In computer science, algorithms are essential tools for solving problems efficiently. When designing algorithms, two key concepts to consider are optimality and complexity. Both concepts are related to the efficiency of an algorithm, but they approach efficiency from different perspectives. Optimality refers to the best possible solution to a problem,...
The Turing Test is a famous benchmark for assessing a machine’s ability to exhibit intelligent behaviour that is equivalent or indistinguishable from that of a human. However, the test has several limitations that have been subject to criticism and debate. One significant limitation of the Turing Test is that it...
Matrix factorization is an essential technique in linear algebra, which involves decomposing a given matrix into the product of two or more matrices. This technique has numerous applications across various fields, including data analysis, signal processing, machine learning, and computer graphics. Matrix factorization can be understood as a process of...
Linear algebra is a branch of mathematics that deals with linear equations, linear transformations, and vector spaces. It plays an important role in various fields, including physics, engineering, computer science, and economics. At its core, linear algebra studies the properties of linear equations and linear systems of equations, which are...
The enterprise as a whole is responsible for making an increasing number of decisions on a daily basis, the majority of which are made by algorithms housed within AI systems. It is possible for humans to make decisions while unconsciously harboring biases; therefore, we need to take precautions to ensure...
