Machine Learning

Steve Jobs famously said that computers would be like "bicycles for the mind". What effect will AI have for our minds?

For all the toil that went into developing the pipeline and deep learning model for my ML baby monitor project, I discovered that my model didn't actually solve the problem at hand. This kind of gap exists in the real world of ML development as well, and there's a critical insight from the field of User-Centered Design that could be the key to bridging it.

Over a year ago, I wrote a couple of blog posts about how I trained a TensorFlow model on audio clips of my crying baby so I could get some insights into his sleep patterns and behavior. I distilled this experience into a PyGotham talk titled "Can Neural Networks Make Me a Better Parent?" and it reflects some extra maturity in my machine learning knowledge, as well as some hard-won parenting wisdom and experience.

In which we train an TensorFlow model with the tears of my little one, then deploy it on a Raspberry Pi.

How do you deal with the travails of colic and new parenthood? You plunge yourself into an ML project, of course. Here, we discuss a naive solution to detecting baby cries via an onboard microphone on a Raspberry Pi.