When we think about artificial intelligence, we often think that the artificial part is what will be challenging. After all, in engineering we want to build solutions that in some sense contain the problem they are trying to solve. But the real difficulty is figuring what we mean by intelligence. And that’s important, because as a society we are building systems that are supposed to be intelligent.
Not all types of unsupervised learning algorithms have the same level of performance in dynamic environments. Algorithms with certain characteristics are more robust to changes in the data being produced.
Writing the abstract for your paper can be one of the hardest things to do. Most readers who get past your title will scan your abstract to decide if they want to continue. The goal is clear, then. You want to hook the reader with a summary of your work that is compelling and accurate. And short. How to balance it all?
I was volunteering as a tutor with Pathways to Education (P2E) in Kingston during the first year of my PhD when I got an email. The P2E Kingston Program Researcher was trying to answer the question “Are P2E’s interventions having the desired effects?” Here’s how I used data mining to help.
For more insight into my background, you can browse my papers & academic works.