Take a standard 52 deck of cards and draw two cards. Can you find something in common about these two cards?
Can you conclude from this observation that someone sorted the deck in a special way?
You may not be certain. Let’s pick two more pairs of cards.
Now that you have three data points, what is your conclusion: Someone arranged this deck in a special way or not?
That’s what most data scientist are doing in a daily basis.
Given a dataset, we’re trying to find surprising correlations. …
Two months ago, I had a first meeting with one of the wind turbine industry leader. His goal was to use machine learning to make wind turbine inspection faster and cheaper.
As for each of these meetings, I started with the question :
How many errors are acceptable for you?
And he answered what everyone in the industry agrees on:
We can’t accept any error, our job is way too sensitive
Machine learning is now everywhere and seems to be able to solve most problems. But running neural network is not an easy task without a proper data science team. So more and more companies are issuing A.I. focused Request For Tender
One main challenge is: how to measure the accuracy of an algorithm?
More than often we see requierements to one accuracy metric :
Your Machine Learning algorithm needs to have over 90% accuracy.
- another artificial intelligence Request For Tender
This article will show that a high score can hide poor business performance.
Detecting a rare problem
Cornis is inspecting wind turbines since 2011. By 2019, we have acquired 4 millions of pictures. During the same period, our experts annotated only 6000 high critical defects. …