AI is a vast field and encompasses far more than just ChatGPT and Large Language Models (LLMs). Let’s discover together what else there is and how these technologies can be used in practice.
Different Learning Types in AI
1. Supervised Learning:
With this method, a model is trained with labeled data. This means that we know the correct output for each input example. An example: A company wants to predict future revenue based on historical sales data. Here, regression can be used to draw a line through the data points and recognize trends. Another example: A camera wants to divide produced components into “defect-free” and “defective.” Here, data classification can help. With classification algorithms, images can be automatically divided into these classes. Algorithms such as Decision Trees, Random Forests, or Support Vector Machines are particularly useful here. These methods work well because they enable precise predictions through the use of clearly defined labeled data.
2. Unsupervised Learning:
Here, data is analyzed without predefined labels. The model searches for patterns and structures in the data. An example: A retailer wants to divide their customers into different groups based on their purchasing behavior. Through clustering, you can recognize which customers have similar purchasing patterns and carry out targeted marketing campaigns accordingly. Algorithms such as K-Means or hierarchical clustering are commonly used techniques. They are particularly helpful because they can uncover unknown patterns and structures in the data that would otherwise be overlooked.
3. Semi-Supervised Learning:
This technique combines few labeled data with a large amount of unlabeled data. An example: A company has only a few manually classified customer reviews, but still wants an AI that automatically classifies all reviews into “positive” or “negative.” Through Semi-Supervised Learning, you can train a powerful model with few labeled examples. This is particularly useful when labeling data is expensive or time-consuming. Techniques such as Self-Training or Co-Training are often used here to improve the model’s performance.
4. Reinforcement Learning:
This is an area where an agent learns how to behave in an environment to receive maximum reward. A well-known example: Autonomous driving. Here, the car learns through trial and error how to navigate safely and efficiently through traffic. The agent is rewarded for correct decisions and punished for wrong ones. Techniques such as Q-Learning or Deep Q-Networks (DQN) are common approaches. This method is particularly useful in dynamic and complex environments where the best actions need to be determined through continuous learning.
Some Practical Application Examples
Regression:
A company uses historical sales data to predict future revenues. This helps in planning and optimizing marketing strategies. For example, an energy provider can predict future electricity consumption to plan their resources efficiently.
Clustering:
A retailer analyzes their customers’ purchasing behavior to divide them into groups and create targeted advertising campaigns. Another example is the segmentation of credit card fraud patterns to detect suspicious activities early.
Classification:
An email service automatically filters out spam messages to improve the user experience. This is achieved through algorithms like Naive Bayes or neural networks, which learn to distinguish spam from important messages.
Segmentation:
A construction company uses AI to analyze blueprints and divide them into different classes, e.g., walls, doors, and windows. This enables efficient planning and execution of construction projects. Another example is medical image analysis, where tumors are distinguished from healthy tissue.
Challenges and Solutions
Each of these techniques brings its own challenges. For example, labeling large datasets can be expensive and time-consuming, but techniques like Semi-Supervised Learning can help overcome this hurdle. Even with unsupervised learning methods, it’s often difficult to interpret the patterns found, but visualization techniques and expert knowledge can help here.
Where Do LLMs like ChatGPT Fit in and Why Are They Special?
Large Language Models (LLMs) like ChatGPT fall into the area of supervised learning, as they are trained with huge amounts of labeled text data. They use methods like classification and regression at an extremely high level to generate texts, answer questions, and conduct conversations. A special technique that LLMs use is the attention mechanism, which allows the model to focus on relevant parts of the text and thus better understand contextual relationships. How this all works using GPT-3.5 as an example, I’ll explain next week with a fitting European Championship example.
What also makes LLMs special is the immense computing power required to train them. It takes millions of computing hours on high-performance computers to create models like GPT-3.5. These models are able to understand contextual and semantic relationships in texts, making them extremely versatile and powerful.
However, to exploit the full potential of AI in companies, specialized techniques like regression, clustering, classification, segmentation, reinforcement learning, and semi-supervised learning are often necessary. These methods offer specific solutions for different problem situations and can provide significant added value by enabling targeted analyses and optimizations.