Almost every industry existing today uses artificial intelligence (AI) in some form. However, AI seems like a formidable subject that only technical and science-minded persons can understand. The development of AI is in that particular realm, but the developers and innovators created tools and devices that use AI to operate efficiently.
On the other hand, it is essential to know that machine learning is part of the more expansive domain of artificial intelligence. You may not realize that you are already using the system.
If you have encountered chatbots on some websites or use predictive text on your smartphones, those are already examples of machine learning. Machines that can diagnose medical conditions after seeing images and the power behind self-driving cars is machine learning.
AI programs that companies use today use machine learning, which provides computers with the ability to learn in a different training method without using computer codes. Companies such as Dataloop.ai are developing and supplying tools and services for machine learning, such as annotation platforms, and systems for data management, etc.
Machine learning is becoming ubiquitous.
It is easy to interchange artificial intelligence and machine learning because the two are from the same program. In reality, machine learning started in 1952, but it became popular around the mid-1980s. But it started booming as a separate field around the 1990s.
Today, more and more companies are integrating machine learning into their systems, according to the Deloitte survey in 2020. The survey showed that 67 percent of companies today are already deploying machine learning, and 97 percent are planning to implement it in 2021.
Understanding machine learning
Machine learning is the capability of a machine to mimic the intelligent behavior of humans, allowing the system to do complex tasks similar to what humans do when solving problems.
The goal of artificial intelligence is to develop computer models that manifest intelligent behaviors similar to humans. For example, the model can understand text written in natural human language, recognize a visual scene, or execute an action in the real world with machine learning.
There is a difference in the way people teach machines to learn. In the traditional method, a developer needs to write a program that the computer will follow. The process involves a lengthy process, depending on the complexity of the program.
In machine learning, a computer model is trained to recognize objects without a developer writing a code for the process. It is similar to teaching a young child to recognize colors, shapes, sounds, and things. Instead, the computer learns to program itself through various experiences, which can be more specific depending on the project’s needs.
Process of teaching a machine
Instead of using computer programming languages such as JavaScript, Python, Ruby, or HTML, starting the machine learning process begins with data—the more extensive the datasets are, the better, as they will provide the computer with a broader range of experiences.
Suppose the project is to train a machine model for autonomous driving. The project will require tons of data. The machine model needs to learn everything about road conditions, traffic conditions, pedestrian behaviors, traffic signs, lanes, moving and static objects, driving in different weather conditions, and more.
Each category will have a compilation of datasets. All the data must be annotated or labeled to ensure that the machine can recognize them in a real-world setting.
After preparing the data, the programmers will choose the machine learning model, provide that data and allow the computer model to learn by itself to look for patterns or make predictions. Then, human programmers can tweak the model or change the parameters to make the results more accurate.
Not all datasets are given to the machine learning model. Some are withheld to evaluate the machine’s knowledge and accuracy by how it responds to the introduction of new data. The evaluation results can then be used for future projects using different datasets.
Functions and subcategories
The machine learning system is developed to have three primary functions: descriptive, predictive, and prescriptive. The descriptive function uses the data to interpret what happened. Predicting what will happen is the purpose of the predictive process, while the prescriptive function uses the data to provide suggestions regarding what action to take in a given situation.
Aside from the different functions, machine learning has subcategories, too.
The supervised machine learning (ML) model is trained using labeled or annotated datasets. It is the most common method, where the ML model learns and improves its accuracy over time.
The training uses unlabeled data sets in the unsupervised ML model, allowing the machine to look for trends or patterns by itself. For example, it can review online sales data and determine the different types of buying customers.
The last category is the reinforcement ML model. In this category, the machine learns through trial and error, where the device must decide on the best action to take using a reward system. Over time, the machine will learn to make the right decisions.
Business applications of machine learning
There is a long list of business and industry applications of machine learning. Many of them are already being implemented, and people are benefiting from them. If you notice, search engines usually offer suggestions in the same manner that Netflix or YouTube can suggest other movies or videos for you to watch because you started watching a film in a particular genre.
In addition, social media sites use recommendation engines to determine what feeds to send you, what tweets to show, and the ads to display when you’re using Facebook (Meta).
Machine learning is behind programs that do object detection and image analysis, such as the program that can analyze the number of cars in parking lots. In addition, detection of spam emails, unauthorized login attempts, and identifying fraudulent transactions via credit cards are made possible by machine learning.
Other companies use models or algorithms to deploy chatbots and automated helplines to enhance customer service. Machine learning technology is integral in the development of self-driving cars and medical diagnostics and imaging.
Knowing how to develop machine learning models can help increase your appreciation of the system’s usefulness and why it is now one of the essential business tools today.
