Why AI Projects Fail, Part 2: Common Pitfalls

Published
Oct 30, 2019
Reading time
1 min read
Why AI Projects Fail, Part 2: Common Pitfalls

Dear friends,

Welcome to the Halloween edition of The Batch!

I promised last week to share some common reasons for AI project failures. But first, let’s start with some of the least common reasons.

If your AI project fails, it is probably not because:

  • Your neural network achieved sentience. Your implementation of ResNet not only refused to classify cat pictures accurately, but worse, it set out to enslave humanity.
  • A poltergeist inhabits in your hardware. Now you know the real reason why GPUs run so hot. Track your system’s temperature and make sure you have an exorcist in your contacts.
  • Daemon and zombie processes are in progress. Daemons and zombies are active in your computer. Wikipedia says so, so we know it to be true. Simple solution: Wipe all hard drives and find a different line of work.

A hair-raising Halloween to all of you who celebrate it, with plenty of tricks and treats.

Keep learning,

Andrew

Read part 1 of this series now.

Read part 3 of this series now.

Read part 4 of this series now.

Read part 5 of this series now.

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