Learning paradigms in AI – Understanding how AI learns

Understanding AI from a technical perspective will help you learn how to unlock new opportunities, drive innovation, and create lasting value. But with so many terms to understand, what is the best way to approach it? As part of our Demystifying AI series, we have created three short articles that cover the 37 top terms you need to know, with each article focusing on a key AI domain area: learning paradigms, how to build and deploy AI, and gen AI specifics.

This article explores the key learning paradigms in AI, as part of our Foundational Understanding terms.

Learning paradigms in this context mean how machines can be trained to process information, make decisions, and solve problems. These paradigms comprise a range of techniques and methodologies that teach computers to learn from data and experiences, adapting and improving over time.

It’s key to understand that many complex concepts work together to create a robust AI learning system, so you can better understand how to drive reinvention with this new technology. We’ll explore these terms more fully below, but in short:

• Machine Learning provides the foundation, enabling computers to identify patterns and insights from vast data sets.
  
  
• Supervised, Unsupervised, and Reinforcement Learning offer different approaches to learning from data.
  
  
• Transfer Learning accelerates this learning process, and Optimization Algorithms refine the AI model's accuracy.
  
  
• Loss Functions measure the model's performance, while NLP and Computer Vision expand the AI's capabilities to understand and interact with the world.

Together, these components enable AI to learn, adapt, and perform a wide range of tasks, from predicting customer behavior to driving cars and understanding human language.

Please read on to explore these terms more fully.

Machine Learning (ML): Teaching computers to learn from data

At its essence, machine learning empowers computers to learn from data, much like humans learn from experience. Instead of being explicitly programmed with rigid rules, machine learning algorithms analyze vast datasets to identify patterns, trends, and relationships. This enables them to make predictions or decisions on new, unseen data.

For example, consider a scenario where a company wants to predict customer churn. Traditionally, this might involve manually analyzing customer data and creating rules based on observations. With machine learning, the process becomes automated and more accurate. The algorithm is fed historical customer data, including demographics, purchase behavior, and interactions with the company. It then learns the subtle patterns that differentiate customers who churned from those who remained loyal. Armed with this knowledge, the model can predict which current customers are at risk of churning, allowing the company to take proactive measures to retain them.

Neural network: The building blocks of deep learning

A neural network is the fundamental building block of deep learning. It's a computational model loosely inspired by the interconnected structure of the human brain, composed of layers of interconnected nodes, or "neurons," that process information. Each connection between neurons has a "weight," which determines the strength of the signal passed between them. During training, these weights are adjusted, allowing the network to learn patterns in data and make predictions.

Deep Learning (DL): Unlocking the power of neural networks

Deep learning is a specialized branch of machine learning that uses artificial neural networks with many layers to analyze complex patterns in data. Traditional machine learning algorithms often struggle with unstructured and high-dimensional data like images, speech, and text; conversely, deep learning excels in these domains. Its ability to learn hierarchical representations, extracting intricate features and relationships from raw data, allows it to do things that were previously challenging for computers. Deep learning has driven massive advances in image recognition, natural language processing, and speech synthesis, powering applications like self-driving cars, medical image analysis, and even creative content generation.

Supervised learning: Learning with a guide

Supervised learning is a type of machine learning where the AI model learns from labeled data. Think of it as a student learning with the help of a teacher. The model is provided with input data (features) and its corresponding correct output (the label). For example, in a customer churn scenario, the features could be customer demographics and purchase history, while the label would be whether the customer churned or not. By analyzing these examples, the model learns to identify patterns and build a mapping between the input features and the output labels. This knowledge enables it to make predictions or classifications on new, unseen data – such as whether a customer is going to leave or not.

Supervised learning is commonly used for tasks such as image classification and spam email filtering.

Unsupervised learning: Unlocking insights from unlabeled data

Unlike supervised learning, unsupervised learning deals with unlabeled data, such as images, articles, recordings, etc. The model's task is to discover hidden patterns, structures, or relationships within the data on its own. It's like an explorer charting uncharted territory, looking for meaningful clusters, anomalies, or underlying trends. Unsupervised learning is valuable for exploratory data analysis, customer segmentation, anomaly detection, and dimensionality reduction, which simplifies data for visualization or further analysis.

Reinforcement learning: Learning through trial and error

Reinforcement learning is a type of machine learning where an AI agent learns to interact with an environment through trial and error, much like how we learn through experience. The agent receives rewards for positive actions and penalties for negative ones. Over time, the agent learns to make decisions that maximize its cumulative reward, developing a strategy or "policy" for navigating its environment. This approach is useful for training AI agents to play games, control robots, and optimize complex systems like supply chains.

Transfer learning: Learning from past experiences

Transfer learning is like building on existing knowledge to learn something new. Instead of starting from scratch, we can adapt a pre-trained AI model to a new, related task, working with the patterns and features it has already learned. For example, a model trained to recognize different car models could be adapted to identify trucks, using its existing knowledge of vehicle features. This approach accelerates the learning process, requires less data, and often leads to improved performance. It's a powerful technique used across various AI applications, from image recognition to natural language processing.

Optimization algorithm: Refining AI’s accuracy

In machine learning, an optimization algorithm is the engine that drives the learning process. It's responsible for adjusting the model's internal parameters over time, to narrow the gap between the model's predictions and the actual outcomes. Think of it as a sculptor refining a piece of art, constantly making small adjustments until it reaches its desired form. Common optimization algorithms like gradient descent use mathematical techniques to guide the model towards better performance.

Loss function: Measuring the gap between prediction and reality

A loss function, in the context of machine learning, is a mathematical function that measures the difference between a model's predictions and the actual target values or outcomes. You can think of it as a fact checker that takes action to improve results over time. The goal of the optimization algorithm is to improve the model's accuracy and performance.

Natural Language Processing (NLP): Deciphering language

Imagine teaching a computer to understand the nuances of human language, from the subtle sarcasm in a tweet, to the complex legal jargon in a 200-page contract. That's the realm of Natural Language Processing (NLP), a field of AI dedicated to bridging the gap between human communication and machine understanding. NLP encompasses a wide array of tasks, including language translation, sentiment analysis, text summarization, and even chatbots that can engage in natural conversations. It empowers computers to analyze, interpret, and generate human language, unlocking a wealth of possibilities for automation, communication, and knowledge discovery. NLP helps businesses gain insights from customer feedback, automate content creation, and provide personalized experiences – transforming the way they interact with their audience and the world around them.

Computer vision: Teaching machines to see

Just as our eyes perceive and interpret the visual world, computer vision enables machines to "see" and understand images and videos. This field of AI involves training algorithms to recognize objects, scenes, and even emotions within visual data. Computer vision is behind technologies like self-driving cars, facial recognition systems, and medical image analysis. It's revolutionizing industries by automating tasks, enhancing safety, and providing valuable insights from visual information, unlocking new possibilities for innovation and efficiency.

We extend our gratitude to Dr. Andrew Ng of DeepLearning.AI and Dr. Savannah Thais of Columbia University for their invaluable review and insights, which greatly enriched this blog series.