Understanding Artificial Intelligence

Artificial Intelligence (AI) is a broad field of computer science focused on creating systems that can perform tasks typically requiring human intelligence. These tasks include learning, reasoning, problem-solving, perception, language understanding, and decision-making. Examples of AI applications include expert systems, speech recognition, natural language processing (NLP), computer vision, and robotics.

Machine Learning (ML) is a subset of AI that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. ML algorithms build a model based on sample data, known as ‘training data’, in order to make predictions or decisions without being explicitly programmed to perform the task.

Deep Learning (DL) is a further subfield of machine learning based on artificial neural networks with multiple layers (deep architectures). These networks learn representations of data with multiple levels of abstraction. TensorFlow is a popular library extensively used for implementing deep learning models.

Imagine a set of three concentric circles. The outermost circle is Artificial Intelligence. Inside it is Machine Learning, and at the very core is Deep Learning. This illustrates that DL is a specialized type of ML, which itself is a way to achieve AI.

The core idea of machine learning is that a program can improve its performance on a task by learning from observed data. This often involves ‘pattern recognition,’ where the system learns to identify patterns in data. These learned patterns are then used to make predictions or decisions on new, unseen data.

Machine learning models can be trained in several ways, with two primary categories being:

Supervised Learning
Unsupervised Learning

Supervised Learning

In supervised learning, the model is trained on a labeled dataset. This means that each training example is paired with a correct output label or target value. The algorithm learns to map input data to the correct output. The training process typically involves minimizing a loss function, which quantifies the difference between the model’s predictions and the true labels.

After training, the model’s performance is evaluated on a separate dataset (the test or validation set) that was not used during training. This helps to assess how well the model generalizes to new, unseen data. Common supervised learning tasks include classification (predicting a category, e.g., spam vs. not spam) and regression (predicting a continuous value, e.g., house price).

A good analogy for supervised learning is learning with a teacher. For example, if you provide a system with many pictures of cats, each labeled ‘cat’, and many pictures of dogs, each labeled ‘dog’, the system learns to distinguish between cats and dogs. It can then classify new, unlabeled pictures as either ‘cat’ or ‘dog’.

Unsupervised Learning

In unsupervised learning, the model is trained on an unlabeled dataset. The algorithm tries to learn patterns, structures, or relationships directly from the input data without any explicit guidance in the form of labels. The system identifies commonalities in the data and reacts based on the presence or absence of such commonalities in each new piece of data.

Common unsupervised learning tasks include clustering (grouping similar data points together, e.g., customer segmentation), dimensionality reduction (reducing the number of variables while preserving important information), and anomaly detection (identifying rare or unusual data points).

An analogy for unsupervised learning is like giving a collection of mixed fruits to someone who has never seen them before and asking them to sort them into groups based on similarity (e.g., color, size, texture). The system itself discovers the criteria for grouping. For example, given a large set of unlabeled customer data, an unsupervised algorithm might identify distinct customer segments based on purchasing behavior.