Machine learning (ML) applications are growing exponentially alongside the growth of artificial intelligence (AI) every day — but are you growing with them?
As technology and culture advance, the buzzwords normally associated with high-tech engineers and developers are becoming more commonplace in our daily routines. In the world of artificial intelligence, or AI, machine learning (ML) is one example that many of us have come to know and accept.
What exactly is machine learning, and how is it shaping our digital lives and habits? Which machine learning applications are on the rise, and how do programmers plan to develop even more advanced examples?
In this article, we’ll take a firsthand look at how this branch of AI technology came to be. You’ll also discover what to expect as you use and interact with machine learning applications more frequently.
What is machine learning?
Basically, this means that if an application is constructed using machine learning, it learns by adaptation, rather than by being specifically programmed to behave a certain way. These predictive and adaptive features of machine learning make it a great tool for technologies of convenience, security, and automation.
Machine learning algorithms can be designed in one of four ways, including:
Supervised learning - Data engineers supply the input and output in a closer, more hands-on approach to programming
Unsupervised learning - Algorithms scan sets of data on their own in order to look for patterns and major connection points
Semi-supervised learning- A combination of the two learning types in which data scientists input some data while allowing the machine to learn freely
Reinforcement learning - Using repeated learning opportunities to help a machine understand set rules in a given construct
If you want to dive deeper into what machine learning is before continuing on, start here.
What is a machine learning application?
A machine learning application, on the other hand, is any specific software or system that operates using machine learning technology. Machine learning tools can be physical products or software, but they are all reliant on artificial intelligence technology, and in some cases, neural networks.
In many examples and use cases, developers build machine learning applications and tools into other kinds of commonly used software. This is true in the case of social media platforms, in which certain aspects of the platform are engineered to work in conjunction with machine learning algorithms.
ML applications can be simple or complex, and they can suit a wide variety of industries, use cases, and basic human needs. These applications are becoming more commonplace, especially as the technology becomes more widely accessible.
Creating a machine learning system
Most machine learning systems are built around AI models. These models help machine learning developers identify specific problems, gather relevant data, and evaluate how artificial intelligence can successfully fill in the gaps.
The steps to creating a workable machine learning application are also heavily reliant upon the four methods mentioned above. Supervised, unsupervised, semi-supervised, and reinforcement learning methods each have their own unique processes that data scientists must match to the corresponding need.
Machine learning application examples
From social media to traffic stops to business growth, examples of machine learning applications are highly visible. All it takes is careful attention and basic knowledge of AI, and you’ll quickly be able to spot machine learning tools in the wild.
To realize how prevalent machine learning applications are to everyday tasks, we’ve compiled a list of some of the most common use cases.
Machine learning models learn by adaptation, rather than by being specifically programmed to behave a certain way.
Social media suggestions
When you use a social media platform such as Facebook or Instagram, it’s likely that you’ll start to receive curated suggestions based on previous browsing activity. These recommendations aren’t an accident.
Under the surface of your swipes and clicks, social media algorithms — based on, you guessed it, machine learning — are recording data about the content that interests you most and providing corresponding suggestions in response. This is the essence of machine learning: receiving data and learning from it without human supervision.
Real-time traffic data
In addition to self-driving cars (another advancement of artificial technology), real-time traffic and travel notifications are part of the machine learning ecosystem. Traffic apps, such as Google Maps, use historical data and corresponding user data to learn about current conditions. Then, navigation apps are programmed to deliver real-time alerts to drivers traveling the same route.
In addition to providing automated feedback, traffic and navigation apps can also learn about your typical routes or places that appear close to home and work. Users then receive relevant suggestions based on location data that the app collects over time.
Customer relationship management (CRM) tools
From a business perspective, machine learning applications have a place in CRM tools and platforms. Since this software relies on the accessibility of up-to-date customer data, machine learning can easily read and interpret that data to help businesses achieve higher ROI.
Machine learning provides better data to customer-facing teams so that sellers and other specialists are more equipped to handle future customer interactions. This takes place through:
Identification of errors, pain points, behaviors, and major objections
Ability to spot major trends in customer data to help direct resources appropriately
Insights into qualitative data that can result in more accurate decisions
Cybersecurity threat detection
Machine learning tools are also highly beneficial when it comes to managing a growing number of digital security concerns. This protection can include automatic fraud detection services (especially in the case of identity theft) and malware detection and removal.
Machine learning applications gather identifying markers about cybersecurity threats and alert users to their presence before they become larger issues.
These ML advancements are particularly helpful for businesses that operate fully online or within cloud-based environments. More simply, ML tools help average users filter harmful messages out of their inboxes and into spam folders.
Voice and image recognition
One of the most common uses for machine learning applications is image and voice recognition. These tools are all around us, from automatic picture tagging on social media to virtual assistants that use voice recognition to perform basic commands.
As the machine learning algorithms process more visual cues, verbal comments, and other sensory inputs, they become more adept at helping the user accomplish a basic goal or task.
The future of machine learning
Machine learning has been around and accessible for decades, but it’s recently become increasingly prominent in our everyday lives. On a global scale, machine learning applications are predicted to grow to a value of over $100 billion dollars. Further investments are likely to include deep learning tools and additional predictive analytics.
For average users, this exponential growth and development means that we can expect to see additional applications of ML more regularly. From work to home to health, machine learning is bound to be a prominent force of the future.
From a business or government perspective, watch to see how major corporations, organizations, or even public agencies invest in machine learning growth. As the technology becomes more accessible, it will be even more crucial to keep these applications secure, protected, and safe for use.