Global AI Research Articles

Global AI Regulatory Landscape Challenges, Trends, and Future Outlook

Global AI Regulatory Landscape Challenges, Trends, and Future Outlook

Assessing impact of borehole field data’s input parameters on hybrid deep learning models for heating and cooling forecasting: A local and global explainable AI analysis

Achieving accurate performance forecasting of borehole heat exchanger is essential for optimizing ground source heat pump systems, enabling optimal control, and facilitating energy-efficient operations with enhanced sustainability of the built environment. This study aims to investigate and quantify the impact of model architecture, the number of input data sensors, and their accurate identification on multivariate hybrid deep learning models. Moreover, the significance of incorporating a recent development in deep learning to pay selective attention to the input data i.e., attention-based mechanisms in LSTM-CNN and CNN-LSTM architectures is also investigated. The significance of input parameters for the data-driven AI models is assessed through a significance interpretability analysis utilizing Explainable-AI local-method, namely Shapley Additive Explanations and global-explanation methods i.e., permutation feature importance method and Friedman statistical test. The findings highlight the efficacy of attention mechanisms in capturing temporal dependencies in LSTM-CNN-At and spatial patterns in CNN-LSTM-At, may not necessarily enhance their multistep forecasting capabilities for the borehole field data in comparison to LSTM-CNN architecture. The 24 hours ahead forecasting results show that the order of accuracy is LSTM-CNN> LSTM-CNN-At> CNN-LSTM> CNN-LSTM-At. The findings emphasize that by carefully designing the model layers, it is feasible to remove redundant borehole field sensors for data measurement while maintaining the forecasting accuracy of the hybrid data-driven models.

Dynamically engineered multi-modal feature learning for predictions of office building cooling loads

This paper reports a new knowledge-driven engineered feature learning approach in response to the Global AI Challenge for Building E&M Facilities held by the Electrical and Mechanical Service Department (EMSD) of the Hong Kong SAR. The results were awarded with a Grand Prize by the competition organizer. A dynamically engineered multi-modal feature learning (DEMMFL) method is proposed for predicting the cooling load of two office buildings. The DEMMFL model is estimated with the Lasso-ridge regression and compared with other well-known methods such as the Lasso. The novel approach applies control system knowledge to engineer useful features and explore load patterns for multi-mode modeling. Deep learning methods including LSTM, GRU, and AutoGluon are implemented for automated machine learning and tested in parallel to compare the performance of the proposed model with existing methods. The proposed model is demonstrated to predict long-term cooling load most accurately using engineered features from weather information only.

Low-Latency Collaborative Predictive Maintenance: Over-the-Air Federated Learning in Noisy Industrial Environments.

The emergence of Industry 4.0 has revolutionized the industrial sector, enabling the development of compact, precise, and interconnected assets. This transformation has not only generated vast amounts of data but also facilitated the migration of learning and optimization processes to edge devices. Consequently, modern industries can effectively leverage this paradigm through distributed learning to define product quality and implement predictive maintenance (PM) strategies. While computing speeds continue to advance rapidly, the latency in communication has emerged as a bottleneck for fast edge learning, particularly in time-sensitive applications such as PM. To address this issue, we explore Federated Learning (FL), a privacy-preserving framework. FL entails updating a global AI model on a parameter server (PS) through aggregation of locally trained models from edge devices. We propose an innovative approach: analog aggregation over-the-air of updates transmitted concurrently over wireless channels. This leverages the waveform-superposition property in multi-access channels, significantly reducing communication latency compared to conventional methods. However, it is vulnerable to performance degradation due to channel properties like noise and fading. In this study, we introduce a method to mitigate the impact of channel noise in FL over-the-air communication and computation (FLOACC). We integrate a novel tracking-based stochastic approximation scheme into a standard federated stochastic variance reduced gradient (FSVRG). This effectively averages out channel noise's influence, ensuring robust FLOACC performance without increasing transmission power gain. Numerical results confirm our approach's superior communication efficiency and scalability in various FL scenarios, especially when dealing with noisy channels. Simulation experiments also highlight significant enhancements in prediction accuracy and loss function reduction for analog aggregation in over-the-air FL scenarios.

Comparison of Rationales and Approaches of Artificial Intelligence Regulatory Policies in the U.S, EU and China Draws Recommendations on a 'Fit for Purpose' Global AI Governance Mechanism

With the rapid development of AI technologies, our society is facing a global AI regulatory dilemma [1]., with a wide spectrum of AI regulatory rules not only confusing but also incurring very high costs for the participants of the AI industry [2].

A Study of the Revolution in Digital Banking and it’s Connection with Artificial Intelligence

This paper analyzes the performance of Indian banks over the period of the last five year. The paper also conducts an empirical analysis the connection between digital banking and artificial intelligence. In addition it also finds Challenges in Adopting AI in banking industry. The study find that due to the concerted efforts of the entire government and all interested parties, the number of digital payment transactions has greatly increased, rising from 2,071 crore in FY 2017–18 to 8,840 crore in FY 2021–22. With regard to the AI Due to the numerous advantages of the technology, AI and banking go hand in hand. According to a McKinsey analysis on a global AI survey, 60% of financial services organizations have at least one AI capability in place to speed up business operations

Human-First, Please: Assessing Citizen Views and Industrial Ambition for Emotional AI in Recommender Systems

This paper qualitatively explores the views of diverse members of the British public on applications of biometric emotional AI technologies patented by two globally dominant consumer-facing recommender systems, Amazon and Spotify. Examining Amazon and Spotify patents for biometric profiling of users’ emotions, disposition, and behaviour to offer them tailored services, ads, and products from their wider platforms, this paper points to industrial ambition regarding emotional AI. Little is known about ordinary people’s views on deployment of such technology, and given the complex, abstract, and future-facing nature of such technologies, ascertaining informed lay views is hard. We address this through our innovative, qualitative study of diverse British-based adults (n=46) that presents to them near-horizon use cases in an interactive fictional narrative that deploys design fiction principles and ContraVision techniques. We find the themes of “usefulness,” “resignation,” “uneasy terms of engagement,” and “human-first,” adding rich and nuanced insights to prior survey work on users’ views towards biometric-based emotional AI technologies. In contributing to a richer understanding of whether emotional AI technologies should be deployed in consumer-facing recommender systems, and if so, on what terms, we find that well-established policy-friendly criticisms apply to global emotional AI recommender systems. We conclude, however, that problems of alienation and need for a human-first approach to emerging AI technology are the most significant criticisms.

The application of chatbot in gastroenterology nursing

ObjectiveThe advent of large language models has triggered a wave of technological advancements in the global AI dialogue system, which has been widely adopted in various fields including medical care. This research aims to investigate the potential of chatbots in the field of gastroenterology nursing. MethodsTwo nurses compiled and categorized 20 relevant questions related to gastroenterology nursing, grouping them into four modules. Two chatbot-based AI language models were selected to answer all the questions. The satisfaction levels and satisfaction rates for each module were analyzed to evaluate the performance of the two chatbots. ResultsChatbot A received an overall satisfaction rate of 85% (with 9 very satisfied, 8 satisfied, and 3 dissatisfied responses), while chatbot B had a lower satisfaction rate of 45% (with 0 very satisfied, 9 satisfied, and 11 dissatisfied responses). The satisfaction rates for module 1 (pre-hospital care) were 60% for chatbot A and 20% for chatbot B. In module 2 (health education during hospitalization), chatbot A's satisfaction rate was 100%, while chatbot B's satisfaction was only 60%. For module 3 (continuing care after discharge), chatbot A's satisfaction rate was 100%, while chatbot B's was 40%. Finally, in module 4 (nursing management), chatbot A received an 80% satisfaction rate, compared to chatbot B's 60% satisfaction rate. ConclusionThe performance of chatbot in terms of health education and nursing management for patients during hospitalization is acceptable; Further optimization is needed in terms of pre hospitalization nursing interventions and post discharge continuity care. The performance of different chatbots varies, and intelligent large models need to be tailored to the medical or nursing fields to better apply in the field of digestive disease care.

1823-PUB: Expanded Dataset Improves Accuracy of Wrist-Worn Noninvasive BGM Using AI

Background and Aims: To determine accuracy, safety and specificity of a novel non-invasive wrist-worn continuous BGM device which analyses resonance shifts in the microwave spectrum using AI. We present results from an ongoing study in patients with T1D and T2D from an expanded dataset (cohorts 1-3). Methods: In this open, pilot, adaptive design study, subjects (N=5/cohort) attended 4 test occasions (n=2/session), each ≤7 days apart. Devices automatically collected data every 60 secs for 500 msec over 3 hours/session, with plasma glucose measured every 5 mins. A global AI model was evaluated by MARD using venous blood glucose. Interim results from every 5 completed patients informed the next device iteration with 10 iterations possible across the study. Results: Data from each cohort was used to train a neural net algorithm to predict a new trial. Each cohort improved overall MARD prediction accuracy. All analyses followed a leave-one-trial-out methodology where the data for the omitted trial was predicted for each analysis cycle. Using the first cohort, the MARD was 21%, adding the second reduced MARD to 15% and with all 3 cohorts the predictive MARD was 13%. Improvement in the SEG plots showed data falling into low risk SEG categories. Conclusions: We have shown that by giving the neural network an expanded dataset, the MARD decreased to nearer commercially available minimally-invasive BGMs by 38% from a predictive MARD of 21% to 13%. Disclosure M.S.Chaudhry: None. G.J.Dunseath: None. J.Ryan: None. L.Barlow: None. I.C.Carrillo masso: Employee; Afon Technology Ltd. J.H.Crane: None. M.R.A.Qureshi: Employee; Afon Technology Ltd. S.C.Bain: Advisory Panel; Novo Nordisk, Sanofi, Lilly, Boehringer Ingelheim Inc., AstraZeneca. S.D.Luzio: None. C.M.Handy: None. B.Love: Consultant; Afon Technologies LTD. N.M.M.Silva: None. L.M.Ferreira: None. K.Wareham: None.

Navigating the Ethical Challenges of Artificial Intelligence in Higher Education: An Analysis of Seven Global AI Ethics Policies

AI use in higher education raises ethical concerns that must be addressed. Biased algorithms pose a significant threat, especially if used in admission or grading processes, as they could have devastating effects on students. Another issue is the displacement of human educators by AI systems, and there are concerns about transparency and accountability as AI becomes more integrated into decision-making processes. This paper examined three AI objectives related higher education: biased algorithms, AI and decision-making, and human displacement. Discourse analysis of seven AI ethics policies was conducted, including those from UNESCO, China, the European Commission, Google, MIT, Sanford HAI, and Carnegie Mellon. The findings indicate that stakeholders must work together to address these challenges and ensure responsible AI deployment in higher education while maximizing its benefits. Fair use and protecting individuals, especially those with vulnerable characteristics, are crucial. Gender bias must be avoided in algorithm development, learning data sets, and AI decision-making. Data collection, labeling, and algorithm documentation must be of the highest quality to ensure traceability and openness. Universities must study the ethical, social, and policy implications of AI to ensure responsible development and deployment. The AI ethics policies stress responsible AI development and deployment, with a focus on transparency and accountability. Making AI systems more transparent and answerable may reduce the adverse effects of displacement. In conclusion, AI must be considered ethically in higher education, and stakeholders must ensure that AI is used responsibly, fairly, and in a way that maximizes its benefits while minimizing its risks.

Crafting an AI Compass: The Influence of Global AI Standards on Firms

Crafting an AI Compass: The Influence of Global AI Standards on Firms

Global AI governance: barriers and pathways forward

Global AI governance: barriers and pathways forward

A Federated Learning Framework for Enforcing Traceability in Manufacturing Processes

The plethora of available data in various manufacturing facilities has boosted the adoption of various data analytics methods, which are tailored to a wide range of operations and tasks. However, fragmentation of data, in the sense that chunks of data could possibly be distributed in geographically sparse areas, hampers the generation of better and more accurate intelligent models that would otherwise benefit from the larger quantities of available data which are derived from various operations taking place at different locations of a manufacturing process. Moreover, in regulated industrial sectors, such as in the medical and the pharmaceutical fields, sector-specific legislation imposes strict criteria and rules for the privacy, maintenance and long-term storage of data. Process reproducibility is often an essential requirement in these regulated industrial sectors, and this issue could be supported by AI models which can be applied to enforce traceability, auditability and integrity of every initial, intermediate and final piece of data used during the AI model training process. In this respect, blockchain technologies could be potentially also useful for enabling and enforcing such requirements. In this paper, we present a multi-blockchain-based platform integrated with federated learning functionalities to train global AI (deep learning) models. The proposed platform maintains an audit trail of all information pertaining the training process using a set of blockchains in order to ensure the training process’s immutability. The applicability of the proposed framework has been validated on three tasks by applying three state-of-the-art federated learning algorithms on an industrial pharmaceutical dataset based on two manufacturing lines, achieving promising in terms of both generalizability and convergence time.

Decolonizing global AI governance: Assessment of the state of decolonized AI governance in Sub-Saharan Africa

Decolonizing global AI governance: Assessment of the state of decolonized AI governance in Sub-Saharan Africa

Artificial Intelligence and the Political Legitimacy of Global Governance

Although the concept of “AI governance” is frequently used in the debate, it is still rather undertheorized. Often it seems to refer to the mechanisms and structures needed to avoid “bad” outcomes and achieve “good” outcomes with regard to the ethical problems artificial intelligence is thought to actualize. In this article we argue that, although this outcome-focused view captures one important aspect of “good governance,” its emphasis on effects runs the risk of overlooking important procedural aspects of good AI governance. One of the most important properties of good AI governance is political legitimacy. Starting out from the assumptions that AI governance should be seen as global in scope and that political legitimacy requires at least a democratic minimum, this article has a twofold aim: to develop a theoretical framework for theorizing the political legitimacy of global AI governance, and to demonstrate how it can be used as a compass for critially assessing the legitimacy of actual instances of global AI governance. Elaborating on a distinction between “governance by AI” and “governance of AI” in relation to different kinds of authority and different kinds of decision-making leads us to the conclusions that much of the existing global AI governance lacks important properties necessary for political legitimacy, and that political legitimacy would be negatively impacted if we handed over certain forms of decision-making to artificial intelligence systems.

“Autonomous weapons” as a geopolitical signifier in a national power play: analysing AI imaginaries in Chinese and US military policies

“Autonomous weapon systems” (AWS) have been subject to intense discussions for years. Numerous political, academic and legal actors are debating their consequences, with many calling for strict regulation or even a global ban. Surprisingly, it often remains unclear which technologies the term AWS refers to and also in what sense these systems can be characterised as autonomous at all. Despite being feared by many, weapons that are completely self-governing and beyond human control are more of a conceptual possibility than an actual military reality.As will be argued, the conflicting interpretations of AWS are largely the result of the diverse meanings that are constructed in political discourses. These interpretations convert specific understandings of AI into strategic assets and consequently hinder the establishment of common ethical standards and legal regulations. In particular, this article looks at the publicly available military AI strategies and position papers by China and the USA. It analyses how AWS technologies, understood as evoking sociotechnical imaginaries, are politicised to serve particular national interests.The article presents the current theoretical debate, which has sought to find a functional definition of AWS that is sufficiently unambiguous for regulatory or military contexts. Approaching AWS as a phenomenon that is embedded in a particular sociotechnical imaginary, however, flags up the ways in which nation states portray themselves as part of a global AI race, competing over economic, military and geopolitical advantages. Nation states do not just enforce their geopolitical ambitions through a fierce realpolitik rhetoric but also play around with ambiguities in definitions. This especially holds true for China and the USA, since they are regarded and regard themselves as hegemonic antagonists, presenting competing self-conceptions that are apparent in their histories, political doctrines and identities. The way they showcase their AI-driven military prowess indicates an ambivalent rhetoric of legal sobriety, tech-regulation and aggressive national dominance. AWS take on the role of signifiers that are employed to foster political legitimacy or to spark deliberate confusion and deterrence.

Forgotten African AI Narratives and the future of AI in Africa


 
 
 Ancient and contemporary imaginative thoughts, stories, literary works, and beliefs about intelligent machines or otherwise known as AI narratives influence the development, implementation and governance of AI. Responsible AI therefore requires the understanding of these narratives. However, in the global AI narratives discourse, narratives of AI from Africa are missing or are often forgotten. Potentially, this has implications for how AI is or will be designed, deployed and regulated in Africa. This paper presents insights into our understanding of the reasons why Africa’s AI narratives are often missing, the implications this has for the future of AI in Africa, how the situation can be improved and the path to take to achieve responsible AI in Africa. These insights emerged following a workshop organized at Mozilla Festival 2021 and demonstrates the growing need to explore uncovered AI narratives in Africa to ensure better AI outcomes.
 
 

Appreciating Brazilian Virtues in Flourishing Positive Actions

With Vânia Bueno’s invitation to co-create a meeting in Brazil, we started to think about what was wanting to happen that would integrate with the proposals for the 2022 Global AI Jam on the topic of thriving together, and at the same time would appeal to the Brazilian public.

Mixing Old Ingredients to Create a New Model for ‘Glocal’ Appreciative Inquiry Meetings

It was especially this wish to meet face-to-face again at what appears to be the end of the Covid 19 pandemic, at least in some parts of the world, that led to the idea of sending an invite to the European network to investigate the possibility of combining attendance of the Global AI Jam with a real face-to-face meeting.

Experiences of and Reflections on the 2022 Global AI Jam

The preparation for this year’s Jam took energy and enthusiasm on different fronts. As host of several sessions, I met with each of my co-facilitators beforehand.