Machine Learning: The New ‘Gold Rush’

Machine Learning: The New ‘Gold Rush’

“Scientia potentia est” is a Latin adage that means “knowledge is power”. This phrase is commonly attributed to Sir Francis Bacon and its most common modern interpretation is ‘information is power’. There has never been a time in human history when this phrase was more relevant, as each day humanity creates over 2 Quintillion bytes of data.

This reality has manufactured the big data boom that the world is currently experiencing. All of this data has to be processed, analyzed and stored in some way. The need for effective and transparent data management and processing techniques has paved the way for data science and its growing ecosystem. That’s why ‘data scientist’ was the hottest job opening in 2016.

But data science is only a symptom of the growing ecosystem that’s using the incredibly vast amounts of data that we generate and collect daily. Simply put – data science can’t keep up with the amounts of data that we generate and the applications that we want to utilize it for. That’s why the current pool of information technologies, including data science applications, serve as lubricant for the rapidly expanding niche of Machine learning.

Machine learning is the next evolutionary step for data management and processing, as it allows us to amplify the value of collected information. It allows us to teach machines by using data as the metaphorical ‘fuel’ for programs and applications.

This self-learning mechanism allows software to evolve and greatly amplify its own efficiency. This is one of the underlying principles of Artificial Intelligence. It has gotten to the point that some people already consider software that doesn’t include a self-teaching mechanism, to be legacy software. This is perfect for data-intensive applications, like data securityand operational predictions of various sorts.

That’s why machine learning is becoming the ‘gold rush’ of the tech world. Let’s take a look at some of the details surrounding this rush and try to get an idea of exactly why this is happening.

Machine learning has an incredible variety of applications in our data-driven world. In its current state, machine learning has a number of very promising use cases.

At the same time, capturing value from data and analytics for a variety of other applications is a problem for current standard data pipelines. This is something machine learning is perfect for – extracting value through optimized algorithms and processes. And there’s a lot of room to grow.

The labor market is even more ripe for a takeover by machine learning. Not afraid of losing your job to a ‘machine’? Think again.

There are still applications where machine learning excels better than anything else – for a variety of reasons: some niches have more data available, others have a history of machine learning applications and experiments.

Here are machine learning use cases that resonate the most with current business requirements and the availability of data, which these specific applications require:

If that’s not enough, just look at the current startup landscape for machine learning/Artificial Intelligence.

Machine learning startups are rolling in dough. And, as a result, everything else follows suit, like algorithm marketplaces that have only recently become a thing.

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