I write about design, technology, and people.
Sometimes I take photos of places I visit.
– if you're interested in older posts or looking for something specific.
The hype around artificial intelligence (AI) and machine
learning (ML) skyrocketed in late 2016. I felt at the time
that my lack of understanding of the topics affected my
ability to discuss them with others and my ability to grapple
with some of the more existential questions around algorithm
fairness. At that moment I decided to immerse myself in the
world of data science. I’ll share the resources and
educational materials I’ve used for more than a year with a
hope that you’ll find them helpful too.
Context: I’ve spent around 15 months learning statistics and
machine learning (ML). I’ve written a post about the journey
and resources I’ve used. Here I try to share my thoughts and
observations about what ML is in essence, why we experienced
the recent hype, and some of the potential dangers and
opportunities that await us in the future.
Machine learning is pattern recognition
If I had to summarize what ML is as practiced today, I would
say pattern recognition. Many of the algorithms try to find
patterns in data or build patterns from rules and the
environment. Many things are a pattern in some form or
another. For example, home prices usually increase with size,
a disease has similar symptoms across a population, people in
the same life situation buy similar things (young families buy
a lot of baby stuff), similar objects have recognizable shapes
and color, traffic participants respond to changes in the
environment similarly, and speech is just a collection of
patterns of sound waves. There are more examples than I can
think of, and that’s the beauty of ML—you can use it for so
many things. It doesn’t mean it will replace everything we do
today, just that it can help us in more areas than it does at
Even though I’ve listed only a few books on the topic, last
year I read a lot about current technology and how it may
affect our society in the future. Ethics, machines that
“think,” and our inability to comprehend complexity around us
were the themes of the year, prompted by my short post about
fairness in late 2016.
Here are some of the books you should consider putting on your
As I was reflecting on the past year, the phrase “delicate
balance” was the only thing that came to mind. There is one
point at which opposing forces form a balance between work and
family time, between pushing yourself to the limit and taking
a break, and between being online and offline. There isn’t one
correct ratio—it depends on the individual and the
environment. I found mine in 2017.
Red and white, and a lot of snow—so very Swiss.
If you want to read more, check out the