Domain adaptation is a technique in machine learning where a model trained on data from one environment or context is adjusted to work well in a different but related environment. This is useful when collecting labelled data for every new situation is difficult or expensive. Domain adaptation methods help models handle changes in data, such…
Active learning is a machine learning method where the model selects the most useful data points to learn from, instead of relying on a random sample of data. By choosing the examples it finds most confusing or uncertain, the model can improve its performance more efficiently. This approach reduces the amount of labelled data needed,…
Meta-learning is a method in machine learning where algorithms are designed to learn how to learn. Instead of focusing on solving a single task, meta-learning systems aim to improve their ability to adapt to new tasks quickly by using prior experience. This approach helps machines become more flexible, allowing them to handle new problems with…
Bayesian Neural Networks are a type of artificial neural network that use probability to handle uncertainty in their predictions. Instead of having fixed values for their weights, they represent these weights as probability distributions. This approach helps the model estimate not just an answer, but also how confident it is in that answer, which can…
Variational Autoencoders, or VAEs, are a type of machine learning model that learns to compress data, like images or text, into a simpler form and then reconstructs it back to the original format. They are designed to not only recreate the data but also understand its underlying patterns. VAEs use probability to make their compressed…
Generative Adversarial Networks, or GANs, are a type of artificial intelligence where two neural networks compete to improve each other’s performance. One network creates new data, such as images or sounds, while the other tries to detect if the data is real or fake. This competition helps both networks get better, resulting in highly realistic…
A semantic forking mechanism is a process that allows a system or software to split into different versions based on changes in meaning or interpretation, not just changes in code. It helps maintain compatibility or create new features by branching off when the intended use or definition of data or functions diverges. This mechanism is…
Intent shadowing occurs when a specific intent in a conversational AI or chatbot system is unintentionally overridden by a more general or broader intent. This means the system responds with the broader intent’s answer instead of the more accurate, specific one. It often happens when multiple intents have overlapping training phrases or when the system…
Memory-constrained inference refers to running artificial intelligence or machine learning models on devices with limited memory, such as smartphones, sensors or embedded systems. These devices cannot store or process large amounts of data at once, so models must be designed or adjusted to fit within their memory limitations. Techniques like model compression, quantisation and streaming…
Subsymbolic feedback tuning is a process used in artificial intelligence and machine learning where systems adjust their internal parameters based on feedback, without relying on explicit symbols or rules. This approach is common in neural networks, where learning happens through changing connections between units rather than following step-by-step instructions. By tuning these connections in response…