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Game Changers—Artificial Intelligence: What You Need to Know

Game Changers—Artificial Intelligence: What You Need to Know

Artificial intelligence (AI) is a vision, a goal, and a set of technologies. Its breadth and complexity make it a difficult subject to understand and explain. Adding to the confusion is the number and variety of terms used. Machine learning (ML), deep learning, neural networks, and predictive analytics describe different AI approaches. Other marketing terms such as cognitive computing or autonomous machines further muddy the water.

The term artificial intelligence is often used to refer to artificial general intelligence (AGI). This is a type of AI that can transfer learning from one domain to another. AGI will be able to apply learning techniques to gain new skills without pre-programming. This AI is also called 'strong AI' and would be indistinguishable from a human mind.

Artificial super-intelligence (ASI) is the second type of AI. This is an extension of AGI, and would be superior to humans in every domain—from logic to creativity and from social intelligence to persuasion. It is this type of AI that forms the basis of media and cultural stories about the future of AI and robotics.

The most ground-breaking developments are occurring in the third category: artificial narrow intelligence (ANI). ANI systems have the ability to complete pre-defined and limited tasks. ANI is already part of software such as Google Search, Netflix, and Apple Siri. Once ANI algorithms are embedded in software, the threshold for what constitutes ANI shifts higher.

ML is the AI approach that gives machines the ability to learn from data. Other AI approaches, symbolic and statistical, use a different rule-based approach.

About this report

It is difficult to understand what is happening in the field of artificial intelligence (AI). New developments seem to happen weekly, and companies use different words to describe their products. The terms artificial intelligence, cognitive intelligence, autonomous machines, and machine learning are all thrown around. This research brings clarity to the complex AI landscape and explores recent breakthroughs in a technique called deep learning, explaining how it is making progress in AI challenges such as language, vision, and motion. Finally, the research takes a broad look at the impact of AI in the enterprise, specifically the energy, financial services, healthcare, manufacturing, and transportation industries.


  • Executive Summary
    • Key Findings
  • Why Explore Artificial Intelligence?
    • Identification of 30 Key Technology Trends
    • Scoring Technology Trends by Disruptive Potential
  • Artificial Intelligence
    • Artificial Intelligence Overview
    • Artificial Intelligence Overview-Other Terminology
    • Artificial Intelligence Approaches Map
    • Artificial Intelligence Approaches
    • Key Driver of Artificial Intelligence Progress-Exponential Rate of Change
  • Machine Learning
    • Machine Learning Overview
    • Drivers in the Development of Machine Learning
    • Restraints in the Development of Machine Learning
    • Most Important Restraint-The End of Moore's Law
    • Machine Learning by Learning Style
    • Machine Learning by Function
    • Key Machine Learning Algorithm Classes by Function
    • Machine Learning Business Models
  • Neural Networks
    • Neural Networks Overview
    • Neural Networks by Class
  • What is Deep Learning?
    • Deep Learning Overview
    • Key Deep Neural Network Architectures
    • Leaders in Deep Learning-B2C Business Model
    • Deep Learning Applications
  • Applications-Text Natural Language Processing
    • Text Natural Language Processing Overview
    • Leaders in Text Natural Language Processing
  • Applications-Audio Natural Language Processing
    • Audio Natural Language Processing Overview
    • Leaders in Audio Natural Language Processing
  • Applications-Computer Vision
    • Computer Vision
    • Leaders in Machine Vision
  • The Enterprise
    • Machine Learning Will be a Part of Every Function in Every Business
    • Machine Learning for Sales, Marketing, and Personal Assistants
    • Machine Learning for Communication, HR, and Security
    • Machine Learning for Customer Service, Finance, and Productivity
    • What Happens When Jobs Run Out?
  • Energy & Natural Resources Industry
    • Distributed Energy Resources
    • Smart Grid
    • Digital Oilfield (DOF)
  • Financial Services Industry
    • Payments & Loyalty
    • Retail Banking Data Mining
    • Blockchains
  • Healthcare Industry
    • Digital Hospital
    • mHealth Services
    • Personalised Medicine
  • Manufacturing Industry
    • Distributed Infofacturing
    • Collaborative Manufacturing
    • Mobile Robotics
  • Transportation Industry
    • Connected Vehicles
    • Mobility Integration
    • Autonomous Cars
  • The Last Word
    • The Last Word-3 Key Takeaways
  • Appendix
    • Key Technologies Definitions

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