Roni Kobrosly Ph.D.'s Website

The great Benn Stancil wrote a blog post way back in the summer of 2022, and ever since I read it I can't get it out of my mind. As you can guess from the title of this blog post, the topic is: "Do data-driven companies actually win? Some gut-based judgements on the effectiveness of data." We take for granted that collecting data, mining it for insights, and building ML gives companies a competitive edge. Is that true though? Have we rigoriously assessed that?

Whenever I discuss causal thinking with data, people inevitably bring up the topic of automated causal discovery. I get it. It's sort of like the search for El Dorado and would change the field of data science to its core if it were possible. I talk about what it is and how this is unlikely to happen anytime soon.

My updated website and the joys of Terminal CSS and static site generators.

Anyone who has worked in tech in the data science / MLE / applied ML field knows that most ML projects will fail. Newcomers to the field have super high expectations about what they can do, and, sadly, this leads to a lot of them leaving the space altogether. ML project failure is an inevitable part of life in this field and it's happened to me many times at different stages of my career. In the last couple of years, I've begun to think about the building of ML projects as a sort of funnel process; ideas are cheap and many, but only a small amount of these ideas reach the end of the funnel and become productionized, solid, used, effective, and maintained ML applications.

One of the hottest ChatGPT takes I've heard from mostly non-technical, tech-enthusiast folks is that it, or one of it's near-term successors, will effectively put software engineers into the dustpan of history. The following is my attempt to collect my thoughts around this and present my case to a lay audience on why this will not be the case.