Much of the division between technology and human activity is artificial — how do people make our work possible? — We don’t talk enough about how much manual, human work we rely upon to make the exciting advances in ML possible. The truth is, the division between technology and human activity is artificial. All the inputs that make models are the result of human effort, and all the outputs in…

Learning how to be a professional data scientist is different now, but it’s not impossible — Today’s column is partly about data science, but it’s also about the sociology of work. As a senior practitioner in the field, I started my data science career long before Covid-19 and the radical shift in the way we work today developed. I started my professional career years before even…

Businesses are jumping on a bandwagon of creating something, anything that they can launch as a “Generative AI” feature or product. What’s driving this, and why is it a problem? — The AI Hype Cycle: In a Race to Somewhere? I was recently catching up on back issues of Money Stuff, Matt Levine’s indispensable newsletter/blog at Bloomberg, and there was an interesting piece about how AI stock picking algorithms don’t actually favor AI stocks (and also they don’t perform all that well on the picks they do make). …

Do our definitions of “mistake” make sense when it comes to ML/AI? If not, why not? — A comment on my recent post about the public perception of machine learning got me thinking about the meaning of error in machine learning. The reader asked if I thought machine learning models would always “make mistakes”. As I described in that post, people have a strong tendency to anthropomorphize…

do you think it will always make mistakes Yes, I think it will. Machine learning is founded upon probabilities, at its core. In some way or another, everything machine learning produces is because a probabilistic estimation indicated this was more likely than other alternatives to be the desired answer. …

Why machine learning literacy for the public needs to be a priority for data science, and what we can do about it. — I was listening to a podcast recently with an assortment of intelligent, thoughtful laypeople (whose names I will not share, to be polite) talking about how AI can be used in healthcare. I had misgivings already, because they were using the term “AI”, which I find frequently means everything and…

Data science roles can be very different, and job postings are not always clear. What hat do you want to wear? — After the positive responses to my recent post in Towards Data Science about Machine Learning Engineers, I thought I would write a bit about what I think are the real categories of roles for data science practitioners in the job market. While I was previously talking about the candidates, e.g…

Does “Machine Learning Engineer” mean something new to our field? If so, what? — The title is a trick question, of course. Much like Data Scientist before it, the title Machine Learning Engineer is developing into a trend in the job market for people in our profession, but there is no consensus about the meaning of the title or the functions and skills it…

I sometimes mention in my written work and speeches that I have a sociology background, and used to be an adjunct professor of sociology at DePaul University before embarking on my data science career. …