As we talked about in the previous article, open-mindedness is our ability to view things from a wider perspective, with as few assumptions as humanly possible. We saw how this concept is relevant and useful in a data science project. But how does open-mindedness benefit A.I. in practice?
Just like in the data science case, learning A.I. also stands to benefit from open-mindedness. This is quite obvious, considering how mediocre a job most of the educators of A.I. do and the number of con artists in this field. Keeping an open mind about A.I. and learning it properly are essential for becoming a good A.I. practitioner, particularly one who can see beyond ANNs. After all, there is much more to A.I. than this graph-based family of systems and no matter how good these are, there are always other things to learn such as AI-based optimization methods and Fuzzy Logic systems.
Researching A.I. is probably that case where open-mindedness not only shines but it’s also more or less essential. This is something harder and harder to do since everything in the A.I. research space seems to be geared towards either ANNs or NLP (usually ANN-based). It’s quite rare to find any originality in the A.I. research these days, though a lot of the stochastic optimization systems (which are AI-based optimizers) have some pretty good ideas behind them. Also, some AI-based dimensionality reduction methods are quite interesting too.
Open-mindedness can also be applied when exploring the limits of an A.I. system. This is particularly important in a project since it’s easy to get stuck with the impression that an A.I. system is the best choice, even if it isn’t. Open-mindedness allows you to view other options and consider the limitations of an A.I. system more realistically, making more pragmatic choices.
Finally, open-mindedness as an attitude can help us have a more humble view of things when it comes to A.I. After all, this is a fairly new field (compared to data analytics, for example) and has a lot of room for improvement, something many A.I. practitioners tend to forget. Perhaps that's why many such practitioners are still comparing A.I. models with Stats one (the equivalent of comparing a sports car with a toy car), instead of looking at the various non-AI-based machine learning options, which can be viable alternatives to the project at hand.
In any case, you can learn more about A.I. from an open-minded perspective through a book I have co-authored a couple of years ago. Namely, the AI for Data Science book from Technics Publications covers this subject and provides a variety of stimuli for researching it properly. Although the term open-mindedness is not used per se in this book, it does delve into the way of thinking of an AI-oriented data scientist. So, check it out when you have the chance. Cheers!
Zacharias Voulgaris, PhD
Passionate data scientist with a foxy approach to technology, particularly related to A.I.