Training AI Systems - Learning Types
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
Learning Types refer to the fundamental ways in which an AI model learns from data. This category includes:
- Supervised Learning: The model learns from labelled data.
- Unsupervised Learning: The model learns from unlabelled data.
- Reinforcement Learning: The model learns from interacting with its environment and receiving feedback in the form of rewards and punishments.
Learning Strategies or Paradigms refer to the higher-level strategies that an AI model can use to enhance its learning process, often regardless of the learning type being used. These are covered in a separate article.
While there can be overlap between the two categories (a model might use supervised learning and also employ transfer learning, for instance), this framework provides a broad way to differentiate between the core methodologies of machine learning and the strategies that can be used to enhance and refine these methodologies.
Different Approaches to Training AI Systems
There are several approaches to training AI systems, each with its unique advantages and challenges. Some of the most common methods include:
- 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.
- 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 Training AI Systems Important?
Training AI systems 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.
Training Chat GPT
ChatGPT, like its predecessors, was trained 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 generalized from this reviewer feedback to respond to a wider array of user inputs.
It's important to note that the training data for ChatGPT only goes up to September 2021, so any information or events beyond that date might not be accurately reflected in its responses.
Training AI systems is a complex and essential process that enables these systems to make accurate predictions, generalise knowledge, reduce biases, and enhance efficiency. By drawing inspiration from how animals learn through rewards and intrinsic motivations, AI developers can create effective training methodologies for supervised, unsupervised, and reinforcement learning. As AI systems continue to advance and play an increasingly significant role in various domains, the importance of refining training techniques and understanding their implications cannot be overstated. By learning from the training approaches used for models like ChatGPT, we can pave the way for more sophisticated and beneficial AI applications in the future.