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Introduction

The AI community has now a long enough memory, having survived a few "AI winters" when research funds dried up following largely unfulfilled hype, that it can take a longer view about current favorites such as DL.

Highlights

  • Neuroscience-based systems are beginning to make a showing in the AI market.
  • The number of players in the AI hardware accelerator market has expanded to unsustainable levels.
  • The ML tools market has mushroomed; Ovum segments the market.

Features and Benefits

  • Learn how the number of players in the AI hardware accelerator market has expanded to unsustainable levels.
  • Learn how best to segment the ML tools market.

Key questions answered

  • Why is Julia a good fit for programming ML applications?
  • How does the AI processor market segment across the players?

Table of contents

Summary

  • Catalyst
  • Ovum view
  • Key messages

Recommendations

  • Recommendations for enterprises
  • Recommendations for vendors

Neuroscience-based systems are beginning to make a showing in the AI market

  • Machine learning has more to achieve
  • Slow-burning Numenta research shows dividends

The number of players in the AI hardware accelerator market has expanded to unsustainable levels

  • AI hardware accelerators appear from incumbents and startups
  • The MLPerf benchmark standard grows with industry support

The ML tools market has mushroomed; Ovum segments the market

  • Segmenting the machine learning tools market

Julia shapes up to be the programming language for machine learning

  • Python and R are largely popular in data science
  • Julia shows promise for ML

Appendix

  • Further reading
  • Author