Original article was published on Deep Learning on Medium
Myth Buster: Machine Learning Edition.
Metaphorically speaking, In this world of WhatsApp forwards realizing the truth is as difficult as the problem itself due to which “ Myths & Prejudices” find a place in the society. Some are apt whereas some are just random and completely bogus.
Myths are not an alienated topic when it comes to Machine Learning and Deep Learning in general. Some myths find its way to the user’s head while some people just move on with the facts.
Here in this article, I am going to jot down a few myths that I came across and how I dealt with it.
- Talking about Machine Learning the first thing that comes to mind is the word-“Data”. Related to it the first myth I heard was that Machine Learning can do anything with a huge amount of data, which is never the case.No data is accurately valuable. Every dataset is a “good” dataset if it follows the functionalities of the phenomenon on which the work is going on.No amount of data will help if it’s irrelevant to the phenomenon you are looking to analyze.
- Machine Learning models get better over time because of the new insight from the latest data. To this I can say, you can develop better ML models as you collect additional training data, but that accuracy will stagnate after a while. When your model is deployed, it gets frozen with time and will degrade as the situation changes.
- This is the perspective I initially had when people used to talk about, how much superior Machine Learning is as a concept. Machine Learning is all about computers being taught to make decisions and give insights like humans. Personally I feel ML models are better than humans in analyzing data and finding certain patterns and outliers. Whereas, ML model struggle at situations where there is sparse data or limited data, which humans are capable enough to cope with.
- Another common myth I came across was, ML will take over humans and kill their jobs. Well busting this myth is actually funny because to make a large scale model a good number of skilled personnel are required. Humans will spend more time on decision making jobs rather than repetitive tasks that ML can take care of. The job market will see a significant reduction in repetitive job roles but the wave of ML, AI will create a new sector of jobs to handle the data, train it, and derive outcomes based on the ML systems.
- There’s No Difference Between Artificial Intelligence And Machine Learning. Most often we use machine learning and artificial intelligence terms interchangeably. However, both are not the same and not synonymous with each other. Robotics, computer vision, and natural language processes are areas under the artificial intelligence stream. Machine learning is learning about patterns, using statistics and data predictions.
There are so many articles on the internet that discusses the myths around ML, but this article solely focuses on my interpretation regarding the myths.
Do, share in the comments about the myths you came across and your perspective regarding it.