Batch Normalization, Softmax
    • Structuring Machine Learning Projects
    • Convolutional Neural Networks
      • Introduction
    • Sequence Models
      • Recurrent Neural Networks
      • Natural Language Processing & Word Embeddings
      • Sequence models & Attention mechanism
  • Linear Algebra
    • Vectors and Spaces
      • Vectors
      • Linear combinations and spans
      • Linear dependence and independence
      • Subspaces and the basis for a subspace
      • Vector dot and cross products
      • Matrices for solving systems by elimination
      • Null space and column space
    • Matrix transformations
      • Functions and linear transformations
      • Linear transformation examples
      • Transformations and matrix multiplication
      • Inverse functions and transformations
      • Finding inverses and determinants
      • More Determinant Depth
  • Machine Learning
    • Introduction
    • Linear Regression
      • Model and Cost Function
      • Parameter Learning
      • Multivariate Linear Regression
      • Computing Parameters Analytically
      • Octave
    • Logistic Regression
      • Classification and Representation
      • Logistic Regression Model
    • Regularization
      • Solving the Problem of Overfitting
    • Neural Networks
      • Introduction of Neural Networks
      • Neural Networks - Learning
    • Improve Learning Algorithm
      • Advice for Applying Machine Learning
      • Machine Learning System Design
    • Support Vector Machine
      • Large Margin Classification
      • Kernels
      • SVM in Practice
  • NCKU - Artificial Intelligence
    • Introduction
    • Intelligent Agents
    • Solving Problems by Searching
    • Beyond Classical Search
    • Learning from Examples
  • NCKU - Computer Architecture
    • First Week
  • NCKU - Data Mining
    • Introduction
    • Association Analysis
    • FP-growth
    • Other Association Rules
    • Sequence Pattern
    • Classification
    • Evaluation
    • Clustering
    • Link Analysis
  • NCKU - Machine Learning
    • Probability
    • Inference
    • Bayesian Inference
    • Introduction
  • NCKU - Robotic Navigation and Exploration
    • Kinetic Model & Vehicle Control
    • Motion Planning
    • SLAM Back-end (I)
    • SLAM Back-end (II)
    • Computer Vision / Multi-view Geometry
    • Lie group & Lie algebra
    • SLAM Front-end
  • Python
    • Numpy
    • Pandas
    • Scikit-learn
      • Introduction
      • Statistic Learning
  • Statstics
    • Quantitative Data
    • Modeling Data Distribution
    • Bivariate Numerical Data
    • Probability
    • Random Variables
    • Sampling Distribution
    • Confidence Intervals
    • Significance tests
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On this page
  • What is AI ?
  • Introducing to AI
  • Impact and Examples of AI
  • Chatbot powered by Natural Language Processing (NLP)
  • AI speech-to-text technology
  • Computer Vision
  • Application Domains for AI

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  1. Artificial Intelligence

Introduction

What is AI ?

  • AI 可以說是 Augmented Intelligence，依照專家的資訊及決定，來進行需要費時的工作。

  • AI 可以算是一種程序，幫助 machine 用 intelligent ways 來解決問題，並且不需要任何人力來 hard code。

AI is about augmenting human intelligence by providing information and evidence that subject matter experts need to make informed decisions. AI uses mathematical algorithms to examine examples and create machine learning models based on the inputs and desired outputs.

Introducing to AI

簡單來說，我們將大數據中的 unstructured data 提供給 machines，這些 data 通常為一些 inputs 和我們希望得到的 outputs ，並且利用一些方式讓 AI 學習辨識。

方式大致上可以分為 :

  • Supervised Learning

  • Unsupervised Learning

  • Reinforcement Learning

AI 又可以基於 strength, breadth, applications 分為 weak, strong, super AI :

  • Weak or Narrow AI

    • Perform specific tasks

    • Make decisions based on programmed algorithms and training data in a specific domain

    • e.g. translators, self-driving cars

  • Strong or Generalized AI

    • learn new tasks to solve problems

    • teaching itself new strategies

    • It's the combination of many AI strategies that learn from experience

    • It can perform at a human level of intelligence

    • strong AI doesn’t exist yet

  • Super or Conscious AI

    • AI with human-level consciousness.

    • self-aware

    • can't even define what consciousness is now

    • unlikely to be created in the near future

AI 甚至需要非常多學科的知識加入 :

  • Psychology

  • Linguistics

  • Philosophy

    • Provide guidance on ethical considerations

AI enables computers to understand certain kinds of data that they could not have understood before, such as natural language, auditory and visual data.

Impact and Examples of AI

AI 根據不同的工作領域，執行不一樣的事情 !

Chatbot powered by Natural Language Processing (NLP)

透過 AI algorithms 我們有辦法和電腦的語音助理對話，這類 Chatbot 用於 :

  • 在醫療上用於與病患的溝通、診斷

  • 在教育上用於與學生的交流、教學

  • 在 Customer service 節省了客服人員的程本

AI speech-to-text technology

AI 幫助我們能夠更清楚識別並轉化語音為文字，有許多東西受益 :

  • Real time transcription

  • Speech synthesis

  • Regain patient's real voice

Computer Vision

AI 能比人類更好的識別圖片 ! Computer vision 常常用於 :

  • Detecting and labeling objects

  • Self-driving cars

  • Facial recognition

  • Finding symptoms in X-Ray and MRI scans

Application Domains for AI

  1. Collaborative Robots helping humans lift heavy containers

  2. Classifying rock samples to identify best places to drill for oil

  3. Medical aspects

    • support doctors arrive at more accurate preliminary diagnoses

    • converting the stethoscope into a digital device, then using a machine learning model trained with previous diagnosis results to assist physicians with their current diagnosis

    • by creating massive data sets of information of how people move and how that corresponds to different areas of the brain, AI-powered robots are able to trigger specific movements in the human body which in turn create new neural pathways in the brain

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