Emerging and cross-disciplinary topics are subjects and fields that combine ideas, methods, and tools from different traditional disciplines to address new or complex challenges. These topics often arise as science and technology advance, leading to unexpected overlaps between areas like biology, computing, engineering, social sciences, and the arts. The goal is to create innovative solutions…
Category: Artificial Intelligence
Scriptless Scripts
Scriptless scripts refer to automated testing methods that do not require testers to write traditional code-based scripts. Instead, testers can use visual interfaces, drag-and-drop tools, or natural language instructions to create and manage tests. This approach aims to make automation more accessible to people without programming skills and reduce the maintenance effort needed for test…
Hybrid Consensus Models
Hybrid consensus models combine two or more methods for reaching agreement in a blockchain or distributed system. By using elements from different consensus mechanisms, such as Proof of Work and Proof of Stake, these models aim to balance security, speed, and energy efficiency. This approach helps address the limitations that each consensus method might have…
Sparse Gaussian Processes
Sparse Gaussian Processes are a way to make a type of machine learning model called a Gaussian Process faster and more efficient, especially when dealing with large data sets. Normally, Gaussian Processes can be slow and require a lot of memory because they try to use all available data to make predictions. Sparse Gaussian Processes…
Kernel Methods in ML
Kernel methods are a set of mathematical techniques used in machine learning to find patterns in data by comparing pairs of data points. They allow algorithms to work with data that is not easily separated or structured, by transforming it into a higher-dimensional space where patterns become more visible. This makes it possible to solve…
Gaussian Process Regression
Gaussian Process Regression is a method in machine learning used to predict outcomes based on data. It models the relationship between inputs and outputs by considering all possible functions that fit the data, and then averaging them in a way that accounts for uncertainty. This approach can provide both predictions and a measure of how…
Expectation-Maximisation Algorithm
The Expectation-Maximisation (EM) Algorithm is a method used to find the most likely parameters for statistical models when some data is missing or hidden. It works by alternating between estimating missing data based on current guesses and then updating those guesses to better fit the observed data. This process repeats until the solution stabilises and…
Variational Inference
Variational inference is a method used in statistics and machine learning to estimate complex probability distributions. Instead of calculating exact values, which can be too difficult or slow, it uses optimisation techniques to find an easier distribution that is close enough to the original. This helps to make predictions or understand data patterns when working…
Conditional Random Fields
Conditional Random Fields, or CRFs, are a type of statistical model used to predict patterns or sequences in data. They are especially useful when the data has some order, such as words in a sentence or steps in a process. CRFs consider the context around each item, helping to make more accurate predictions by taking…
Markov Random Fields
Markov Random Fields are mathematical models used to describe systems where each part is related to its neighbours. They help capture the idea that the condition of one part depends mostly on the parts directly around it, rather than the whole system. These models are often used in situations where data is organised in grids…