Machine learning limitations marked by data demands Posted on : Aug 20 - 2020

Machine learning has impressive capabilities in the enterprise, but with high-data requirements and struggles with explainability, it remains unable to reach widespread use.

While machine learning has a variety of use cases and the capability of deep analysis it is not without limitations.

With large data requirements coupled with challenges in transparency and explainability, getting the most out of machine learning can be difficult for organizations to achieve. Understanding these realities of machine learning and establishing realistic expectations is the only way to put your organization in a stronger position.

High potential but high data demands

Data is the core of machine learning. The very nature of machine learning is to train an algorithm on clean and prepared sample data. Through this repeated process, it can learn from the data set and create and apply generalizations to data it has never seen before.

One of the more impressive feats of machine learning to date is represented by the remarkable performance and capability of OpenAI's GPT-3 model, which can generate surprisingly humanlike text output from just a small amount of starter text. While the results are noteworthy, the reality is that petabytes of data, millions of dollars of CPU and GPU power and many hours of training time went into creating the resulting model.

This quantity of data and computing is not available to the average machine learning model developer and highlights one of the major challenges with the current state of the art for machine learning: an extreme dependency on data.

All forms of machine learning are heavily dependent on data, from supervised and unsupervised learning to even reinforcement learning that can teach itself across multiple iterations. But to accomplish many of the tasks of machine learning with acceptable levels of accuracy it requires copious amounts of data to train machine learning algorithms, especially the currently favored deep learning neural networks.

Deep learning neural networks consist of many layers of individual artificial neurons that are connected to subsequent layers with many connections per neuron. Each of these connections consist of weights and biases that must be determined by the machine learning algorithm over multiple iterations of training for the overall network to be adequately trained. The more layers, the more weights and biases that need to be calculated. These calculations take a significant amount of computing power, as well as data and time. View More