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Introduction

The subfield in artificial intelligence (AI) known as machine learning or cognitive computing has in recent years become highly active as techniques such as deep learning and IBM’s Watson initiative have given rise to improved performance in their generalization capabilities.

Highlights

  • Deep Learning based neural networks have created a step change improvement in the accuracy of AI systems, enabling human-machine interface through speech.

Features and Benefits

  • Understand that the combination of Deep Learning algorithms accelerated on GPUs has been the pivotal breakthrough that has also accelerated progress in the field.
  • Understand that there are two kinds of AI: Type A aims to be a true artificial brain, and Type B which has a scope limited to performing useful specific tasks intelligently.

Key questions answered

  • What is deep learning and how can I use it in practice?
  • What are the type of AI systems on the market?

Table of contents

Summary

  • Catalyst
  • Ovum view
  • Key messages

Recommendations

  • Recommendations for enterprises
  • Recommendations for vendors

Deep learning-based neural networks have created a step change improvement in the accuracy of AI systems

  • The impact of deep learning

Business applications powered by deep learning are now available

  • Startups are emerging with deep learning-based applications
  • The next decade: a market for AI systems worth billions

The combination of deep learning algorithms accelerated on GPUs has been the pivotal breakthrough

  • The ImageNet competition shows a 26% to 5% error rate improvement in the last five years
  • Nvidia is the main player in the GPU accelerated AI market
  • Open source and sharing of knowledge is helping accelerate AI innovation

There are two kinds of AI and only one is practical for businesses

  • Defining AI
  • Dark side of AI

Appendix

  • Further Reading
  • Author

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