Category Archives: data science

Are Machines Ready to be CEO?

Or Assistant to Top Jobs! Note: The article is also on authors other social media accounts as well. #ai #management #robotics #artificialintelligence  My answer is No, as of now, partially yes to be an assistant! Here is why! Integrating all kinds of AI algorithms in one robot shall still take some time, months if notContinue reading “Are Machines Ready to be CEO?”

Elon Musk’s LLM — Grok, does it read your X (Twitter) accounts?

Note: The article is on authors medium accounts as well. This is not a news article. Elon Musk, a name that owns Tesla vehicles, claims AI in them to be in beta stage, it owns SpaceX massive business, and as we all know Starlink is also one of Elon’s popular initiatives. In today’s world allContinue reading “Elon Musk’s LLM — Grok, does it read your X (Twitter) accounts?”

Regulating AI — How and When

Regulating AI- time is now, as we missed it out in past, but it is never too late. Here are some points on How, Why, What and When to regulate in AI! References [1] Nobile, C. G. (2023). Regulating Smart Robots and Artificial Intelligence in the European Union. Journal of Digital Technologies and Law, 1(1),Continue reading “Regulating AI — How and When”

Terror Threats Detection using Fuzzy Logic: An Example of Framework

Here is article I wrote on Terror message detection using AI algorithms. DOI: 10.13140/RG.2.2.10272.35844 Link: (PDF) Terror Threats Detection using Fuzzy Logic: An Example of Framework (researchgate.net) Abstract- As terrorism grows so does the incidence of attacks by terror groups. How to detect terror messages on time is a critical issue and scientists need to address thisContinue reading “Terror Threats Detection using Fuzzy Logic: An Example of Framework”

Generic Language Graph-Based Query Expansion (IR)

A generalized algorithm based on the algorithm in [1] made especially for Hindi Query Expansion and Hindi Information Retrieval (IR) is as follows. Note: The modified algorithm below has not been published as of now. This can be modified and implemented in your works, based on the language you are working with. This has not beenContinue reading “Generic Language Graph-Based Query Expansion (IR)”

Telemedicine and AI

#ai #ml #artificialintelligence Telemedicine name started to be used in the 1970s [1]. It refers to treatments and medical directions being provided from a distance. These include the use of ICT, medical images, and medical data sharing with secure communications. Use of radiology from a distance, oncological services, gynae, respiratory illness treatment, and consultations allContinue reading “Telemedicine and AI”

AI Ethics. Chapter Summary

Note: This article is present in its copy on author’s medium.com account as well. #ai #artificialintelligence #robotics #machinelearning This is a summary of a chapter from the latest book on AI Ethics, by C. Bartneck et al. (2021) and has been self-written. Ethics define the moral set of rules that define a person’s decisions. WhatContinue reading “AI Ethics. Chapter Summary”

Chapter 2 Review. What is AI? Book Chapter Bartneck et al.

Note: This article is present in its copy on author’s medium.com account as well. #ai #artificialintelligence #robotics #machinelearning This is a review of Chapter 2 of their book, Bartneck et al., on Ethics in AI and Robotics. Key points in the chapter are as follows: — AI definitions (Bartneck et al): It involves the study,Continue reading “Chapter 2 Review. What is AI? Book Chapter Bartneck et al.”

Feature Selection Using Genetic Algorithm

#AI #artificialintelligence #ml #machinelearning Note: This article is present on authors medium.com account as well. In the past few articles, you might have got an idea of what feature selection is. In short, it is used to improve accuracy, decrease noise and at the same time decrease computational complexity by reducing irrelevant features from bigContinue reading “Feature Selection Using Genetic Algorithm”

Feature Selection — Part II

In the last article on Feature Selection, we discussed what feature selection is all about. Let’s briefly discuss what some key feature selection algorithms are: Here the features are independently categorized as important or not important without the use of any other classifier. Examples of such methods are, information gain, correlation coefficient, mean absolute difference.Continue reading “Feature Selection — Part II”