Demystifying Artificial Intelligence - Understanding the Basics Part II

How does AI work?

Now that we have explored the fundamental concepts of Artificial Intelligence (AI) in Part I, let’s delve into how an AI system actually works. At the heart of an AI system lies its remarkable ability to learn and adapt to tasks independently. This ability is made possible through a powerful technique called machine learning. Machine learning is the workhorse that drives AI systems, enabling them to learn from data and improve their performance over time. To understand the inner workings of machine learning, let’s briefly discuss its major components.

Machine learning, a captivating field within the realm of artificial intelligence (AI), has revolutionized our problem-solving capabilities and decision-making processes. By enabling computers to learn from data and make predictions without explicit programming, machine learning has opened up a world of possibilities. In this blog post, we will dive into its two main categories: supervised machine learning and unsupervised machine learning.

Supervised Learning: Learning with a Guide

Supervised learning is like having a knowledgeable guide by your side. In this approach, machines are trained using labeled data, where each data point is paired with its correct answer. Consider an example where we want to build a model to identify pictures of dogs. We would need a vast dataset of dog pictures, labeled as “dog”. By feeding this labeled data to the model, it learns to recognize patterns and is able to make predictions on new, unseen data. This approach is widely used in various domains, such as image recognition, speech recognition, natural language processing, and fraud detection.

Unsupervised Learning: Discovering Patterns Independently

Unsupervised learning, on the other hand, is like setting off on an adventure without a map. In this approach, machines are presented with unlabeled data, where there are no pre-existing tags or labels. The goal is for the model to find inherent patterns and structures within the data.

In unsupervised learning, the model is provided with inputs, or features, and tasked with independently identifying similarities or differences among them. For instance, imagine we have customer purchase data and want to group customers based on their buying behavior. The model analyzes the data and may reveal clusters of customers who purchase similar items, providing valuable insights for targeted marketing strategies. Unsupervised learning finds applications in anomaly detection, recommendation systems, and clustering.

A significant portion of the remarkable progress in AI can be attributed to the advancements in supervised learning, particularly in the realm of deep learning. Deep learning is a subset of supervised learning that has gained significant attention due to its ability to tackle complex tasks with exceptional accuracy.

At the heart of deep learning algorithms are artificial neural networks, which take inspiration from the structure and functioning of the human brain and mimic the interconnectedness of neurons in the brain. The network consists of layers of nodes, also known as neurons. Each neuron performs a mathematical operation on the input it receives, using weights and biases associated with the connections between neurons. These weights and biases determine the strength and impact of each input on the final output prediction. The input traverses through the network, layer by layer, until it reaches the final layer, which produces the prediction. This process is known as the forward pass.

However, the true power of artificial neural networks lies in their ability to learn and improve their predictions over time. This learning process occurs through a technique called back propagation. In supervised learning, where the correct output values (labels) are known for each data point, the neural network computes a loss or error between the predicted output and the actual output. This loss is then back propagated through the network, allowing the algorithm to adjust the weights and biases based on the magnitude and direction of the error. This adjustment is typically performed using optimization techniques like gradient descent, which incrementally updates the weights and biases to minimize the loss and improve the accuracy of predictions.

By iteratively repeating the forward pass and back propagation process on large amounts of labeled data, artificial neural networks can learn intricate patterns and relationships within the data, leading to increasingly accurate predictions.

In conclusion, artificial neural networks by mimicking the structure and functioning of the human brain, can process vast amounts of data and iteratively refine their predictions through the forward pass and back propagation. With continued advancements in deep learning, we can expect further breakthroughs and exciting applications of AI in the future.