AI Algorithms: Understanding different types and their applications

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From self-driving cars to multimodal chatbots, there’s no doubt that artificial intelligence is making rapid progress. But behind these mystifying technologies are a set of algorithms that have seen optimization and refinement for over many years. If you want to understand AI deeply, then you definitely should know about AI algorithms, which is also what we are gonna discuss in this article. So, without any further ado, let’s get started.

What Are AI Algorithms? 

The mathematical models enabling machines to learn from data are known as AI algorithms. They come in different forms, including unsupervised learning, supervised learning, and reinforcement learning (RL).

When AI algorithms learn from labeled examples, it is known as supervised learning, and unsupervised learning algorithms learn from unlabeled data. When data is annotated with predefined target values, it’s known as labeled data. The data that is not assigned any such values is known as unlabeled data. Algorithms that learn by trial and error are known as reinforcement learning. They have become very popular in robotics and game playing (like chess).

AI Algorithms

Artificial Neural Networks (ANNs): ANNs are inspired by the human brain and are used for natural language processing and image and speech recognition. You input data in the ANN algorithm, and the network sends the data through artificial neuron layers. Each neuron takes information from the previous layer. It calculates an output, which gets passed on to the next layer. Deep learning uses ANNs with multiple layers. ANN is the architecture of choice for most AI applications today. 

Support Vector Machines (SVMs): They are used for regression and classification problems. SVMs work by finding the best line or curve that separates different groups of data points. Then, these lines or curves can be used to predict which group a new data point belongs to. SVMs help you identify if an email is spam or not. They are widely used in areas such as finance, computer vision, and bioinformatics.

Decision Trees: As a type of supervised learning algorithm, decision trees are used to make predictions. They work by recursively partitioning data into subsets based on a selected feature value.

Random Forests: As an extension of decision trees, random forests improve the accuracy of predictions by combining the multiple decision trees’ decisions.

K-Means Clustering: As an unsupervised machine learning algorithm, K-Means Clustering partitions data points into K number of clusters based on their similarity. The value of K is determined using algorithms or pre-defined by the user. It is useful in areas such as document clustering and image segmentation.

Gradient Boosting: Gradient Boosting is a type of machine learning algorithm that builds a predictive model by combining several weak model results. It is used in online advertising and web search ranking.

Convolutional Neural Networks (CNNs): Inspired by the visual cortex of the human brain, CNNs can automatically learn features like edges and corners from images. CNNs are specialized networks that process grid-like data (such as pixels) and hence are used for image and video processing.

Long Short-Term Memory Networks (LSTMs): As a deep learning algorithm, LSTMs are designed to handle sequential data like speech and text. They are useful for handwriting recognition, speech recognition, and machine translation.


When you interact with any AI system, you are actually interacting with these algorithms. 

AI systems are just a set of optimized algorithms based on mathematical principles and probability and statistics that are well-established. It is not yet agreed upon at which point (an AI-based information processing system becomes intelligent and exceeds the human mind. But it’s clear that we are entering a new era with the increasing demand for automation. AI is all set to change the world.

If you want to build an AI system using any of the above or any other algorithm but are confused about how to go about doing it, then we are here to help. Contact us today to discuss your requirements.

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