Welcome to AIAD 2024

3rd International Conference on Artificial Intelligence Advances (AIAD 2024)

July 13 ~ 14, 2024, Virtual Conference

Accepted Papers
Transforming Everyday Environments: the Power of Ambient Intelligence

Partha Sarathi Samal, Rocky Hill, Connecticut, USA


This paper on Ambient Intelligence (AmI) represents a transformative approach to integrating technology seamlessly into everyday environments to enhance human interaction and quality of life. By leveraging the synergy of ubiquitous computing, artificial intelligence, and the Internet of Things, AmI environments are designed to be sensitive, adaptive, and responsive to human presence and needs. This paper delves into the fundamental aspects of Ambient Intelligence including its core concepts of context awareness, personalization, and adaptability. We explore the key technologies driving AmI—sensing and data collection, data processing and analysis, and communication networks. Additionally, we highlight practical applications of Ambient Intelligence across various domains such as smart homes, healthcare, and smart cities, showcasing their potential to significantly improve daily life and operational efficiency. The paper also addresses the pivotal challenges of privacy, security, interoperability, and user acceptance that need overcoming to realize the full potential of AmI. By providing a comprehensive overview of Ambient Intelligence, this document aims to foster a deeper understanding and spur further innovation in creating intelligent, intuitive environments that anticipate and fulfil human needs.


Ambient Intelligence, Smart Environments, Ubiquitous Computing, Context-Aware Systems, Intelligent Systems.

Beyond Binary Classification: Unraveling Nuances in Generative AI and Human-authored Texts Through Nlp and Statistical Data Analysis

Mayowa Akinwande, Department of Computer Sciences, Austin Peay State University, Clarksville, Tennessee, USA


This research explores the nuanced differences in texts produced by AI and those written by humans, aiming to elucidate how language is expressed differently by AI and humans. Through comprehensive statistical data analysis, the study investigates various linguistic traits, patterns of creativity, and potential biases inherent in Human-written and AI-generated texts. The significance of this research lies in its contribution to understanding AI's creative capabilities and its impact on literature, communication, and societal frameworks. By examining a meticulously curated dataset comprising 500K essays which comprised a diverse range of text samples, spanning various topics and genres generated by LLM's or written by Humans, the study uncovers the deeper layers of linguistic expression and provides insights into the cognitive processes underlying both AI and human-driven textual compositions. The paper addresses challenges in assessing the language generation capabilities of AI models and emphasizes the importance of datasets that reflect the complexities of human-AI collaborative writing. Through systematic preprocessing and rigorous statistical analysis, this study offers valuable insights into the evolving landscape of AI-generated content and informs future developments in natural language processing (NLP).


Linguistic analysis, AI-generated texts, Creativity patterns, Bias detection, LLMs (Large Language Models), Natural language processing (NLP).

An Approach to Demonstrate That a Cognitive System Does Not Have Subjective Consciousness

Manuel Boissenin, Medalgo, Montpellier, France


In this synthetic article, the principle of a proof, based on a variation of the thought experiment of the Chinese Room from John Searl, will be developed. It can allow to show that a cognitive system does not have any subjective consciousness. The demonstration will be made on a transformer architecture-based language model, however, it could be carried out and extended to many kind of cognitive systems with known architecture and functioning. It will also be shown that transformer architecture-based large language model have a form of asubjective phenomenal understanding.


Language models, transformers, subjective consciousness, understanding, asubjectivity.

Artificial Intelligence and the Data Storage Capacity for Growth

Dr. Richard Encarnacion, United States of America


This paper explores the relationship between data storage capacity and the growth potential of artificial intelligence (AI). Contrary to Nobel laureate Paul Romer's assertion that data limitations hinder AI growth, this report argues that both AI and human intelligence continuously generate new data, driving AI's development. The study includes data collection methods, relevant graphs, and references to multiple sources of data research and analysis.


Artificial Intelligence, Data Growth, Human Intelligence, Data Segmentation, Big Data