Machine Learning: What is It and what are Its Features?

Although the computing capacity of modern workstations allows processing enormous amounts of data, there has never been a solid technical solution for full utilization of those capabilities — until recently. The technology is called machine learning (ML), and it’s a key component in WorkFusion products. Today we’ll explore the essentials of what it is and how it works.

As machine learning is often discussed with AI, let’s draw a line between the two terms.

Artificial Intelligence and Machine Learning

Many people mix the concepts of AI and ML, as if they were synonyms, but this is not correct.

In a nutshell, artificial intelligence, or AI, is an attempt to make machines emulate human thought processes, particularly reasoning, learning, and self-correction. The scope of AI includes a multitude of applications, from speech recognition to natural language processing and computer vision.

Machine learning (ML) is a subset of AI that studies algorithms and models used by machines so they can perform certain tasks without explicit instructions and can improve performance through experience. ML has been one of the fundamental fields of AI study since its inception.

How machine learning works

There are dozens of ways to explain how machine learning works (if you want to go deep), but there are always three pillars to lean on:

•  Data
•  Parameters
•  Algorithms

Mining data

You can’t enable machine learning without a certain minimum amount of data. The more data harvested and ingested, the more “educated” software can become. The gathered data is accumulated into datasets, before AI starts working through them, with the process backed by specialized machine learning algorithms. (We’ll get back to those a bit later.)

There are two methods to gather data—manually or automatically. The former requires more effort and more time (and money), but it pays off with precision in the end result. The latter requires less data screening, but there’s a risk of including too much irrelevant information.

Working with parameters

Classifying datasets is a crucial element of the whole process. Software needs parameters (which can be both pre-defined manually or created as you go) and will use them as boxes to put the data into order.

Again, precision is the key. It’s best to reach as much clarity as possible early in the process to ease the way for your system.

Adjusting algorithms

Algorithms in machine learning define the way software processes incoming datasets. A diligently fine-tuned system assures more accurate results, leading to success—whether it’s better stock value predictions, boosted online sales, or more effective usage of telemetry in racing.

The flip side of the coin is no matter how good a machine learning algorithm is, it can’t go far with poorly organized, irrelevant data.

Supervised vs. Unsupervised ML

There are two types of processing for datasets.

Supervised ML uses structured data and has a certain calculation result that software must reach. Each time an end result differs from the expected value, the machine adjusts formulas until it reaches the target numbers. For example, this method is used heavily in weather forecasting, where AI learns to predict weather or climate changes based on historical data.

Unsupervised ML mostly leaves AI on its own to process fully unstructured information. Though it requires considerably more time, this is the go-to method to work with when there is little or no initial data. Typical applications are in e-commerce, to predict user behavior, or suggest other goods related to recent purchases by similar customers.

Machine Learning vs Deep Learning

As with AI, machine learning vs. deep learning is a faulty comparison, as the latter is an integral part of the former. Deep learning methods are based on artificial neural networks that are inspired by the structure and functions of the brain. They include deep neural networks, convolutional neural networks, recurrent neural networks and other architectures. Deep learning performs multiple stages of complex data analysis before delivering end results, and can extract higher-level features from the raw data.

The Future of Machine Learning

Machine learning is definitely one of the hottest trends in tech right now, and the hype is not likely to decrease anytime soon. There are plenty of discussions about the future of machine learning and other AI disciplines, but there are a few opinions most experts agree on:

Machine learning will become ubiquitous: With the development and availability of open-source ML frameworks, there will be more opportunities to integrate machine learning capabilities in a wide range of applications.

More business applications of ML: Machine learning will be widely used in enterprise software and in conjunction with automation to provide better decision-making. Models will become more flexible and general, and thus applicable to more tasks.

Low-code and no-code ML: In the future, machine learning is likely to become more approachable to wider audiences, without a need for people to have training in data science or coding.

Unsupervised ML: More emphasis will be put on unsupervised ML algorithms and using neural networks.

How WorkFusion Uses Machine Learning

Machine learning is an essential element of process automation, laying the foundation for WorkFusion’s Intelligent Automation Cloud. While most business process automation products currently on the market either require data science or provide simple out-of-the-box solutions, our tools combine the two approaches: providing immersive experiences for both tech experts and those new to automation.

Regarding machine learning, these are just some basics. More fascinating topics to explore include regression vs. classification, clustering vs. pattern search, recurrent vs. convolutional neural networks, and more. The deeper you go into studying machine learning, the more there is to digest, and we’ll be covering more of these in articles to come.

Stay tuned!