Several folks have heard me express the believe that leaning more into data science should be foundational for systems engineers because of the need to examine things from many different angles. In current practice, we are more like intelligence agents. In that I mean, we identify, develop, and continuously come back to assets in specific corners of a project to build up our insight about what is happening and what problems are on the way. We also trade what we’ve gathered to these folks in order to keep the contacts going.
Jeremiah Crane
5mo
Completely agree! Do have any recommended papers you've read? We've started a group dedicated to AI4SE.
Lockheed Martin Advanced Technology Center
5moWhen discussing data science, I think folks are a little hasty to jump straight to AI. If I’ve learned anything about systems engineering over the years, it’s that we start with basics, with fundamentals. Data science starts with every flavor of multi-dimensional linear regression you can think of, and then some. This is the basis of the models. If we don’t understand the models, and understand them thoroughly, we are inches away from shooting outselves in the foot by jumping to AI. We can trace through why some solution space is valid by developing a multidimensional and multimodal regression. As soon as we kick in the NNs, we are just trusting the process, skipping the real detective work Bjorn is referring to.