July 13 ~ 14, 2024, Virtual Conference
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.
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).
Manuel Boissenin, Medalgo, Montpellier, France
With Large Language Models (LLMs) exhibiting astounding abilities in human language processing and generation, a crucial debate has emerged: do they truly understand what they process and can they be conscious? While the nature of consciousness remains elusive, this synthetic article sheds light on its subjective aspect as well as some aspects of their understanding. Indeed, it can be shown, under specific conditions, that a cognitive system does not have any subjective consciousness. To this purpose the principle of a proof, based on a variation of the thought experiment of the Chinese Room from John Searl, will be developed. 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. The main conclusions are that while transformers architecture-based LLMs lack subjective consciousness based, in a nutshell, on the absence of a central subject, they exhibit a form of “asubjective phenomenal understanding” demonstrably through various tasks and tests. This opens a new perspective on the nature of understanding itself that can be uncoupled with any subjective experience.
Language models, transformers, subjective consciousness, understanding, asubjectivity.
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
Maikel Leon, Department of Business Technology, Miami Herbert Business School, University of Miami, Florida, USA
The intersection of symbolic and sub-symbolic Artificial Intelligence (AI) presents a fertile ground for innovations that combine the interpretability of the former with the learning capabilities of the latter. This paper introduces Fuzzy Cognitive Maps (FCMs) as a hybrid model that encapsulates the strengths of both paradigms, proposing them as a viable solution to the challenges of explainability and interpretability in AI systems. FCMs have emerged as a compelling framework for representing causal knowledge and facilitating decision-making processes intuitively and justifiably. FCMs can handle the inherent uncertainty and vagueness seen in real-world scenarios, thus enabling a more natural and flexible approach to problem-solving. This intrinsic flexibility, combined with the capacity for learning and adaptation derived from sub-symbolic AI, positions FCMs as an ideal candidate for applications demanding high degrees of explainability and interpretability.
Fuzzy Cognitive Maps, Symbolic AI, Sub-symbolic AI, Explainable AI, and Interpretable AI.