Neural Networks in Automated Trend Detection in Large Datasets

Neural Networks have become ubiquitous now. Whether it be convolutional neural networks, recurrent neural networks. Its applications to medicine, transportation, visualization or anything is manyfold.

While the latest state of art methods have reached an accuracy percentage of around 95%, which is great, there still are a lot of questions that need to be answered.

It is one thing to incorporate neural networks into organizations and another thing that we actually make use of it. While fancy ML models are great, how useful are they to the context of the organization?

Some of the unsolved problems in neural networks that remain untackled:

1. What is the right parameter or application that will minimize human in the loop?
2. Is it possible that we can achieve super-human detection capabilities?
3. What are the challenges that we can anticipate in the next 5–10 years?

While these questions are great to answer, there underlies an important aspect of learning. Are the problems formed correctly? Do you even neural networks in the first place? Are there good solid data science workflows that we can generate for scalable dataset ingestion? Is good labeled data available to us? How important is data quality?

While these questions help us determine ways to identify the usefulness of ML in organizations, we still need methods that can help us determine the usefulness of data.