Artificial Intelligence And Machine Learning In Big Data And IoT Market 2018-2023

1 Introduction

• 1.1 Executive Summary
• 1.2 Research Objectives
• 1.3 Key Findings
• 1.4 Target Audience
• 1.5 Companies in Report

2 Artificial Intelligence Technology and Market

• 2.1 Artificial Intelligence and Machine Learning
• 2.2 AI Types
• 2.3 Use of AI and ML in Enterprise
• 2.4 Artificial Intelligence Technology
  • 2.4.1 Machine Learning
  • 2.4.2 Natural Language Processing
  • 2.4.3 Image Processing
  • 2.4.4 Voice Recognition
  • 2.4.5 Artificial Neural Network
  • 2.4.6 Deep Learning
  • 2.4.7 Others
• 2.5 AI and ML Technology Goals
  • 2.5.1 Reasoning
  • 2.5.2 Knowledge Representation
  • 2.5.3 Planning
  • 2.5.4 Learning
  • 2.5.5 Communication
  • 2.5.6 Machine Perception
  • 2.5.7 Motion Manipulation
  • 2.5.8 Social Intelligence
  • 2.5.9 Creativity
  • 2.5.10 Artificial General Intelligence
  • 2.5.11 Computer Vision
  • 2.5.12 Robotics
• 2.6 AI Approaches
  • 2.6.1 Cybernetics and Brian Simulation
  • 2.6.2 Symbolic
  • 2.6.3 Sub-Symbolic
  • 2.6.4 Statistical
  • 2.6.5 Integration
• 2.7 AI Tools
  • 2.7.1 Search and Optimization
  • 2.7.2 Logic
  • 2.7.3 Probability
  • 2.7.4 Classifier and Statistics
  • 2.7.5 Neural Network
  • 2.7.6 Deep Feedforward Neural Network
  • 2.7.7 Deep Recurrent Neural Network
  • 2.7.8 Control Theory
  • 2.7.9 Language
• 2.8 AI Outcomes
• 2.9 Neural Networks and Artificial Intelligence
• 2.10 Deep Learning and Artificial Intelligence
• 2.11 Predictive Analytics and Artificial Intelligence
• 2.12 Internet of Things and Big Data Analytics
• 2.13 IoT and Artificial Intelligence
• 2.14 Consumer IoT, Big Data Analytics, and Artificial Intelligence
• 2.15 Industrial IoT, Big Data Analytics, and Machine Learning
• 2.16 Artificial Intelligence and Cognitive Computing
• 2.17 Transhumanism and Artificial Intelligence

3 Artificial Intelligence and Machine Learning in Big Data and IoT

• 3.1 Machine Learning Everywhere
  • 3.1.1 Machine Learning as Open Source Technology
  • 3.1.2 Machine Learning and Intelligent Discovery in IoT
  • 3.1.3 Supervised and Unsupervised Machine Learning
  • 3.1.4 Machine Learning as Big Data Analysis Technique
  • 3.1.5 Machine Learning AI Robots
  • 3.1.6 Machine Learning and Data Democratization
• 3.2 Machine Learning APIs and Big Data Development
  • 3.2.1 Phases of Machine Learning APIs
  • 3.2.2 Machine Learning API Challenges
  • 3.2.3 Top Machine Learning APIs
    • 3.2.3.1 IBM Watson API
    • 3.2.3.2 Microsoft Azure Machine Learning API
    • 3.2.3.3 Google Prediction API
    • 3.2.3.4 Amazon Machine Learning API
    • 3.2.3.5 BigML
    • 3.2.3.6 AT&T Speech API
    • 3.2.3.7 Wit.ai
    • 3.2.3.8 AlchemyAPI
    • 3.2.3.9 Diffbot
    • 3.2.3.10 PredictionIO
  • 3.2.4 Machine Learning API in General Application Environment
• 3.3 Ultra-Scale Analytics and Artificial Intelligence
• 3.4 Rise of Algorithmic Business
• 3.5 Cloud Hosted Machine Intelligence
• 3.6 Contradiction of Machine Learning
• 3.7 Value Chain Analysis
  • 3.7.1 AI and Machine Learning Companies
  • 3.7.2 IoT Companies
  • 3.7.3 Big Data Analytics Providers
  • 3.7.4 Connectivity Solution and Infrastructure Providers
  • 3.7.5 Hardware and Equipment Manufacturers
  • 3.7.6 Developers and Data Scientists
  • 3.7.7 End Users

4 Artificial Intelligence and Machine Leaning Applications and Services

• 4.1 Intelligence Performance Monitoring
• 4.2 Infrastructure Monitoring
• 4.3 Generating Accurate Models
• 4.4 Recommendation Engine
• 4.5 Block Chain and Crypto Technologies
• 4.6 Enterprise Applications
• 4.7 Contextual Awareness
• 4.8 Customer Feedback
• 4.9 Self-Driving Cars
• 4.10 Fraud Detection Systems
• 4.11 Personalized Medicine and Healthcare Service
• 4.12 Predictive Data Modelling
• 4.13 Smart Machines
• 4.14 Cybersecurity Solutions
• 4.15 Autonomous Agents
• 4.16 Intelligent Assistant
• 4.17 Intelligent Decision Support Systems
• 4.18 Risk Management
• 4.19 Data Mining and Management
• 4.20 Intelligent Robotics
• 4.21 Financial Technology
• 4.22 Machine Intelligence

5 AI Powered Predictive Analytics Market Outlook and Forecasts

• 5.1 Global Market Forecast
  • 5.1.1 Global Market Revenue 2018 - 2023
    • 5.1.1.1 Market by Type 2018 - 2023
    • 5.1.1.2 Market by Business Application 2018 - 2023
    • 5.1.1.3 Market by Core Technology 2018 - 2023
    • 5.1.1.4 Market by Technology Application 2018 - 2023
    • 5.1.1.5 Market by Segment 2018 - 2023
    • 5.1.1.6 Market by Industry Vertical 2018 - 2023
  • 5.1.2 Productivity in Industry Vertical 2018 - 2023
  • 5.1.3 System and Hardware Market 2018 - 2023
  • 5.1.4 Cognitive Computing Market 2018 - 2023
  • 5.1.5 Business Content Creation
  • 5.1.6 Economic Transactions
  • 5.1.7 Robo-Boss and Worker Supervision
  • 5.1.8 Building Security System
  • 5.1.9 Business Analytics Software
  • 5.1.10 Smart Machine and Employment
  • 5.1.11 Self-Service Visual Discovery and Data Penetration Tools
• 5.2 Regional Market Forecasts
  • 5.2.1 Revenue by Region 2018 - 2023
  • 5.2.2 North America Market Forecasts 2018 - 2023
    • 5.2.2.1 Market by Types
    • 5.2.2.2 Market by Business Application
    • 5.2.2.3 Market by Core Technology
    • 5.2.2.4 Market by Technology Application
    • 5.2.2.5 Market by Segment
    • 5.2.2.6 Market by Industry Verticals
  • 5.2.3 Europe Market Forecasts 2018 - 2023
    • 5.2.3.1 Market by Types
    • 5.2.3.2 Market by Business Application
    • 5.2.3.3 Market by Core Technology
    • 5.2.3.4 Market by Technology Application
    • 5.2.3.5 Market by Segments
    • 5.2.3.6 Market by Industry Verticals
  • 5.2.4 APAC Market Forecasts 2018 - 2023
    • 5.2.4.1 Market by Type
    • 5.2.4.2 Market by Business Application
    • 5.2.4.3 Market by Core Technology
    • 5.2.4.4 Market by Technology Application
    • 5.2.4.5 Market by Segments
    • 5.2.4.6 Market by Industry Vertical
  • 5.2.5 ME&A Market Forecasts 2018 - 2023
    • 5.2.5.1 Market by Type
    • 5.2.5.2 Market by Business Application
    • 5.2.5.3 Market by Core Technology
    • 5.2.5.4 Market by Technology Application
    • 5.2.5.5 Market by Segment
    • 5.2.5.6 Market by Industry Vertical
  • 5.2.6 Latin America Market Forecasts 2018 - 2023
    • 5.2.6.1 Market by Type
    • 5.2.6.2 Market by Business Application
    • 5.2.6.3 Market by Core Technology
    • 5.2.6.4 Market by Technology Application
    • 5.2.6.5 Market by Segment
    • 5.2.6.6 Market by Industry Vertical

  6 AI Supported Connected Device Deployment Forecasts

  • 6.1 Global Deployment Forecast 2018 - 2023
    • 6.1.1 Deployment by Device and Platform
    • 6.1.2 Deployment by Application Sector
    • 6.1.3 Deployment by Core Technology
    • 6.1.4 Deployment by Technology Applications
    • 6.1.5 Deployment by Segments
    • 6.1.6 Deployment by Industry Verticals
  • 6.2 Regional Deployment Forecasts 2018 - 2023
    • 6.2.1 Deployment by Region
    • 6.2.2 North America Deployment Forecasts 2018 - 2023
      • 6.2.2.1 Deployment by Device and Platform
      • 6.2.2.2 Deployment by Application Sector
      • 6.2.2.3 Deployment by Core Technology
      • 6.2.2.4 Deployment by Technology Application
      • 6.2.2.5 Deployment by Segment
      • 6.2.2.6 Deployment by Industry Verticals
    • 6.2.3 Europe Deployment Forecasts 2018 - 2023
      • 6.2.3.1 Deployment by Device and Platform
      • 6.2.3.2 Deployment by Application Sector
      • 6.2.3.3 Deployment by Core Technology
      • 6.2.3.4 Deployment by Technology Applications
      • 6.2.3.5 Deployment by Segment
      • 6.2.3.6 Deployment by Industry Vertical
    • 6.2.4 APAC Deployment Forecasts 2018 - 2023
      • 6.2.4.1 Deployment by Device and Platform
      • 6.2.4.2 Deployment by Application Sector
      • 6.2.4.3 Deployment by Core Technology
      • 6.2.4.4 Deployment by Technology Application
      • 6.2.4.5 Deployment by Segment
      • 6.2.4.6 Deployment by Industry Vertical
    • 6.2.5 ME&A Deployment Forecasts 2018 - 2023
      • 6.2.5.1 Deployment by Device and Platform
      • 6.2.5.2 Deployment by Application Sector
      • 6.2.5.3 Deployment by Core Technology
      • 6.2.5.4 Deployment by Technology Application
      • 6.2.5.5 Deployment by Segment
      • 6.2.5.6 Deployment by Industry Vertical
    • 6.2.6 Latin America Deployment Forecasts 2018 - 2023
      • 6.2.6.1 Deployment by Device and Platform
      • 6.2.6.2 Deployment by Application Sector
      • 6.2.6.3 Deployment by Core Technology
      • 6.2.6.4 Deployment by Technology Application
      • 6.2.6.5 Deployment by Segment
      • 6.2.6.6 Deployment by Industry Vertical

    7 Company Analysis

    • 7.1 AI Initiatives and Acquisition Strategies
      • 7.1.1 Google
      • 7.1.2 Twitter
      • 7.1.3 Microsoft
      • 7.1.4 IBM
      • 7.1.5 Apple
      • 7.1.6 Facebook
      • 7.1.7 Amazon
      • 7.1.8 Skype
      • 7.1.9 Salesforce
      • 7.1.10 Intel
      • 7.1.11 Yahoo
      • 7.1.12 AOL
      • 7.1.13 NVIDIA
      • 7.1.14 x.ai
      • 7.1.15 Tesla
      • 7.1.16 Baidu
      • 7.1.17 H2O.ai
      • 7.1.18 SparkCognition
      • 7.1.19 OpenAI
      • 7.1.20 Inbenta
    • 7.2 Big Data, Analytics, and IoT Companies and Solutions
      • 7.2.1 Tachyus
      • 7.2.2 Sentrian
      • 7.2.3 Maana
      • 7.2.4 Veros Systems
      • 7.2.5 Neura
      • 7.2.6 Augury Systems
      • 7.2.7 Glassbeam
      • 7.2.8 Comfy
      • 7.2.9 mnubo
      • 7.2.10 C-B4
      • 7.2.11 PointGrab
      • 7.2.12 Tellmeplus
      • 7.2.13 Moov
      • 7.2.14 Sentenai
      • 7.2.15 Imagimob
      • 7.2.16 FocusMotion
      • 7.2.17 MoBagel

    8 Conclusions and Recommendations

    • 8.1 Recommendations for Data Analytics Providers
    • 8.2 Recommendations for AI and Machine Learning Companies
    • 8.3 Recommendations for IoT Companies and Equipment Manufacturers
    • 8.4 Recommendations for Service Providers