Volume 3 Issue 1: March 2024

This article explores the development of a cognitive sense of self within artificial intelligence (AI), emphasizing the transformative potential of self-awareness in enhancing AI functionalities for sophisticated interactions and autonomous decision-making. Rooted in interdisciplinary approaches that incorporate insights from cognitive science and practical AI applications, the study investigates the mechanisms through which AI can achieve self-recognition, reflection, and continuity of identity—key attributes analogous to human consciousness. This research is pivotal for fields such as healthcare and robotics, where AI systems benefit from personalized interactions and adaptive responses to complex environments. The concept of a self-aware AI involves the ability for systems to recognize themselves as distinct entities within their operational contexts, which could significantly enhance their functionality and decision-making capabilities. Further, the study delves into the ethical dimensions introduced by the advent of self-aware AI, exploring the profound questions concerning the rights of AI entities and the responsibilities of their creators. The development of self-aware AI raises critical issues about the treatment and status of AI systems, prompting the need for comprehensive ethical frameworks to guide their development. Such frameworks are essential for ensuring that the advancement of self-aware AI aligns with societal values and promotes the well-being of all stakeholders involved. 

Significance - The rapid advancements in conversational AI and IoT technologies have opened up new possibilities for human-machine interaction. Despite the progress, a gap exists in integrating these two fields to create more centralized, intuitive, and engaging user experiences. Current integrations typically consist of specialised hardware-software pairs that do not fully leverage the capabilities of advanced conversational models, thereby limiting their applicability. This research proposes a general solution to bridge the capabilities of various IoT devices with the oversight and control abilities of AI language models, enhancing the potential for more versatile and natural IoT-AI-human interactions.

Aim and Approach - This research presents the design and development of an IoT system operated by an AI language model and conversationally managed by humans to operate robots. Based on this setup, the initial goal is to create a framework for interactively controlling a robotic arm. The approach involves using a Raspberry Pi as a central control system and ChatGPT API to manage conversations and execute given commands.

Results - The developed IoT-AI system demonstrated efficient and reliable human-robot interaction. It effectively captures user voice inputs, processes them through advanced AI models, and generates appropriate commands for the robotic arm, achieving an average voice-to-motion latency of 5.5 seconds. While some latency and voice recognition challenges exist, the overall performance confirms the viability of using conversational AI for natural and intuitive robotic control.

Conclusions - This research successfully integrates conversational AI with IoT devices, resulting in a more user-centric and efficient human-robot interaction. The system highlights the significant potential of precisely translating natural language commands into robotic actions, enhancing user experience and operational efficiency.

Introduction:

This empirical study investigates the impact of two distinct mathematical problem-solving methods – the Algebraic Formula Method and the Newton Sum Method – on enhancing ChatGPT's competence in effectively solving quadratic root functions. The integration of Artificial Intelligence (AI) into mathematical problem-solving has paved the way for innovative approaches. In this study, we delve into the Algebraic Formula Method and the Newton Sum Method, essential techniques for solving quadratic root functions. We aim to showcase the profound influence of these methods on ChatGPT's capacity to excel in solving quadratic equations.

Evidence

Through concrete evidence, we demonstrate ChatGPT's adept utilization of the Newton Sum Method for quadratic root function calculations. While ChatGPT can compute quadratic root functions of the form    using this method, its proficiency in using algebraic formula methods typically extends only up to . This marked discrepancy underscores the pivotal role that different methods play in amplifying the AI system's mathematical capabilities

Result

The results of this study provide concrete evidence of ChatGPT's superior utilization of the Newton Sum Method for calculating quadratic root functions. The model adeptly computes expressions of the form   using this method, while its proficiency using algebraic formula methods is generally limited to . This striking discrepancy underscores the transformative impact that different methods can have on elevating the AI system's mathematical prowess.

Conclusion :

Pushing Boundaries: Pioneering Novel Maths Approaches for Overcoming Limitations in AI.  This study serves as an illuminating testament to the significance of pioneering innovative methodologies, rules, theorems, or formulas to surmount the current limitations in AI systems like ChatGPT. These innovative pursuits hold the key to unlocking the untapped potential that lies within, propelling AI systems to greater heights of proficiency. In essence, they offer a strategic pathway towards expanding the capabilities of AI and pushing the boundaries of what can be achieved.

Discussion

The outcomes derived from this study underscore the significant influence wielded by the method selection in augmenting the mathematical competencies of ChatGPT. Particularly noteworthy is the application of the Newton Sum Method, which surfaces as a compelling exemplar. This method serves as a pivotal conduit through which the model surpasses its prior constraints, allowing it to venture into the realm of calculations entailing higher exponents.

Implications and Future Research:

These findings not only contribute to AI's mathematical competencies but also emphasize the need for pioneering new methods, rules, theorems, or formulas to further enhance AI systems like ChatGPT. Future research could explore the development of novel mathematical techniques tailored to AI systems, thus expanding their capabilities across diverse problem-solving domains.

The fast development of social media and online marketing brings a lot of benefits to business. In marketing, the use of controversial advertisements has increased in the last 2 decades. However, it is not clear whether consumers hold positive or negative attitudes toward controversial advertisements. Although the consumers’ attitudes toward competing brands are important determinants of their buying decisions, provocative messages could cause negative attitudes and negatively affect purchase decisions. While the issue of controversial or offensive advertising has been raised in Western countries, but there is a lack of study in this topic in developing countries like Vietnam. This study focuses on exploring the factors affecting to the attitude toward controversial advertising of consumers about FMCG products in Vietnam and suggesting solutions for applying controversial advertising in the Vietnamese context. Based on the related research, a research model is proposed including five variables: general beliefs, positive emotions, negative emotions, affective attitudes, cognitive attitudes. Through a survey of 286 valid samples, the research model has been tested using SEM/AMOS. The results show that all 6 hypotheses are supported. The research gives meaningful insights for any FMCG brand when it comes to deciding on controversial advertising strategy in Vietnamese market.

One very essential tool for the success of any business operation is a good knowledge of the consumer behaviour of its target. Consumer behaviour encompasses the various actions, thoughts, and emotions that impact individuals’ decisions related to the purchase, usage, and disposal of goods and services in their daily lives. While existing literature extensively covers consumer decision-making processes, there is a noticeable lack of information on the factors influencing the decision-making processes of university students, particularly in a developing country like Ghana. To address this gap, the present study investigates the factors influencing the decisions of University of Cape Coast (UCC) students when choosing specific fast-food delivery vendors. Utilizing one-on-one interviews, a purposive sample of 12 University of Cape Coast students was selected to gather their perspectives. The findings from these interviews indicate that the primary factors influencing students’ choices to engage with fast-food delivery services are convenience/proximity, timely delivery, and the taste of the food. Additionally, sub-categories such as the quantity of food, packaging, and the appearance of delivery motor riders were identified. The outcomes of this study have implications for marketing communications, highlighting the importance of addressing factors like convenience, timely delivery, and taste to effectively engage university students in the context of fast-food delivery services.

Information and Communication Technologies over the past decades have enhanced University Teachers’ ability to provide effective and prompt teaching and learning. Therefore, this study explored University Teachers' behaviour intentions toward the use of digital technologies for teaching and learning in higher educational institutions in Ghana. We grounded our study on the Unified Theory of Acceptance and Use of Technology (UTAUT) by testing the contributions of two key variables, the Cost of Internet Data and the Cost of Smart Phones to predict Behaviour Intentions (BI) of university teachers in three higher educational institutions towards the use of Digital Technologies (DTs) for online teaching and learning. We applied Partial Least Square Structural Equation Modelling for data analysis. Hypotheses testing on how the Cost of Internet Data and the Cost of Smartphones influence university teachers' Behaviour Intentions (BI) toward the use of Digital Technologies (DTs) were supported. The findings of our study further showed that university teachers’ intentions to use DTs are influenced by determinants such as social influence, personal experience, and facilitating conditions. The study concludes that the polarity in the findings could help the university authorities to understand the factors to consider in selecting appropriate digital technologies for teaching and learning in universities. The findings from this study are a template for University teachers to get governments to change policies that affect the introduction of digital technologies in higher educational institutions.