Machine learning for the layperson: pt1

Why you should care

AI and Machine Learning are Different. Cool Picture though right?

Machine Learning undoubtedly has an impact on your daily life right now, whether you are aware of it or not. What you watch, what you buy, and potentially who you are romantically involved with are all impacted. Companies like Netflix, Amazon and Tinder are all making huge investments into machine learning and it’s extremely exciting.

Getting you to the doctor before you feel symptoms, inferring what kind of cell populations make up cancerous tumors to better treat them, picking out the insight from the noise in new and enormous super telescopes… the list goes on.

It’s 100% rosy though.

There will be a lot of ethical conversations as well. If a company knows too much about you but only because they’re predicting it with a high degree of accuracy, is that an invasion of privacy? What if these predictions make your health insurance more expensive? Is there an obligation for match making sites to avoid segregating demographics or is that then playing god on a slippery slope towards eugenics? What constitutes profiling in data driven predictive policing solutions?

I don’t claim to have the answer to any of these questions. I’m just asserting that we are all going to have to answer them ourselves so we’d better get familiar with the topic. As it stands, there is a lot of confusion about what Machine Learning is and is not.

No.

It was pointed out on ‘The Talking Machines’ that the machine learning community often revels in using deeply specialized mathematical language. It doesn’t have to be that way though. While there is some amazingly clever math that goes into this stuff, a lot of the core ideas are very relatable.

As someone who is working on ML(Machine Learning) projects as a hobbyist, I’m hardly removed from ‘layperson’ myself. That’s why I think I can be a good ambassador and why I’ve decided to write this series.

I think it’s important that everyday people start trying to attain a basic literacy in the subject so they can participate in these important conversations. Look out for part two this weekend where I’ll be explaining our first and easiest machine learning model: linear regression.

If you would like other ML resources, I’d start with Andrew Ng’s online course and ‘The Talking Machines’ podcast

If you enjoyed this post or have ideas to improve it, let me know! I like to write about things I think are relevant to my path as a growing developer so expect general ramblings on ~self improvement~ and ~Computer Science~ wooOOoo

info/contact at camwhite.io