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

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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

Artificial Intelligence and Machine Learning in Big Data and IoT Market for Data Capture, Analytics, and Decision Making 2018-2023

 

More than 50% of enterprise IT organizations are experimenting with Artificial Intelligence (AI) in various forms such as Machine Learning, Deep Learning, Computer Vision, Image Recognition, Voice Recognition, Artificial Neural Networks, and more. AI is not a single technology but a convergence of various technologies, statistical models, algorithms, and approaches. Machine Learning is a sub-field of computer science that evolved from the study of pattern recognition and computational learning theory in AI.

 

Every large corporation collects and maintains a huge amount of human-oriented data associated with its customers including their preferences, purchases, habits, and other personal information. As the Internet of Things (IoT) progresses, there will an increasingly large amount of unstructured machine data. The growing amount of human-oriented and machine generated data will drive substantial opportunities for AI and Machine Learning support for unstructured data analytics solutions.

 

This research evaluates various AI technologies and their use relative to analytics solutions within the rapidly growing enterprise data arena. The report assesses emerging business models, leading companies, and solutions. The report also provides forecasting for unit growth and revenue from 2018 – 2023 associated with AI supported predictive analytics solutions. All purchases of Researcher reports includes time with an expert analyst who will help you link key findings in the report to the business issues you're addressing. This needs to be used within three months of purchasing the report.

 

Report Benefits:

 

Forecasts for AI in predictive analytics 2018 to 2023

Identify highest potential AI technology area opportunities

Understand AI strategies and initiatives of leading companies

Learn the optimal use of AI for smart predictive analytics in IoT data

Understand the AI in Big Data, Analytics, and IoT ecosystem and value chain

Identify opportunities for AI in Analytics for IoT and other unstructured data

 

Key Findings:

 

North America will lead the AI in Big Data and IoT through 2023

The AI powered predictive analytics market will reach $18.5 billion by 2023

Autonomous robots and intelligent agents will be the top application areas

AI will find its way into edge data devices for security and real-time data analytics

 

Target Audience:

 

Internet of Things companies

Artificial Intelligence companies

Big Data and analytics companies

Robotics and automation companies

Cloud and Internet of Things companies

Investment firms focused on automation

Product and service providers of all types

Governments and NGO R&D organizations

 

Companies in Report:

 

Amazon

AOL

Apple

Augury Systems

Baidu

C-B4

Comfy

Facebook

FocusMotion

Glassbeam

Google

H2O.ai

IBM

Imagimob

Inbenta

Intel

Maana

Microsoft

mnubo

MoBagel

Moov

Neura

NVIDIA

OpenAI

PointGrab

Salesforce

Sentenai

Sentrian

Skype

SparkCognition

Tachyus

Tellmeplus

Tesla

Twitter

Veros Systems

x.ai

Yahoo
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