In the realm of artificial intelligence, SHRDLU stands as a pioneering example of early attempts to imbue machines with natural language understanding and reasoning capabilities. Developed by Terry Winograd in the late 1960s and early 1970s at the Massachusetts Institute of Technology (MIT), SHRDLU marked a significant milestone in the...
Have you ever heard of an object defying gravity and levitating above a magnetic surface without any visible means of support? Welcome to the mind-bending phenomenon known as “quantum locking.” 🪄🔮 Quantum locking, also referred to as the “Meissner effect,” is a captivating manifestation of quantum physics that seems like...
The discriminator in Generative Adversarial Networks (GANs) has a crucial role, acting as a classifier to distinguish between genuine and fabricated samples. A perfect discriminator assigns a probability of 1 to true samples and 0 to fake ones. However, this ideal scenario can lead to a challenging problem known as...
I have published my article “Comparison of Optimisation Methods for The Knapsack Problem: Steepest Ascent Hill Climbing vs. Simulated Annealing” on ResearchGate.
The paper explores the application of optimization algorithms, specifically Hill Climbing and Simulated Annealing, to solve the knapsack problem. The knapsack problem is a well-known combinatorial optimization problem with various real-world applications. The aim is to select a subset of items with maximum value while adhering to a weight constraint. The paper provides a comparative analysis of the two algorithms, evaluates their performance based on metrics such as best solution, average solution, and iterations required to converge, and identifies the optimal solution. Additionally, it includes a literature review discussing the application areas of the knapsack problem and concludes with the limitations of the chosen search methods and potential avenues for further development.
In 1952, British physiologists Alan Hodgkin and Andrew Huxley conducted groundbreaking research on the electrical properties of neurons by simulating their model of the squid giant axon. This work laid the foundation for understanding how action potentials, or electrical impulses, are generated and propagated along nerve cells. The squid giant...
Introduction Artificial Intelligence (AI) optimizers play a crucial role in training and fine-tuning machine learning models. These optimization algorithms are designed to improve model performance, enhance convergence, and efficiently use computational resources. In this article, we will delve into the world of AI optimizers, exploring their importance, popular types, and...
Introduction In the realm of robotics, where precision and rigid structures have long dominated, a new wave of innovation is emerging: soft robotics. Inspired by the flexibility and adaptability of biological systems, soft robots are revolutionizing the field by offering a safer and more versatile approach to human-robot interaction. This...
Introduction The quest for a unified theory of physics, one that can explain the nature of the universe at its most fundamental level, has been a driving force in scientific exploration for decades. Among the many contenders, string theory stands out as a captivating and elegant framework that aims to...
DALE-2 (Dialogue Act Labeling Evaluation 2) It is a research project focused on creating a dataset for dialogue act annotation and recognition. The primary goal is to automate the recognition of various speech acts in conversations, such as asking questions, making statements, or expressing agreement. This automation can help improve...
Navigating using stars is an ancient and fascinating way of finding your way in the dark. Here is a guide to help you learn how to navigate using stars: Learn the basics: Before you start navigating using stars, it is essential to learn some basics. Learn about the constellations visible...
