
We hear this all the time: a new analytics platform or study that uses machine learning to analyze crypto-assets. However, when we dig a bit deeper, instead of cutting edge machine learning we find simple statistics or basic algebra glorified as a sophisticated analysis.
You would think that the use of machine learning methods in crypto-assets would be ubiquitous. After all, cryptocurrencies operate in a largely digital environment governed by public ledgers and we are living in the golden era of machine learning and artificial intelligence(AI). However, the use cases for applying machine learning in cryptocurrencies remain a novelty. At IntoTheBlock, we spent a lot of time applying different machine learning methods to specific problems in the crypto space.
Today, I would like to explore some practical use cases in which machine learning can deliver tangible value in the crypto-asset space.
Machine Learning vs. Statistics
Labeling something as machine learning or AI is sexy these days and the crypto analytics market is not exempt of that hype. In the case of the crypto-asset market, most of the analytics we’ve seen are relatively simple statistics glorified as machine learning techniques for marketing purposes. No harm on that; after all, the line between statistics and machine learning is blurry in some areas and some experts even refer to machine learning as “glorified statistics”.
To understand what methods can be classified as machine learning and which ones shouldn’t, it is key to make the distinction between machine learning and statistical techniques.
There are plenty of elements that can be cited to explain the differences between machine learning and statistics. Conceptually, both disciplines are focused on the analysis, interpretation and organization of patterns in datasets. However, statistics achieves its goal by deriving inferences in the form of mathematical equations while machine learning creates models that have the ability to learn beyond the programmed code. In a nutshell:
Machine Learning is an algorithm that can learn from data without relying on rules-based programming.Statistical modeling is a formalization of relationships between variables in the data in the form of mathematical equations.
We can also think about it these way: “Statistics draws population inferences from a sample, and machine learning finds generalizable predictive patterns.”
In the context of crypto-assets there are plenty of opportunities for applying both statistics and machine learning techniques. However, the use cases for the latter might not be that obvious at first glance. Let’s explore some of the most common scenarios for applying machine learning methods to crypto-assets.
