How Do Machines Learn ?
The word AI pops up more often than ever these days. The rapid advancements and rise of LLM’s have created widespread awareness, excitement, and interest in the world of AI.
While it’s beneficial for people to join in this growing awareness, it’s also helpful to equip ourselves with a philosophical perspective on AI. This allows us to objectively assess our place in AI’s future and ponder a critical question: Will our cognitive skills be at risk of being replaced by AI in the near future?
To explore this question further, I will publish four articles focusing on the following topics:
• How do machines learn?
• How do humans learn?
• What is intelligence & how to define it ?
• The differences & similarities between machine and human learning.
• Is it crucial for machines to learn like humans to achieve humanlike intellectual skills?Let’s begin our philosophical journey by explaining the very well-known topic: How do machines learn?
In this section, I’ll break down the process of how machines learn into seven distinct phases. I’ll keep the explanations as simple as possible, avoiding unnecessary details to ensure clarity. These simple explanations will serve as the foundation for building more complex concepts in the later articles. While this overview will be straightforward, following articles will delve into more intricate aspects.
1. Data Collection
• Gathering Data: Large datasets composed of text from various sources such as books, articles, websites, and other written materials are collected. This ensures a wide variety of language patterns and contexts.
• Diversity: The data includes different topics, styles, and contexts to provide a broad and comprehensive learning base. Diverse data helps the model generalize better to different situations and topics.
2. Preprocessing
• Tokenization: The text is broken down into smaller pieces like words, subwords, or characters. This step is crucial for converting text into a format that can be processed by neural networks.
• Normalization: The text is cleaned up, such as converting everything to lowercase, removing punctuation, handling contractions, and removing stop words. These steps help reduce variability and noise in the data, making it easier for the model to learn.
3. Model Architecture
• Neural Networks: Neural networks, particularly deep learning models like transformers, use layers of neurons to process data. Each neuron performs a simple computation, and layers of neurons work together to learn complex patterns.
• Layers: Each layer processes the data and passes it on to the next layer, gradually building more complex representations of the input. Early layers might detect simple features (like edges in images or common words in text), while later layers detect more complex structures (like shapes or phrases).
• Attention Mechanisms: Advanced models like transformers use attention mechanisms to focus on different parts of the input text, improving understanding of context. This allows the model to weigh the importance of different words when making predictions, leading to more accurate and context-aware outputs.
4. Training
• Supervised Learning: It’s like teaching the AI with examples, showing it pairs of questions and answers or input-output pairs. The model learns by comparing its predictions to the actual outputs and adjusting accordingly.
• Objective Function: The model’s goal is to minimize the difference between its predictions and the actual outputs, often measured by a loss function like cross-entropy loss. This function quantifies how well the model’s predictions match the expected outcomes.
• Backpropagation: Imagine adjusting your aim after each throw based on where the previous throw landed. This technique adjusts the model’s parameters to minimize the loss by propagating the error backward through the network.
• Optimization Algorithms: Algorithms like stochastic gradient descent (SGD) or Adam are used to optimize the model’s performance by updating the model’s parameters based on the gradients of the loss function. These algorithms determine the direction and magnitude of updates to improve the model’s accuracy.
5. Fine-Tuning
• Pretraining: The model is first trained on a large, diverse dataset to learn general language patterns. This initial phase helps the model understand broad linguistic features and structures.
• Task-Specific Fine-Tuning: After pretraining, the model is fine-tuned on a smaller, task-specific dataset, such as sentiment analysis or machine translation, to specialize it for the particular task. This step adapts the pretrained model to the specific requirements and nuances of the new task.
6. Inference
• Generating Outputs: During inference, the trained model generates outputs based on the learned patterns, like predicting the next word in a sentence or providing an answer to a question. The model uses its learned knowledge to produce responses to new inputs.
• Contextual Relevance: The model uses the context provided by the input to generate relevant responses, ensuring coherence and appropriateness in the output. This helps the model maintain a logical flow in conversations or generate contextually appropriate text.
7. Continuous Learning (Updates)
• Retraining: Periodic updates and retraining on new data are required to keep the model’s knowledge current, addressing issues like data drift and changing trends. This helps the model stay accurate and relevant over time.
• Transfer Learning: Pretrained models can be adapted to new tasks with relatively little additional training, leveraging previously learned features to quickly adapt to new challenges. This approach saves time and resources by reusing existing knowledge for new tasks.
Key Points
1. Pattern Recognition: Current AI models, particularly deep learning neural networks, excel at recognizing patterns in large datasets. These models can identify complex structures and relationships within the data, allowing them to make accurate predictions and generate relevant outputs.
2. Static Knowledge: Once trained, the model’s knowledge is static and does not change unless it undergoes further training. This means that while the model can perform well on the data it was trained on, it requires periodic updates and retraining to maintain accuracy and relevance as new data becomes available.
3. Dependence on Data Quality: The quality and diversity of the training data significantly impact the model’s performance. High-quality, diverse datasets help the model generalize better to new, unseen data. Conversely, poor-quality or biased data can lead to inaccurate predictions and biased outputs.
4. Layered Learning: Neural networks use layers of neurons to process data, with each layer learning increasingly complex features. Early layers might detect simple patterns, while deeper layers capture more intricate relationships. Attention mechanisms in advanced models like transformers enhance context understanding by focusing on relevant parts of the input.
5. Supervised Learning and Optimization: Training involves supervised learning where models learn from labeled data, adjusting their parameters to minimize the error between predictions and actual outputs. Optimization algorithms such as stochastic gradient descent (SGD) and Adam play a crucial role in refining model performance.
6. Pretraining and Fine-Tuning: Models are often pretrained on large, diverse datasets to learn general patterns and then fine-tuned on task-specific data to specialize in particular applications. This two-step training process enhances the model’s adaptability and accuracy for specific tasks.
7. Inference and Contextual Relevance: During inference, trained models generate outputs based on learned patterns, using context to produce relevant and coherent responses. This ability to maintain contextual relevance is crucial for applications like natural language processing.
8. Continuous Learning and Transfer Learning: AI models benefit from continuous learning through periodic retraining with new data to stay current. Transfer learning allows pretrained models to be adapted to new tasks with minimal additional training, leveraging existing knowledge to tackle new challenges efficiently.
These processes allows current AI’s to learn and generate human-like text, make predictions, and assist with various tasks based on the patterns identified during training. However, it’s important to question that whether this type of learning is fundamentally different from human learning, which we will elaborate more on later articles.
As we dive deeper and explore the future of AI, a critical question will arise:
Is it crucial for AI’s/machines to learn like humans to achieve human-level intellectual skills?
Understanding the differences between AI learning and human learning will help us determine whether mimicking human cognitive processes is necessary for AI to reach truly intelligent and adaptable capabilities. This distinction will guide our approach to developing AI technologies that complement and enhance human intelligence.
