It makes complete sense depending on the time. My first advisor in grad school was a more traditional "cog sci / comp sci" phd and then I took a "data mining" grad-level course, but aside from that I'm a complete outsider to the field, but here's my take:
Since the 70s, AI was dominated by lisps and nearly everybody was driving at "general" AI by emulating or even attempting to simulate human thought processes.
In the 00s people started putting more weight behind statistical / stochastic methods for arriving at answers without necessarily trying to use any model of human cognition. This was called "data mining" and then "machine learning" (partly, I presume, to distance it from the 40 years of academia's dismal failure to produce working AI) and only in the last ~5 years has it come around to people starting to use the term "artificial intelligence" again.
In the early days of the revamp, it was common to see janky perl and fortran and not much structure holding it all together. Lately, I gather, the ecosystem is nearly entirely python-centric, but this is due to the gravity well around pandas. There's nothing in particular about python that makes it suitable to the task, though it is happily more approachable/accessible for academics than if a pandas equivalent had instead evolved in C++ or Java.
Since the 70s, AI was dominated by lisps and nearly everybody was driving at "general" AI by emulating or even attempting to simulate human thought processes.
In the 00s people started putting more weight behind statistical / stochastic methods for arriving at answers without necessarily trying to use any model of human cognition. This was called "data mining" and then "machine learning" (partly, I presume, to distance it from the 40 years of academia's dismal failure to produce working AI) and only in the last ~5 years has it come around to people starting to use the term "artificial intelligence" again.
In the early days of the revamp, it was common to see janky perl and fortran and not much structure holding it all together. Lately, I gather, the ecosystem is nearly entirely python-centric, but this is due to the gravity well around pandas. There's nothing in particular about python that makes it suitable to the task, though it is happily more approachable/accessible for academics than if a pandas equivalent had instead evolved in C++ or Java.