Tag Archives: AI

Solution to Owning Arts — Traditional Artist versus Digital and AI Artist

Summary: In case artists are working in collaboration, one is a traditional artist and another is digital artist or AI artist, both can collaborate to make the art reach new heights with joint ownership and no tension of copywriting. This is a plausible solution to the future of artwork. This would need a platform whereContinue reading “Solution to Owning Arts — Traditional Artist versus Digital and AI Artist”

Understanding Responsible Robotics-Why and How?

#futurist #future Responsible robotics assumes robotics that is helping humans in solving problems and assisting humans in needful work, and at the same time, Robotics should not do wrong things to harm mankind in any way. With this being the aim, responsible robotics can be built. Once the software is set and knows a machineContinue reading “Understanding Responsible Robotics-Why and How?”

AI powered Tailor-made Courses

#education #school #universities #diplomas #degrees In one of my previous articles, I proposed pluggable courses for schools, colleges, and online/offline diploma providers. In this article, I present to you some points on the AI-made subject outline and AI-made syllabus of the course. The course for example can be an open elective course. Let’s see how!Continue reading “AI powered Tailor-made Courses”

AI as a Threat versus Other Scientific Fields

#AI Like other scientific disciplines, Artificial Intelligence (AI) is also a scientific discipline. The question is how safe is it? If developed in the right way, it would be a boon to other scientific disciplines and to its own progress as well as a help to mankind. But if it is left open then itContinue reading “AI as a Threat versus Other Scientific Fields”

The Good AI and the Bad AI — Part I

Here is a description of what constitutes a Good AI and what forms part of the Bad AI. There has been tremendous progress in AI, irrespective of the fact that it is a good AI or it is a potentially harmful AI. Let us discuss what is in good AI and what is in badContinue reading “The Good AI and the Bad AI — Part I”

Causal Reasoning and LLM Paper Review

The following paper is discussed in this article. Causal Reasoning and Large Language Models: Opening a New Frontier for Causality. Kıcıman and Ness et al. (2023) Paper Review Causality — The automatic detection relationship between cause and effect. For illustration, consider two concepts that are discussed in some text, say medical text. Let the conceptsContinue reading “Causal Reasoning and LLM Paper Review”

Python based Particle Swarm Optimization for a function optimization

Here is a quick introduction to Particle Swarm Intelligence based optimization and its implementation in Python. As the name states, Particle Swarm Optimization (PSO) is an optimization algorithm based on the intelligence of swarm learning. Swarms of birds, ducks, and insects often follow a pattern to achieve their activities. PSO have many application. Lets startContinue reading “Python based Particle Swarm Optimization for a function optimization”

Optimizing a function with Genetic Algorithm in Python

Function Optimization means how to find the maximum and/or minimum of the function in a given domain. The aim is to find the optimal value viz. maximum or minimum value of a function. To explain the process in Python we take an example of the Rastrigin function. Let us understand what this function means first.Continue reading “Optimizing a function with Genetic Algorithm in Python”

GPT can be understood as composition of functions or just as a Black Box?

Here are some points Many people say they can’t understand a GPT model. Well then consider it as a black box, which gives answers. Even the black box here can be understood if given time and resources to understand. The aim to understand it is the fact that the inputs are all predefined text articlesContinue reading “GPT can be understood as composition of functions or just as a Black Box?”

80000 tested GPT but where are the test cases? Testing not enough, use test cases to train new GPT and automate testing

In an article it was relieved that 80000 Google Employees tested its GPT, which still makes errors in outputs. Now there are some key points. These points can be used on any GPT testing, not necessarily of capacity of 80000. When someone makes testing which is so huge, 80000 is a huge number, then oneContinue reading “80000 tested GPT but where are the test cases? Testing not enough, use test cases to train new GPT and automate testing”