Translinearity is the super-set of what’s linear, so as to include what is not linear, in a meaningful manner. In data analytics, it includes all connections among data points and variables that make sense in order to maintain robustness (i.e. avoid any kind of over-fitting). Although fairly abstract, it is in essence what has brought about most modern fields of science, including Relativistic Physics. Naturally, when modeled appropriately, it can have an equally groundbreaking effect in all kinds of data analytics processes, including all the statistical ones as well as some machine learning processes. Effectively, a framework based on translinearity can bridge the different aspects of data science processes into a unified whole where everything can be sophisticated enough to be considered A.I. related while at the same time transparent enough, much like all statistical models.
Because we have reached the limits of what the linear approach has to offer through Statistics, Linear Algebra, etc. Also, the non-linear approach, although effective and accessible, are black boxes, something that may remain so for the foreseeable future. Also, the translinear approach can unveil aspects of the data that are inaccessible with the conventional methods at our disposal, while they can help cultivate a more holistic and more intuitive mindset, benefiting the data scientists as much as the projects it is applied on.
So far, Translinearity is implemented in the Julia ecosystem by myself. This is something I've been working on for the past decade or so. I have reason to believe that it is more than just a novelty as I have observed various artifacts concerning some of its methods, things that were previously considered impossible. One example is optimal binning of multi-dimensional data, developing a metric that can assess the similarity of data points in high dimensionality space, a new kind of normalization method that combines the benefits of the two existing ones (min-max and mean-std normalization, aka standardization), etc.
Translinearity is made applicable through the systematic and meticulous development of a new data analytics framework, rooted in the principles and completely void of assumptions about the data. Everything in the data is discovered based on the data itself and is fully parametrized in the corresponding functions. Also, all the functions are optimized and build on each other. A bit more than 30 in total, the main methods of this model cover all the fundamentals of data analytics and open the way to the development of predictive analytics models too.
Translinearity opens new roads in data analytics rendering conventional approaches more or less obsolete. However, the key outcome of this new paradigm of data analytics is the possibility of a new kind of A.I. that is transparent and comprehensible, not merely comprehensive in terms of application domains. Translinearity is employed in the more advanced deep learning systems but it’s so well hidden that it escapes the user. However, if an A.I. system is built from the ground-up using translinear principles, it can maintain transparency and flexibility, to accompany high performance.
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Zacharias Voulgaris, PhD
Passionate data scientist with a foxy approach to technology, particularly related to A.I.