Machine Learning and where is it used?
Lately, there has been a lot of discussion about the direction in which artificial intelligence is going and how much good and bad it brings to us as humans. It is questionable if machine learning will, eventually, reach a stage where human minds might become obsolete.
But, there is one thing that we can say with certainty; there has been an exponential growth regarding artificial intelligence in the last couple of years. And, one of the main aspects of said intelligence is machine learning.
Machine learning can be found in many things today:
- Online services
- Security and surveillance
- Gaming and gambling
- Payment processing
And, it is always with us. If you have ever allowed cookies on your device when visiting a website you have fed some sort of AI to engage in machine learning. For such a reason, this technology is bound to improve exponentially in the future.
What is Machine Learning?
Machine Learning is a subcategory of artificial intelligence whose goal is that the system itself. It is meant to recognize patterns, learn as much as possible from data, and do all that and provide solutions with minimal human intervention.
Like with everything else in life, there are certain steps that must be followed in order to perform a particular task. When it comes to this form of artificial intelligence, there are 5 basic steps:
- Gathering Data
- Data Preparation
- Training a Model
- Evaluating the model
- Improving the performance
And, unlike with human learning, the computer will cycle the steps tirelessly, providing sets of data for developers to know how to tweak performance or introduce new data.
Regretfully, without human input of contextualizing data and forming algorithms the AI itself won’t know what to do with the resources it has gathered.
Forms of Machine Learning?
Generally, there are 2 main forms of machine learning.
Primarily, there is supervised learning. This is a subcategory of ML where the main goal for the algorithm is to provide adequate solutions for certain data. Most types of service software use this form to improve customer experience.
Then, there is unsupervised learning, where the algorithm is provided with only data and with no solutions. Such a system doesn’t yet have direct market applications but is used to research AI and to understand how something can be developed natively.
This system functions by learning and collecting data, preferences, and interests of individual users, and thus offers services, ads, or products that are similar to what we have apparently liked before. Such automated action is saving us a lot of time and nerves, or at least is meant to.
For instance, Netflix is universally one of the most popular applications in the world for watching TV shows and movies. So if you are a user of this application, you have definitely seen a category called “recommendations”.
This category basically recommends you a variety of movies and shows that you would possibly enjoy based on what types of movies or shows you watched and liked. previously
Similarly, in the musical field ML is integrated by offering us new choices based on the genre we listened to before. With that information, it can recommend new songs and even entire playlists and mixes which we would potentially adore.
Another thing that we are all familiar with are games. Whether you are 7 or 77 years old, games will never go out of style.
Online gaming has been on such a rise lately and is one of the top activities in free time. So let’s say you played wolf’s treasure for a really long time. The machine learning algorithm will pick up on this and will recommend games of similar feature sets and volatility levels.
How is ML Present in Our Everyday Life?
There is no denying that artificial intelligence is basically everywhere and is one of the biggest innovations that made our lives better, happier, and more productive. Using machine learning is very popular in custom software development, as well as in a wide range of service software.
It is almost impossible to divide our smartphones from machine learning as it seems to be ever-present. We can find traces of the system from simple features like alarms and messaging, to very complex apps like navigation and entertainment recommendations.
Additionally, AI is also integral to both device and cybersecurity as well as for the use of AI assistants. Without the software knowing who we are and how we sound most won’t be able to unlock our phones, let alone tell Siri to find us a burger joint.
With that being said, the chance that we use it in almost every aspect of our daily life without even realizing it is undeniable.