Machine Learning Skepticism

Julia Evans gives reasons to tamp down expectations with machine learning:

When explaining what machine learning is, I’m giving the example of predicting the country someone lives in from their first name. So John might be American and Johannes might be German.

In this case, it’s really easy to imagine what data you might want to do a good job at this — just get the first names and current countries of every person in the world! Then count up which countries Julias live in (Canada? The US? Germany?), pick the most likely one, and you’re done!

This is a super simple modelling process, but I think it’s a good illustration — if you don’t include any data from China when training your computer to recognize names, it’s not going to get any Chinese names right!

Machine learning projects are like any other development projects, with more complex algorithms.  There’s no magic and there’s a lot of perspiration (hopefully figuratively rather than literally) involved in getting a program which behaves correctly.

Related Posts

A Quick Keras Example

Shubham Dangare takes us through a quick example using Keras and TensorFlow in Python: Keras is a high-level neural networks API, written in Python and capable of running on top of Tensorflow, CNTK  or Theano. It was developed with a focus on enabling fast experimentation. In this blog, we are going to cover one small […]

Read More

ML Services and Injectable Code

Grant Fritchey looks at sp_execute_external_script for potential SQL injection vulnerabilities: The sharp eyed will see that the data set is defined by SQL. So, does that suffer from injection attacks? Short answer is no. If there was more than one result set within the Python code, it’s going to error out. So you’re protected there. […]

Read More

Categories

May 2016
MTWTFSS
« Apr Jun »
 1
2345678
9101112131415
16171819202122
23242526272829
3031