You will see Artificial Intelligence in search engines, maps and navigation, text editors, etc.
Artificial Intelligence appears in all industries and in all processes, whether in manufacturing, marketing, storage, or logistics. There are dozens of examples of Artificial Intelligence in the real world.
That includes technical use cases, such as human workforce automation and robotic processes, to basic applications. You will see Artificial Intelligence in search engines, maps and navigation, text editors, etc.
But have you ever thought about how these machines work?
Artificial Intelligence systems work with algorithms, but not all Artificial Intelligence algorithms are the same. By understanding how Artificial Intelligence algorithms work, you can facilitate your business processes and save hours of manual work.
Next, we analyze the types of Artificial Intelligence algorithms and how they work.
What are Artificial Intelligence algorithms?
Artificial Intelligence algorithms are instructions that allow machines to analyze data, perform tasks, and make decisions. It is a subset of machine learning that tells computers to learn and operate independently.
All the tasks performed by Artificial Intelligence work with specific algorithms. From turning on your system to browsing the Internet, Artificial Intelligence algorithms work with other machine learning algorithms to perform and complete each task.
Artificial Intelligence and machine learning algorithms allow computers to predict patterns, evaluate trends, calculate accuracy, and optimize processes.
How do Artificial Intelligence algorithms work?
Artificial Intelligence algorithms work by identifying patterns, recognizing behaviors, and empowering machines to make decisions.
Let’s say you tell your voice assistant like Alexa or Google Home to stream your favorite music.
The Artificial Intelligence algorithm on which it is based will first recognize and remember your voice, become familiar with your choice of music, and then remember and play your most played music simply by recognizing it.
Similarly, AI content editing tools work on algorithms such as natural language generation (NLG) and natural language processing (NLP) models that follow certain rules and patterns to achieve desired results.
This is not rocket science, but a simple formula: “The more you learn, the more you grow.” As you provide rich data to computer systems, algorithms use it to gain knowledge and perform tasks more efficiently.
At the most basic level, an Artificial Intelligence algorithm collects data for training and then uses it to modify its knowledge. Then, use this knowledge to complete tasks and improve accuracy.
Types of Artificial Intelligence Algorithms
Different business use cases have different algorithms and categories. For example, the algorithm used in various chatbots differs from those used in the design of autonomous vehicles.
There are three large groups of Artificial Intelligence algorithms under which hundreds of other algorithms are found: supervised, unsupervised, and reinforcement learning. The difference is how they are trained and how they operate.
Supervised learning algorithms
The first most popular form of algorithm is the supervised learning algorithm. It involves training a model with labeled data to make predictions or classify new, unseen data.
The name “supervised” means working under the supervision of training sets. It works by simply using the desired result to cross-validate against the given inputs and train it to learn over time.
This learning algorithm is created under the supervision of a team of dedicated experts and data scientists for testing and error checking.
Developers train the data to achieve maximum performance and then choose the model with the highest performance.
Supervised learning algorithms typically solve classification and regression problems. Examples within this include neural networks, decision trees, random forest linear regression, time series regression, and logistic regression.
Marketers use this Artificial Intelligence algorithm to predict sales over time, map customer sentiments, track stock prices, etc. Other use cases for supervised algorithms involve text recognition, object categorization, and spam detection.
The good thing about this algorithm is its simple process that provides high results and generates accurate information.
Unsupervised learning algorithms
Unsupervised learning uses unlabeled data to feed and train the algorithms. While supervised learning has predefined classes, unsupervised learning trains and grows by identifying patterns and forming groups within the given data set.
Simply put, supervised learning is done under human supervision, while unsupervised learning is not. The unsupervised learning algorithm uses raw data to draw patterns and identify correlations, extracting the most relevant insights.
Prominent examples of unsupervised learning include dimension reduction and clustering, which aim to create defined groups of objects.
Clustering and data reduction have broader applications in biology, chemistry, and data mining. Unsupervised learning finds application in genetics and DNA, anomaly detection, imaging, and feature extraction in medicine.
In marketing and business, unsupervised learning is used for customer segmentation: learning about customer groups and their behavior.
Even Google uses unsupervised learning to categorize and display personalized news to readers. First of all, it collects millions of news on various topics.
The search engine then uses cluster analysis to set parameters and categorize them based on frequency, types, sentences, and word count.
Generative Artificial Intelligence draws patterns and structures by using neural network patterns. However, you can use more than this approach alone.
It leverages different learning models (i.e., unsupervised and semi-supervised learning) to train and convert unstructured data into basic models.
The best part is that you don’t need any tagged data, which in turn is cheaper.
Reinforcement learning
Reinforcement learning works the same way we humans do. The algorithm trains and learns from the environment and receives feedback in the form of rewards or penalties to eventually adjust its actions based on the feedback.
Reinforcement learning is a continuous cycle of feedback and actions that take place. A digital agent is placed in an environment to learn and receives feedback as a reward or penalty.
Throughout the process, the agent tries to decide and achieve the desired result, which is the basis of feedback. If feedback is received as a reward, the agent repeats and uses the same tactics in the next cycle, improving its behavior.
Reinforcement learning is a widely used algorithm that finds applications in marketing, healthcare, gaming systems, traffic control, and image processing.
