Expensive, Labour-Intensive, Time-Consuming: How Researchers Overcome Barriers in Machine Learning

The research and development of neural networks is flourishing thanks to recent advancements in computational power, the discovery of new algorithms, and an increase in labelled data. Before the current explosion of activity in the space, the practical applications of neural networks were limited. Much of the recent research has allowed for broad application, the heavy computational requirements for machine learning models still restrain it from truly entering the mainstream. Now, emerging algorithms are on the cusp of pushing neural networks into more conventional applications through exponentially increased efficiency. Neural networks are a prominent focal point in the current state of computer science research. They are inspired by complex human biology, which, for all but the most niche use cases, still outperforms computers on most conceivable scales.