Statistics is a very interesting field that has some relevance to data science work. Perhaps not as much as some people claim, but it's definitely a useful toolset, particularly in the data exploration part of the pipeline. But how can Stats improve, particularly when it comes to new technologies like A.I.?
Statistics can benefits from such technologies in various ways. The most important is the mindset of the AI-based approach, which is empirical and pragmatic. Instead of imagining complex theories for explaining the data, AI looks at it as it is and works with it accordingly (the data-driven approach).
Conventional Statistics on the other hand tries to fit the data into this or the other mathematical model for describing it and then it processes the data with some arbitrary metrics that are mediocre at best. However, the math is elegant, so we go with it anyway. So many textbooks can’t be wrong, right? Well, in data science there is no right or wrong, just models that work well and others that don’t work as well. Since there is usually money on the line, we prefer to go with the former models, which coincidentally tend to be machine learning related, particularly AI-based. So, there is surely room for improvement for Statistics if it were to adopt the same mindset.
What's more, Statistics can benefit from A.I. through additional heuristics (or statistics, as they are often referred to in that context). The existing heuristics may work well, but they are very narrowly defined, making them overly specialized. AI-based heuristics are broader and tend to be more applicable in a variety of data sets. If Statistics were to adopt a similar approach to heuristics, it would for sure benefit greatly and become more widely applicable.
Finally, Statistics can benefit from A.I. by embracing a different approach to describing the data. This is a more fundamental change and probably most fans of the field will disregard it as impractical. However, it is feasible and even efficient, with a bit of clever programming (based on heuristics and a geometrical approach). The latter is something Statistics seems to be divorced from, which is another area of improvement.
It's worth noting that although developments in Statistics are bound to be beneficial to anyone applying it in data analytics projects, the practitioner also needs to evolve. There is no point in advancing this field if its practitioners remain in their old ways, limited and rigid. Perhaps that's one of the reasons machine learning and A.I. have advanced so much; their practitioners are more open to changes and willing to adapt. No wonder these fields now dominate the data science world. Something to think about...
PS - This article was supposed to be published yesterday. However, there was an issue with the scheduler, hence the delay. Normally, I'll have new material every Monday and sometimes on Thursdays too. Cheers!
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Zacharias Voulgaris, PhD
Passionate data scientist with a foxy approach to technology, particularly related to A.I.