Why use Machine Learning when traditional programming has solved so many business problems? We will discuss how it helps and why machine learning is thriving below:
There are specific use cases like the spam filter, where doing traditional programming is hard. Also, the real use of machine learning, that is, cognitive problems, such as image recognition, speech processing, Natural Language Processing (NLP), and so on. These tasks are extremely data-driven and complex, and solving them using rules would be a nightmare. So, an increase in complexity and data-driven problems are the key areas where machine learning can thrive. For example, we have NLP models that can write entire movie scripts, image processing models that can colorize old black and white images, and so on.
Another driving factor of machine learning is the boom in data. The generation of data is exponential. As per an estimate by Statista, around 41 zettabytes of data are created in 2019 itself. To put that into perspective, if you watch a full HD movie (1024p, 2 hours approx.) with Netflix, it takes about 8GB of data. So, in 2018, the data created is equivalent to ~5000 billion such movies. That’s a lot of data. And as I mentioned earlier, machine learning problems are data-driven problems. So, it helps generalize the models a lot better. Generalization in machine learning signifies how the model performs to new unseen data; that is how general the model is to perform even with various examples ridden with noise. And this also gives another key advantage for machine learning over human learning; we can’t comprehend data at this scale, even in GBs, let alone zettabytes. So, machine learning in certain use cases of big data helps humans learn or infer. For example, machine learning can let us see hidden dependencies/correlations in seemingly unrelated data. One example I can think of is the beer and diaper correlation story/urban legend. As per the story, Wal-Mart, the world’s leading retail chain, supposedly found a correlation between beer and diaper sales on Friday evenings using their transactional data. This kind of learning of association among products from transaction data is called association rule learning/mining. The story suggests that young men take the last dash to take beers on Friday evening and their wives ask them to buy diapers for their children. According to the story, Wal-Mart exploited this association and placed two of these products together. This created a funny meme of kids holding beer bottles. Although this story is said to be fake, association rule mining is true. You can see that during Amazon’s recommendations on products bought together. And there are use cases in genetic engineering where scientists use machine learning to identify genes associated with dominant disorders. You can read more on this in the paper titled “DOMINO: Using Machine Learning to Predict Genes Associated with Dominant Disorders” by Mathieu Quinodoz et al.
Improvement and accessibility in computation is another driving factor of machine learning. We have a lot of computation power nowadays. Also, they are cheaper. We can find a beefy GPU nowadays cheap. And machine learning code has the potential for parallel processing and taking advantage of a high number of cores present in GPUs. Even you can get a shared GPU for free (even TPUs) using Google Colab. So, without having a lot of high computation infrastructure settings, you can still do machine learning.
Hope this was helpful.