AI Learning and Training Approaches
Learning Types refer to the fundamental ways in which an AI model learns from data. This category includes: Supervised Learning, Unsupervised Learning and Reinforcement Learning
Introduction
It's essential to differentiate between the learning paradigm (how the model learns) and the model architecture or algorithm (what model is being trained).
Foundational paradigms like upervised, unsupervised, and reinforcement learning define how a model is trained on data.
These learning paradigms are used to train a model - eg a neural network and its associated algorithms. (A neural networks is a type of model architecture which use the backpropogation algorithm to update the weights of the network during training)
Building upon these foundational paradigms, advanced learning paradigms offer refined techniques that optimise the AI training process.
Learning Types
The 3 primary ways an AI model learns from data are :
Supervised Learning: In supervised learning, AI systems are trained on labelled datasets, which contain input-output pairs. The AI system learns to predict the output based on the input by minimising the difference between its predictions and the actual output. Supervised learning is useful for tasks such as image classification, speech recognition, and natural language processing. Click here to read our blog on Supervised Learning
Unsupervised Learning: Unsupervised learning involves training AI systems on unlabelled data, without any predefined input-output pairs. The AI system learns to identify patterns, correlations, or structures within the data by itself. Unsupervised learning is useful for tasks such as anomaly detection, clustering, and dimensionality reduction. Click here to read our blog on Unsupervised Learning
Reinforcement Learning: In reinforcement learning, AI systems learn by interacting with their environment and receiving feedback in the form of rewards or penalties. The AI system aims to maximise the cumulative reward over time by choosing the optimal sequence of actions. Reinforcement learning is suitable for tasks such as game AI, robotics, and autonomous vehicles. Click here to read our blog on Reinforcement Learning
Why is it important to training AI Models
Training AI models is crucial for several reasons:
- Improving Accuracy: Proper training enables AI systems to make better decisions, increasing the accuracy of their predictions, classifications, and overall performance.
- Generalisation: Training helps AI systems to generalise their knowledge and apply it to new, unseen situations. This adaptability is essential for AI systems to handle a wide range of scenarios and challenges.
- Reducing Bias: Well-trained AI systems can minimise biases in their decision-making processes, leading to more objective and fair results.
- Enhancing Efficiency: Training AI systems can optimise their performance, reducing the computational resources and time required to accomplish tasks.
Advanced Learning Paradigms
While the fundamental learning types provide the foundation for AI learning, there are higher-level strategies that further enhance the learning process:
Transfer Learning: This involves training a model on one task and then reusing parts of that learned model for a different but related task. It's akin to applying past knowledge to new but related problems. Click here to read our blog on Transfer Learning
Multi-task Learning: Here, the AI model solves multiple related tasks concurrently, leveraging commonalities and differences among the tasks to enhance its learning efficiency.
Active Learning: The learning algorithm itself selects the examples it wants to learn from, prioritizing the most informative examples.
Semi-Supervised Learning: This approach uses a small amount of labelled data and a large amount of unlabelled data. The model leverages the labelled data to learn a general structure and then applies this structure to classify the unlabelled data.
Self-Supervised Learning: In this type, the model generates its own labels from the input data, allowing it to learn from vast amounts of unlabelled data.
Few-shot/One-shot/Zero-shot Learning: These approaches are designed for situations where data is scarce. They aim to build models that make accurate predictions with minimal examples.
Case study : Training Chat GPT
ChatGPT, like its predecessors, is a modeltrained using a method called unsupervised learning on a massive dataset containing parts of the internet, such as websites, articles, and other text sources. This dataset provided diverse linguistic and contextual information that the model could learn from.
The training process involved a two-step approach: pre-training and fine-tuning. During pre-training, the model learned to predict the next word in a sentence, given the context of the words that came before it. This allowed the model to learn grammar, facts about the world, reasoning abilities, and some biases present in the data.
After pre-training, the model underwent a fine-tuning process using a narrower dataset, which was carefully generated with the help of human reviewers. Reviewers followed guidelines provided by the developers to review and rate possible model outputs for a range of inputs. The model then generalised from this reviewer feedback to respond to a wider array of user inputs.
Conclusion
In AI, it's crucial to differentiate between foundational learning paradigms, which outline how models learn, and the model architecture or algorithm, which specifies what is being trained. While paradigms like supervised, unsupervised, and reinforcement learning provide the base, advanced learning paradigms build upon them, introducing more refined approaches to address complex AI training needs.
