biotechnology and Pharmaceutics INNOVATION

Topological Dynamics in Metamodel Discovery with Artificial Intelligence:

From Biomedical to Cosmological Technologies

Author: Ariel Fernández
Publication: December, 2022
Publisher: Taylor and Francis, Chapman and Hall/CRC

Review by Weishi Meng

The forthcoming book by Ariel Fernandez reveals the sheer breadth of his intellectual acumen. It seems the author is reinventing himself one more time in what amounts this time to a veritable metamorphosis. In the past four decades, Dr. Fernandez  (阿列尔·费尔南德) has contributed five books and more than 400 research papers to areas as distant as abstract algebra, topological dynamics, chemical physics, biophysics, evolutionary biology and rational drug design, among others. As if that weren’t enough, when the pandemic stroke he turned himself into an expert on Covid-19 and did so in no time flat. This is attested by the fact that Ariel Fernandez’ contributions to the field of Covid-19 were cited by the Wuhan Institute of Virology, the institution at the forefront of the research on Covid-19 (for better or for worse, but that is another story). On an even more recent and dramatic turn in his career, Ariel Fernandez is now leveraging artificial intelligence for meta-model discovery in areas such as cosmology and particle physics, where big data demands a massive modeling effort and data-driven research has not always found its footing. Ariel Fernandez’ new book is placed a the forefront of this endeavor, taking model discovery to the next level: the development of “machine intuition”. This type of intuition is enshrined in the concept of “metamodel”. A metamodel is a more primitive and yet in some ways a more sophisticated entity than a model. Fernandez has compared a metamodel with a Picasso painting of his late period contrasted with the more “academic” works of his youth…

​​ The book is entitled “Topological Dynamics in Metamodel Discovery with Artificial Intelligence: From Biomedical to Cosmological Technologies”. With the implementation of topological methods to construct meta-models, Ariel Fernandez’ book engages with levels of complexity and multiscale hierarchies so far considered off limits for data science. The book paves the way to Fernandez’ project for 2023: Reverse engineer the Standard Model of particle physics through artificial intelligence in order to unravel the origin and nature of dark matter and dark energy. That seems way too ambitious, adventurous and maybe overreaching. We can hardly wait!