Artificial Intelligence Explained – Discover the Driving Force Behind Video AI Analytics

Artificial Intelligence. You hear about it everywhere these days. We write about it a lot, too. After all, it’s what drives the Vaidio platform. If you ask any person what Artificial Intelligence is or where it is used, you will get reasonable answers from many. But the question of how this technology works is a lot harder. In this article, we zoom in on that.

What is Artificial Intelligence?

In essence, artificial intelligence or Artificial Intelligence (AI) is an umbrella term for algorithms and methodologies that perform tasks previously considered exclusively human. AI systems exhibit intelligent behavior by analyzing their environment and, with a degree of autonomy, taking actions to achieve specific objectives. This is not merely computational power, but the ability to learn and make decisions independently. This learning ability is characteristic of AI. In doing so, AI uses rules formulated by humans or compiled by the algorithm itself based on available data. In the process, it trains itself with this data.

The different layers of AI

AI is an incredibly broad concept. It can be divided into several layers, each with specific characteristics and applications. Briefly, the main layers are AI, Machine Learning, Deep Learning and Convolutional Neural Networks.

Artificial Intelligence (AI)

AI is the umbrella term for systems that mimic human intelligence. It encompasses a wide range of techniques and applications, including:

Rule-based systems: These systems follow predefined rules to make decisions.

Expert systems: These use expert knowledge and rules to solve specific problems.

Natural Language Processing (NLP): This includes techniques for understanding and generating human language.

Machine Learning

Machine Learning (ML) is a subfield of AI that focuses on developing algorithms that enable systems to learn and improve based on experience. Instead of explicitly programming how to perform a task, a Machine Learning model learns patterns and rules from data. Important aspects of ML include:

Supervised Learning
The model learns from labeleddatathat are human-initiated. Examples include classification (e.g., spam detection in your mailbox) and regression (e.g., prediction of house prices).

Unsupervised Learning
The model learns from unlabeled data. Examples include clustering (e.g., customer segmentation) and association (e.g., shopping basket analysis). Unsupervised Learning, when creating a model, tries to find underlying structures or patterns that were not indicated beforehand when entering the data or that were not known beforehand. This makes it well suited for exploratory data analysis or when labeled data are scarce or absent.

Reinforcement Learning
Reinforcement learning is a form of Machine Learning in which an algorithm or computer system learns how to act in a given environment to achieve a specific goal. This learning process takes place by performing actions and receiving feedback in the form of rewards or punishments. What makes Reinforcement Learning unique is that the learning process is similar to the way a human or animal learns through trial and error and receiving rewards.

Reinforcement Learning is used, for example, to allow robots to autonomously navigate an environment. A robot can learn to avoid obstacles and navigate to a goal by receiving rewards for each correct movement toward the goal and penalties for collisions or inefficient routes.

Deep Learning

Deep Learning is a specialized form of Machine Learning that uses neural networks with many layers (also called deep neural networks). It is particularly effective for analyzing large amounts of unstructured data such as images, audio and text. Deep Learning algorithms are based on the structure of the biological brain. These networks consist of multiple layers, with each layer learning new properties from the presented data. Take the example of recognizing a cat: by exposing the algorithm to numerous examples, it learns to identify characteristic features (such as fur, paws, eyes and ears).

These algorithms have the capacity to train themselves and continuously improve. Examples of where Deep Learning is being used include self-driving cars and real-time speech translation. Features of Deep Learning are:

Neural Networks: These networks are inspired by the structure and function of the human brain. They consist of neurons (nodes) organized into layers.

Feature Learning: Deep Learning models can automatically extract relevant characteristics (features) from the data without manual intervention.

End-to-End Learning: From input to output, the model can learn complex relationships and make direct predictions.

Deep Learning is at the core of advanced Video AI Analytics, using deep neural networks to detect and recognize objects, people and activities in video footage.

Convolutional Neural Networks (CNNs)

Convolutional Neural Networks (CNNs) are a class of deep neural networks that are particularly effective in analyzing visual images. Inspired by the biological processes that take place in the human eye, these algorithms are widely used in applications where image recognition plays a central role. CNNs play a crucial role in enabling technological advances in various sectors.

Key components of CNNs are:

Convolutional Layers: These layers apply convolution operations to the input data to detect local patterns. They use filters (kernels) that slide through the image to identify features such as edges, textures and objects.

Pooling Layers: These layers reduce the spatial dimension of the data, which helps reduce computational capacity and reduce model overfitting.

Fully Connected Layers: At the end of a CNN, there are usually fully connected layers that perform the final classification or regression.

In the field of Video AI Analytics, Convolutional Neural Networks (CNNs) are deployed for tasks such as classifying images, detecting objects and even interpreting complex scenes. Due to their ability to learn hierarchical patterns in data, CNNs are able to distinguish between diverse visual objects with a high degree of accuracy.

Complex and comprehensive

As is becoming clear, AI is a complex and extensive technology. Far too extensive to explain in one article. Still, we hope the above explanation will shed some light on this beautiful technology that we work with on a daily basis. Want to know more about what AI can do within Video AI Analytics? Read more about it in our articles or visit our resources.