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AIXosphere
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@AIXosphere our research is purposeful, forward looking and actionable. We work to meet the needs of our clients. Our research produces unique insights, innovation in new products and services, and custom policy frameworks.
Adaptive cognitive fit: Artificial intelligence augmented management of information facets and representations.
For more information: Link to Paper
Explosive growth in big data technologies and artificial intelligence (AI) applications have led to increasing pervasiveness of information facets and a rapidly growing array of information representations.
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Information facets, such as equivocality and veracity, can dominate and significantly influence human perceptions of information and consequently affect human performance. Extant research in cognitive fit, which preceded the big data and AI era, focused on the effects of aligning information representation and task on performance, without sufficient consideration to information facets and attendant cognitive challenges.
Therefore, there is a compelling need to understand the interplay of these dominant information facets with information representations and tasks, and their influence on human performance. We suggest that artificially intelligent technologies that can adapt information representations to overcome cognitive limitations are necessary for these complex information environments. To this end, we propose and test a novel “Adaptive Cognitive Fit” (ACF) framework that explains the influence of information facets and AI-augmented information representations on human performance. We draw on information processing theory and cognitive dissonance theory to advance the ACF framework and a set of propositions. We empirically validate the ACF propositions with an economic experiment that demonstrates the influence of information facets, and a machine learning simulation that establishes the viability of using AI to improve human performance.
COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification
For more information: Link to Paper
Along with the Coronavirus pandemic, another crisis has manifested itself in the form of mass fear and panic phenomena, fueled by incomplete and often inaccurate information.
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There is therefore a tremendous need to address and better understand COVID-19’s informational crisis and gauge public sentiment, so that appropriate messaging and policy decisions can be implemented. In this research article, we identify public sentiment associated with the pandemic using Coronavirus specific Tweets and R statistical software, along with its sentiment analysis packages. We demonstrate insights into the progress of fear-sentiment over time as COVID-19 approached peak levels in the United States, using descriptive textual analytics supported by necessary textual data visualizations. Furthermore, we provide a methodological overview of two essential machine learning classification methods, in the context of textual analytics, and compare their effectiveness in classifying Coronavirus Tweets of varying lengths. We observe a strong classification accuracy of 91% for short Tweets, with the Naïve Bayes method. We also observe that the logistic regression classification method provides a reasonable accuracy of 74% with shorter Tweets, and both methods showed relatively weaker performance for longer Tweets. This research provides insights into Coronavirus fear sentiment progression, and outlines associated methods, implications, limitations and opportunities.
Fear of Artificial Intelligence? NLP, ML and LLMs Based Discovery of AI-Phobia and Fear Sentiment Propagation by AI News
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Confusion, fear and mixed sentiments prevail in the minds of people towards what is arguably one of the most important of dynamics of modern human society: Artificial Intelligence (AI).
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This study aims to explore the contributions of news media towards this phenomenon – we analyze nearly seventy thousand recent news headlines on AI, using natural language processing (NLP) informatics methods, machine learning (ML) and large language models (LLMs) to draw insights and discover dominant themes. Our theoretical framework was derived from extant literature which posits the power of fear producing articles and news headlines which produce significant impacts on public behavior even when available in small quantities. We applied extensive textual informatics methods using word and phrase frequency analytics, sentiment analysis and human experts based thematic analysis to discover insights on AI phobia inducing news headlines.
Our rigorous analysis of nearly seventy thousand headlines using multiple validation methods in NLP (exploratory informatics including BERT, Llama 2 and Mistral(1; 2) based topic identification), ML (supervised informatics) and LLMs (neural nets for sentiment classification, with BERT, Llama 2 and Mistral) demonstrates the presence of an unreasonable level of emotional negativity and fear inducing verbiage in AI news headlines. The framing of AI as being dangerous or as being an existential threat to humanity can have a profound impact on public perception, and the resulting AI phobia and confusion in public perceptions are inherently detrimental to the science of AI. Furthermore, this can also impact AI policy and regulations, and harm society. We conclude with a discussion deducing implications for society and make recommendations for education and policies that could support human identity and dignity.
Would You Please Like My Tweet?! An Artificially Intelligent, Generative Probabilistic, and Econometric Based System Design for Popularity-Driven Tweet Content Generation
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An understudied area in the field of social media research is the design of decision support systems that can aid the manager by way of automated message component generation.
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Recent advances in this form of artificial intelligence has been suggested to allow content creators and managers to transcend their tasks from creation towards editing, thus overcoming a common problem: the tyranny of the blank screen. In this research, we address this topic by proposing a novel system design that will suggest engagement-driven message features as well as automatically generate critical and fully written unique Tweet message components for the goal of maximizing the probability of relatively high engagement levels. Our multi-methods design relies on the use of econometrics, machine learning, and Bayesian statistics, all of which are widely used in the emerging fields of Business and Marketing Analytics. Our system design is intended to analyze Tweet messages for the purpose of generating the most critical components and structure of Tweets. We propose econometric models to judge the quality of written Tweets by way of engagement-level prediction, as well as a generative probability model for the auto-generation of Tweet messages. Testing of our design demonstrates the need to take into account the contextual, semantic, and syntactic features of messages, while controlling for individual user characteristics, so that generated Tweet components and structure maximizes the potential engagement levels.
Cultivation of human centered artificial intelligence: culturally adaptive thinking in education (CATE) for AI
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Artificial Intelligence (AI) has become ubiquitous in human society, and yet vast segments of the global population have no, little, or counterproductive information about AI.
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It is necessary to teach AI topics on a mass scale. While there is a rush to implement academic initiatives, scant attention has been paid to the unique challenges of teaching AI curricula to a global and culturally diverse audience with varying expectations of privacy, technological autonomy, risk preference, and knowledge sharing. Our study fills this void by focusing on AI elements in a new framework titled Culturally Adaptive Thinking in Education for AI (CATE-AI) to enable teaching AI concepts to culturally diverse learners. Failure to contextualize and sensitize AI education to culture and other categorical human-thought clusters, can lead to several undesirable effects including confusion, AI-phobia, cultural biases to AI, increased resistance toward AI technologies and AI education. We discuss and integrate human behavior theories, AI applications research, educational frameworks, and human centered AI principles to articulate CATE-AI. In the first part of this paper, we present the development a significantly enhanced version of CATE. In the second part, we explore textual data from AI related news articles to generate insights that lay the foundation for CATE-AI, and support our findings. The CATE-AI framework can help learners study artificial intelligence topics more effectively by serving as a basis for adapting and contextualizing AI to their sociocultural needs.
Artificially Intelligent Readers: An Adaptive Framework for Original Handwritten Numerical Digits Recognition with OCR Methods
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Advanced artificial intelligence (AI) techniques have led to significant developments in optical character recognition (OCR) technologies.
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OCR applications, using AI techniques for transforming images of typed text, handwritten text, or other forms of text into machine-encoded text, provide a fair degree of accuracy for general text. However, even after decades of intensive research, creating OCR with human-like abilities has remained evasive. One of the challenges has been that OCR models trained on general text do not perform well on localized or personalized handwritten text due to differences in the writing style of alphabets and digits. This study aims to discuss the steps needed to create an adaptive framework for OCR models, with the intent of exploring a reasonable method to customize an OCR solution for a unique dataset of English language numerical digits were developed for this study. We develop a digit recognizer by training our model on the MNIST dataset with a convolutional neural network and contrast it with multiple models trained on combinations of the MNIST and custom digits. Using our methods, we observed results comparable with the baseline and provided recommendations for improving OCR accuracy for localized or personalized handwritten text. This study also provides an alternative perspective to generating data using conventional methods, which can serve as a gold standard for custom data augmentation to help address the challenges of scarce data and data imbalance.
Feeling Positive About Reopening? New Normal Scenarios From COVID-19 US Reopen Sentiment Analytics
For more information: Link to Paper
The Coronavirus pandemic has created complex challenges and adverse circumstances.
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This research identifies public sentiment amidst problematic socioeconomic consequences of the lockdown, and explores ensuing four potential public sentiment associated scenarios. The severity and brutality of COVID-19 have led to the development of extreme feelings, and emotional and mental healthcare challenges. This research focuses on emotional consequences – the presence of extreme fear, confusion and volatile sentiments, mixed along with trust and anticipation. It is necessary to gauge dominant public sentiment trends for effective decisions and policies. This study analyzes public sentiment using Twitter Data, time-aligned to the COVID-19 reopening debate, to identify dominant sentiment trends associated with the push to reopen the economy. Present research uses textual analytics methodologies to analyze public sentiment support for two potential divergent scenarios – an early opening and a delayed opening, and consequences of each. Present research concludes on the basis of textual data analytics, including textual data visualization and statistical validation, that tweets data from American Twitter users shows more positive sentiment support, than negative, for reopening the US economy. This research develops a novel sentiment polarity based public sentiment scenarios (PSS) framework, which will remain useful for future crises analysis, well beyond COVID-19. With additional validation, this research stream could present valuable time sensitive opportunities for state governments, the federal government, corporations and societal leaders to guide local and regional communities, and the nation into a successful new normal future.
The Critical Need for Transparency and Regulation amidst the Rise of Powerful Artificial Intelligence Models
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As artificial intelligence (AI) technologies cross over a vital threshold of competitiveness with human intelligence, it is necessary to properly frame critical questions in the service of shaping policy and governance while sustaining human values and identity.
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Given AI’s vast socioeconomic implications, government actors and technology creators must proactively address the unique and emerging ethical concerns that are inherent to AI’s many uses.
AI can be viewed as an adaptive “set of technologies that mimic the functions and expressions of human intelligence, specifically cognition, and logic.” In the AI field, foundation models (FMs) are more or less what they sound like: large, complex models that have been trained on vast quantities of digital general information that may then be adapted for more specific uses. Two notable features of foundation models include a propensity to gain new and often unexpected capabilities as they increase in scale (“emergence”), and a growing predisposition to serve as a common “intelligence base” for differing specialized functions and AI applications (“homogenization”). Large language models (LLMs) that power applications like ChatGPT are foundation models with a focus on modeling human language, knowledge, and logic. Advanced AIs and foundation models have the potential to replace multiple task-specific or narrow AIs due to their scale and flexibility, which increases the risk of a few powerful persons or entities who control these advanced AIs gaining extraordinary socioeconomic power, creating conditions for mass exploitation and abuse.
A New Era of Artificial Intelligence Begins – Where Will it Lead Us?
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In this Editorial, we highlight the emerging dominance of AI + Big Data, and here are some excerpts : We have entered into the age of Artificial Intelligence (AI).
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Everything around us is becoming artificially intelligent: from business applications to healthcare, education to finance and governance to art, music and entertainment. The fact that AI has gripped public attention is evident from the steep rise in public engagement with artificial intelligence applications, explosive increase in news media coverage of AI, increasing volumes of social media posts and the mushrooming of a range of AI ecosystem initiatives. We at JBDAI (formerly JBDTP) hope to encourage and foster much high quality research, rigor and innovative thought leadership on big data and artificial intelligence in the years ahead, supporting human well-being, the sustainability of our natural resources and balanced societal progress – please contribute to JBDAI and be a part of this exciting intellectual adventure!
Unlocking the Potential of AI: Our research extends to additional areas, including AI for agriculture and human-centric robotics!