This paper investigates Isaac Asimov’s impact on modern artificial intelligence (AI) and robotics, focusing on how his visionary narratives and Three Laws of Robotics resonate with current technological practices and ethical debates. Analyzing specific predictions from Asimov’s works that have materialized in today’s AI applications, we draw parallels between his fictional insights and real-world technologies from leading tech firms. The study further considers the social implications of AI, including issues of human displacement and trust. We also discuss the progress and challenges in formulating global ethical standards for AI, reflecting on national and international efforts. The analysis highlights Asimov’s lasting influence and the ongoing importance of ethical deliberation in the AI field.
Category: 4/ Fellow”s Projects
Aula Fellow Project
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Parameter efficient fine tuning: A comprehensive analysis across applications
The rise of deep learning has marked significant progress in fields such as computer vision, natural language processing, and medical imaging, primarily through the adaptation of pre-trained models for specific tasks. Traditional fine-tuning methods, involving adjustments to all parameters, face challenges due to high computational and memory demands. This has led to the development of Parameter Efficient Fine-Tuning (PEFT) techniques, which selectively update parameters to balance computational efficiency with performance. This review examines PEFT approaches, offering a detailed comparison of various strategies highlighting applications across different domains, including text generation, medical imaging, protein modeling, and speech synthesis. By assessing the effectiveness of PEFT methods in reducing computational load, speeding up training, and lowering memory usage, this paper contributes to making deep learning more accessible and adaptable, facilitating its wider application and encouraging innovation in model optimization. Ultimately, the paper aims to contribute towards insights into PEFT’s evolving landscape, guiding researchers and practitioners in overcoming the limitations of conventional fine-tuning approaches.
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The Rainbow Economy Model Leads to Holistic Circular Model
The Rainbow Economy Model is a theoretical framework that proposes a holistic and inclusive approach to economic development. This model emphasizes the importance of diversity, equality, and sustainability in driving economic growth and prosperity. It recognizes that a vibrant and resilient economy is built upon a diverse range of industries, businesses, and individuals, each contributing their unique strengths and perspectives. The Rainbow Economy Model promotes the integration of social, environmental, and economic factors in decision-making processes, aiming to create a balanced and equitable society. This research aims to explore the principles and potential implications of the Rainbow Economy Model, assess its feasibility in different contexts, and identify strategies for its implementation. The research adopts mixed methodology to ascertain the holistic sustainability circular model namely the “The Rainbow Economy Model”. The practical, the social implications are quite evident, and the contribution is the data collected for future research and the Rainbow model of Sustainability.
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You are either with us, or against us: the small state of Serbia between domestic ambition and external pressures
This article examines the position of Serbia as a small state in the context of external pressures, largely reflecting an ambition to balance the East and the West. While clearly interested in offers and benefits from collaboration with both geostrategic realms, Serbia’s authorities have always left space for possible alternatives—a trend that is expected to serve power preservation or to inform external players to what extent Serbia is keen on balancing and juxtaposing great powers in the region. While analyzing the limited case of the Covid-19 pandemic and the never-ending case of Kosovo, additionally actualized by the Russo-Ukrainian war, the present study suggests that Serbia is at the crossroads between growing ambitions and the real limitations of what its smallness can achieve. The paper concludes that Serbian foreign policy contains all the prerogatives of movement without a goal, a search for strategic partnerships, but without a coherent political vision—an approach that generates suspicion of being labelled as distracted and unreliable.
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Artificial intelligence skepticism in news production: The case of South Africa’s mainstream news organizations
This chapter demonstrates that the celebratory acceptance of artificial intelligence (AI) appropriation, popular in mainstream scholarly discourses of AI, is often colored by an emerging, strong pushback by skeptical journalists. Using the case of South African journalists, we make two broad but related arguments. First, we argue that skepticism about AI among journalists in South Africa should be linked to the broader debates about the future and purpose of journalism in post-apartheid South Africa. Second, we argue that journalists view themselves as a peculiar community with a specific role of serving democracy—a role that will not sync neatly with AI practices. This chapter contributes to debates on AI and news production practices in less-explored global South contexts.
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Preconference workshop for International AI Policy Conference at MILA.
Aula Fellows presented a pre-workshop on asking hard questions on AI. Participants practiced identifying key chaarceristics of hard questions and discussing them together. During the conference that followed, several participants stood and asked hard questions, and several presenters were very happy to receive support and feedback on their most pernicious problems. We were able to build on existing collaborations and continue with or bridge-building between all the people and the people in decision-making roles on AI in society.
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The Usefulness of Big Data and IoT/AI at Dubai University. Kurdish Studies, 12(2), pp.6198-6220
Digital transformation is disrupting most sectors and most so the education sector. Universities across the world are using technology to reach out to students and to deliver classes remotely enabling students and staff to adopt modern emerging technologies. Dubai University, based in the heart of a technological hub, has the unique opportunity to leverage cutting-edge technologies like Big Data, the Internet of Things (IoT), and Artificial Intelligence (AI) to revolutionize its academic and operational landscape. This research study explores the usefulness of emerging technologies in enhancing Educational Experiences by analyzing Big Data of student learning patterns, engagement levels, and performance to unlock personalized learning pathways, adaptive courseware, and targeted interventions. AI-powered tutoring systems and virtual labs offer immersive and customized learning experiences shortly. IoT sensors can monitor and manage energy consumption, building security, and resource allocation, leading to sustainable and efficient campus operations. AI-powered systems can automate administrative tasks, streamline processes, and provide predictive maintenance for facilities. The main contribution of the study is using PLS-SEM modeling to analyze Big Data enabling researchers to extract insights from vast datasets and make data-driven discoveries. AI-powered tools can aid in research design, data analysis, and scientific simulation, fostering a culture of innovation. This study will employ a mixed-method approach, utilizing quantitative data analysis of existing university data sets and qualitative interviews with stakeholders. The findings will contribute to developing a strategic roadmap for the optimal integration of Big Data, IoT, and AI within Dubai University’s ecosystem. This research aims to position Dubai University as a pioneer in education and innovation, setting a benchmark for higher education institutions in the region and beyond. The study aims to provide insights to empower decision-makers at Dubai University to make well-informed choices regarding the adoption and integration of emerging technologies. The study facilitates strategic planning by comprehensively grasping the challenges and opportunities presented by digital transformation. Moreover, it guides resource allocation and offers recommendations for leveraging data analytics to support students who may be at risk.
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Strengthening Data Protection: Ensuring Privacy and Security for Nigerian Citizens
This Policy Brief examines the existing data protection regime both in Nigeria and globally and suggests ways to improve the data protection efforts in Nigeria. It considers Nigeria’s principal data protection laws, generally applicable across all sectors (including public and private institutions). By examining and juxtaposing some of the exemptions in legislation, an opportunity for abuse of data subjects’ rights may have been inadvertently created by laws that were enacted to do otherwise. This Policy Brief proffers preferable outcomes that may guide engagement with policymakers to rectify this situation.
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The missing voices at AI conferences
Policymaking should be a society-wide effort, including elected officials, government employees, academics, business leaders, civil society groups and individuals. In theory, each of us should be able to participate.
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Travailleurs du savoir, l’IA changera votre quotidien
Les professionnels et les travailleurs du savoir, en particulier ceux qui n’ont pas encore utilisé l’intelligence artificielle (IA), peuvent être enclins à avoir de fausses idées qui masquent l’ampleur des perturbations à venir.
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Unveiling AI Concerns for Sub-Saharan Africa and its Vulnerable Groups
In Sub-Saharan Africa (SSA), artificial intelligence is still in its early stages of adoption. To ensure that the already existing class imbalance in SSA communities does not hinder the realization of the Sustainable Development Goals, such as data security, safety, and equitable access to AI technologies, acceptable reliability measures must be put in place (as policies). This paper identifies some of the vulnerabilities in AI and adds a voice to the risks and ethical concerns surrounding the use of AI and its impact on SSA and its vulnerable groups. Our systematic literature review of related research between January 2014 and June 2024 shows the current state of AI adoption in SSA and the socio-political challenges that impact its development, revealing key concerns in data Governance, safety privacy, educational and skill gaps, socioeconomic impacts, and stakeholder influence on AI adoption in SSA. We propose a framework for designing data governance policies for the inclusive use of AI in SSA.
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BrainStat: A toolbox for brain-wide statistics and multimodal feature associations
Analysis and interpretation of neuroimaging datasets has become a multidisciplinary endeavor, relying not only on statistical methods, but increasingly on associations with respect to other brain-derived features such as gene expression, histological data, and functional as well as cognitive architectures. Here, we introduce BrainStat – a toolbox for (i) univariate and multivariate linear models in volumetric and surface-based brain imaging datasets, and (ii) multidomain feature association of results with respect to spatial maps of post-mortem gene expression and histology, task-based fMRI meta-analysis, as well as resting-state fMRI motifs across several common surface templates. The combination of statistics and feature associations into a turnkey toolbox streamlines analytical processes and accelerates cross-modal research. The toolbox is implemented in both Python and MATLAB, two widely used programming languages in the neuroimaging and neuroinformatics communities. BrainStat is openly available and complemented by an expandable documentation.