New Radical Empiricism

The new age of computing and data has unleashed a whole new approach to scientific discovery. I was lucky enough to spend a few hours at CogX18 and listen to Zavain Dar (https://twitter.com/zavaindar) at Lux Capital explain how thinkers of old can move away from the basis of ground truths towards a data driven relationship, free interpretation of how things link together. There is no need to understand the ground truths, nor the myriad of supporting hypotheses and assumptions in order to prove a concept. It is now possible to apply cognition beyond human understanding to map input X to outcome Y using machine learning / deep learning / cognitive computing.

Pick an historic scientific legend, eg Newton. Imagine the steps, process, ground truth understanding and empirical proofs required to explain gravity! Now imagine taking readings, mapping to outcomes and feeding the results into a deep learning model. With enough iterations, the core concepts and relationships will be implicitly mapped.

In medicine, for example in the field of dermatology, applied machine learning is already yielding diagnoses that are better than those obtained from human specialists. This is saving lives and reducing the amount of specialist input for critical diagnoses.

New radical empiricism is here! It’s real and can be applied to a wide range of fields. However, it is especially relevant with use cases in which ground truths are notoriously difficult to prove. Healthcare and complex system outcome prediction are perfect. This also implies that economics and investment management with their myriad of nested assumptions are also front and centre of the new radical empiricism wave.

Algospark are developing predictive analytics solutions across equity investment, retail location investment and complex systems prediction. These are front and centre for applied new radical empiricism. NRE is nascent, but a great concept and one that will receive increasing focus in coming months and years.