Artificial Intelligence (AI) is a field of computer science that deals with making computers think like humans. It has been around for decades, but recently there has been a surge in interest in this area.
What Is Artificial Intelligence?
There are two main ways that AI is being applied today. One is machine learning, where machines learn from data without human intervention. The other is natural language processing, where machines understand what people say and use that knowledge to make decisions.
Why Do We Need AI?
Machine learning has been around since the 1950s, but only recently has it become widely available. It allows computers to learn by analyzing large amounts of data and making predictions based on those analyses. This process is called “machine learning” because it mimics how humans learn.
The Basics Of AI
Machine learning is one of the most powerful tools at our disposal today. It can help us make better decisions, predict future trends, and even automate tasks. There are two main ways machine learning is applied to websites:
1) Automated Recommendations – If you’re looking for something to buy online, chances are you’ve seen recommendations pop up on your screen before. These recommendations are generated using algorithms that analyze what people who bought similar products purchased before.
2) Personalized Content – We use machine learning to personalize content on our sites. For example, when you visit a site, the web server might recognize your browser and automatically serve you a different version of the site than someone else would see.
Using AI For Website Design
As mentioned above, there are two main ways machine intelligence is being used to enhance websites: automated recommendations and personalized content. Let’s take a closer look at each of these methods.
Automated Recommendations
The first method uses machine learning to generate recommendations based on past purchases. This recommendation system has been around since the early days of e-commerce. Amazon was one of the first companies to implement such a system, and it continues to be one of the most popular.
To build a recommendation engine, you must collect data about previous customers. You can ask them directly (via surveys) or gather the indirectly through cookies or other tracking mechanisms. Once you have enough data, you can start building a model that predicts whether a customer will purchase a product based on their previous behavior.
There are several different types of recommendation engines, but the most common ones use collaborative filtering algorithms. These algorithms compare what people who bought similar products purchased before. They then make predictions about what new products might interest those people.
Personalized Content
The second method uses machine learning to personalize content. This means that instead of presenting users with generic content, the site presents them with content tailored specifically to their interests.
This approach is more complex than the first because it requires much more data collection. It also requires a lot more human intervention. Instead of simply predicting what people will buy based on their past behavior, the system needs to understand why they bought something in the first place.
Take a look at this company, Coveo to see an example of personalized content: