Writing this article presented a challenge due to the widespread use of the fourth Industrial Revolution as a catchphrase by politicians and technocrats as it might be taken as a criticism.
However, it is crucial for us to understand the challenges that may hinder the 4th Industrial Revolution from offering solutions to specific issues in Namibia.
In contrast to the well-understood first, second, and third industrial revolutions, the 4th is characterised by autonomy. It enables software programmes to independently learn and execute tasks through neural network models. Examples of these autonomous software programmes include facial recognition software predicting a person’s age and a self-driving car adept at identifying objects and making informed decisions, mirroring human capabilities.
My concern, however, lies in the lack of practicality in our approach. The fourth industrial revolution demands substantial electronic data to train software models.
The critical question therefore arises: Are we collecting sufficient electronic data to feed the neural network models that we may conceive? The resounding answer is a glaring NO.
IT systems are absent in the public space, those that exists work in silos: no school management system to give visibility into important factors such as enrollment, progression, identify talented teachers, identify poor learners performance due to socioeconomic challenges, etc. no comprehensive healthcare system, disjointed birth certificate and national ID systems to allow Namibian ID numbers (not ID cards) to be issued at birth, an important aspects of uniquely identifying citizens which is a key requirement for e-governance.
Additionally, there’s a lack of real-time systems to track unemployment and employment or predict potential gender-based violence incidents based on reported cases over time.
Without the IT systems to collect essential data for neural network programmes, our talk of the fourth industrial revolution as a solution in Namibia remains a mere rhetoric.
The solution therefore lies in returning to basics by developing fundamental IT systems, and breaking silos to establish a singular source of truth, eliminating redundant data capturing processes.
It’s not a complex feat, like constructing an advanced neural network model–let’s embrace simplicity and practicality.
*Nabot Uushona holds a Bachelor of Science in Physics and a Master of Science in Electronics.
