In living organisms, evolution is a multi-generational process where mutations in genes are dropped and added. Well-adapted organisms survive and those less fortunate go extinct. This is Natural Selection. Resilience is great, but if you don’t grow gills in time for the flood, then tough luck.
Engineering, on the other hand, is a deliberate process with reliable steps designed to reach a stated objective. With the emergence of artificial intelligence, we are beginning to see the convergence of evolution and engineering as Machine Learning algorithms begin to evolve.
For the sake of our comparison (natural evolution to machine evolution), let’s consider data and how it is normalized as “the environment” and the training process as “Natural Selection.” The training process can be supervised or unsupervised learning, reinforcement learning, clustering, decision trees or different methods of “deep learning.”
Much like natural evolution, different organisms solve for the same problem differently depending on their environment, but ultimately reach the same outcome. Sharks and dolphins wound up with similar mechanisms to survive despite starting from completely different beginnings. In technology, we see similar patterns. The K-means clustering algorithm, a technique often used for image segmentation, for example, ingests essentially unlabeled inputs (usually images) and coherently grouped clusters are produced until a desired grouping is reached.
