Machine learning and the future of business
Machine learning is one of the driving forces behind the radical transformation of all industries and business operations. This blog explores how the rapid and unprecedented growth of machine learning is setting the stage for the future of business. A subset of artificial intelligence, machine learning is one of the driving forces behind what many people have dubbed the fourth industrial revolution, or Industry 4.0, and it’s changing the world.
What exactly is machine learning?
You’ve no doubt heard the term before, likely used interchangeably with artificial intelligence. However, AI is simply an umbrella term that refers to any software (or algorithm) that enables a machine to mimic human behavior. AI encompasses a wide range of different technologies, including machine learning, deep learning, data science, and natural language processing.
Machine learning specifically refers to software that allows a machine to automatically learn from past data without manual programming. By contrast, having people learn from past data has become a practical impossibility in many use cases, simply because data sets have grown too large for human comprehension.
Because machine learning works at machine speed, it can analyze and parse vast amounts of data in short order. In doing so, it can determine trends and, through continuous learning, recognize and understand new types of data that it wasn’t explicitly programmed to.
What is an example of machine learning?
There are many possible use cases for machine learning. Perhaps one of the simplest, as well as one most of us have first-hand experience with, is the predictive text on our smartphones. As you use your device, the software learns from your input and provides suggestions tailored to your unique way of writing. Speech recognition works in much the same way to the point it has now become the new standard for hands-free operation and home entertainment.
Another example is autonomous vehicles. Consider, for example, what a self-driving car would do if it encountered an object on the road that it had never seen before. Instead of the whole system just breaking down, it will make an educated guess as to what the object is and take appropriate action. This is due to its image-recognition algorithm, which has been trained using vast amounts of data so that it can recognize almost any object on the road.
The business use cases for machine learning are extremely broad. For example, the finance sector now relies heavily on algorithmic trading to analyze vast data sets and identify real-time investment opportunities. Similarly, every industry sector can benefit from predictive analytics, which can do everything from flagging potentially fraudulent transactions to automating pricing based on past trends in supply and demand.
One sector that has been particularly affected by the rise of machine learning is manufacturing, hence the term Industry 4.0. Industrial robotics no longer have to be restricted by their original programming. Instead, machine learning can continuously optimize industrial processes with predictive maintenance. These systems use live data to analyze failure patterns in real time and adjust machine settings to proactively safeguard them against breakdowns.
Machine learning is ultimately all about automating decision-making based on previous data, albeit at a speed that is far beyond manual capabilities. Businesses across all industry sectors are now turning to robotic process automation (RPA) to automate entire workflows. If there’s a workload that can be automated, then the sage advice holds that it should be to mitigate the risk of human error and boost efficiency. That being said, machine learning goes a step further by tackling myriad new scenarios without requiring human intervention in most cases.
Big data – the fuel that powers machine learning
Big data refers to data sets that are too large for human comprehension. There are now more bytes of data in the world than there are stars in the observable universe. According to Statista, in 2021 alone, the world will generate 74 zettabytes of data; the equivalent of several billion high-capacity hard drives.
The average enterprise has around 400 data sources, such as social media, cloud computing, and email, and manages over one petabyte of data in total. With the amount of data doubling every couple of years, big data is entrenched in today’s business world. The size of these data sets is the driving force behind machine learning.
In many ways, machine learning was born out of necessity. Without it, these data sets would be practically useless. However, with the power of automation and insight offered by machine learning, that data can add enormous business value.
On the other hand, machine learning also uses data to learn, hence the machine learning life cycle. The concept is that algorithms analyze data to reveal insights and automate decision-making but, as they do so, they learn from that data and get better every time. The only time when human intervention is necessary is when the model detects a so-called edge case that it fails to understand.
Machine learning and the future of business
It’s often said that technologies like machine learning will take our jobs. The reality, however, is rather more complex. While any kind of technological advancement has always rendered certain jobs obsolete, machine learning and artificial intelligence will actually create more jobs than they destroy. In fact, several rapidly emerging fields, like data science and data labelling, are currently in enormous demand and suffering from a shortage of experts in their domains. This is why it often makes sense for businesses looking for professionals in these spaces to seek out freelance contractors, rather than pay huge salaries for full-time in-house employees.
Another important thing to remember about machine learning is that people play a critical role in its entire lifecycle. After all, a machine is only ever going to be as effective as the people tasked with training it. People also need to step in on occasion to make corrections or changes, such as when the model exhibits a low confidence factor. This approach is known as humans in the loop (HITL), and it’s what unleashes the true value of machine learning in practically all use cases.
Whether it’s predictive analytics for sales teams or climate modelling or self-driving vehicles, machine learning represents an exciting future where people and machines work together to augment one another’s capabilities.
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