Daswin De Silva’s research while affiliated with La Trobe University and other places

The rapid adoption of artificial intelligence (AI) systems, such as predictive AI, generative AI, and explainable AI, is in contrast to the slower development and uptake of robotic AI systems. Dynamic environments, sensory processing, mechanical movements, power management, and safety are inherent complexities of robotic intelligence capabilities that can be addressed using novel AI approaches. The current AI landscape is dominated by machine learning techniques, specifically deep learning algorithms, that have been effective in addressing some of these challenges. However, these algorithms are subject to computationally complex processing and operational needs such as high data dependency. In this paper, we propose a computation-efficient and data-efficient framework for robotic motion intelligence (RMI) based on vector symbolic architectures (VSAs) and blockchain-based smart contracts. The capabilities of VSAs are leveraged for computationally efficient learning and noise suppression during perception, motion, movement, and decision-making tasks. As a distributed ledger technology, smart contracts address data dependency through a decentralized, distributed, and secure transactions ledger that satisfies contractual conditions. An empirical evaluation of the framework confirms its value and contribution towards addressing the practical challenges of robotic motion intelligence by significantly reducing the learnable parameters by 10 times while preserving sufficient accuracy compared to existing deep learning solutions.

Generative artificial intelligence (Generative AI) is transforming the way we live and work. Following several decades of artificial narrow intelligence, Generative AI is signaling a paradigm shift in the intelligence of machines, an increased generalization capability with increased accessibility and equity for nontechnical users. Large language models (LLMs) are leading this charge, specifically conversational interfaces, such as ChatGPT, Gemini, Claude, and Llama (large language model meta AI). Besides language and text, robust and effective Generative AI models have emerged for all other modalities of digital data, image, video, audio, code, and combinations thereof. This article presents the opportunities and challenges of Generative AI in advancing industrial systems and technologies. The article begins with an introduction to Generative AI, which includes its rapid progression to state-of-the-art, the deep learning algorithms, large training datasets, and computing infrastructure used to build Generative AI models, as well as the technical limitations. The contribution, value, and utility of Generative AI is presented in terms of its four capabilities of accelerating academic research, augmenting the learning and teaching experience, supporting industry practice, and increasing social impact. The article concludes with an expeditious message to the academic research and industry ­practitioner communities to invest time and effort in the training, adoption, and application of Generative AI, with consideration for AI literacy for all stakeholders, human-centricity, and the responsible development and use of AI in industrial settings.

Highlights What are the main findings? Smart Cities as Hyper-Connected Digital Environments generate large and diverse data streams and repositories that do not consistently translate into insights and decisions. A Responsible AI Hyper-Automation framework with Generative AI agents is developed and evaluated to address these complex challenges. What are the implications of the main findings? The developed AI framework is effective when grounded on five core technical capabilities with an independent cognitive engine for hyper-automated agentic AI that feeds into human-in-the-loop processes. The framework provides a prototypical setting for university cities of the future to provide direction, guidance, and standards for sustainable and safe smart cities of the future. Abstract Smart cities are Hyper-Connected Digital Environments (HCDEs) that transcend the boundaries of natural, human-made, social, virtual, and artificial environments. Human activities are no longer confined to a single environment as our presence and interactions are represented and interconnected across HCDEs. The data streams and repositories of HCDEs provide opportunities for the responsible application of Artificial Intelligence (AI) that generates unique insights into the constituent environments and the interplay across constituents. The translation of data into insights poses several complex challenges originating in data generation and then propagating through the computational layers to decision outcomes. To address these challenges, this article presents the design and development of a Hyper-Automated AI framework with Generative AI agents for sustainable smart cities. The framework is empirically evaluated in the living lab setting of a ‘University City of the Future’. The developed AI framework is grounded on the core capabilities of acquisition, preparation, orchestration, dissemination, and retrospection, with an independent cognitive engine for hyper-automation of these AI capabilities using Generative AI. Hyper-automation output feeds into a human-in-the-loop process prior to decision-making outcomes. More broadly, this framework aims to provide a validated pathway for university cities of the future to take up the role of prototypes that deliver evidence-based guidelines for the development and management of sustainable smart cities.

Artificial Intelligence (AI) is transforming the way we live and work. The disruptive impact and risks of Generative AI have accelerated the global transition from voluntary AI ethics guidelines to mandatory AI regulation. The European Union AI Act is the world’s first horizontal and standalone law governing AI that came into force in August 2024, just as other jurisdictions, countries and states, are navigating possible modes of regulation. Starting with the EU AI Act, most of the current regulatory effort follows a risk-based classification approach. While this is prescriptive and application-focused, it overlooks the complex circular impacts of AI and the inherent limitations of measurement of risk, overemphasis on high-risk classification, perceived trustworthiness of AI and the geopolitical power imbalance of AI. This article contributes an overview of the current landscape of AI regulation, followed by a detailed assessment of the limitations and potential means of addressing these limitations through a structuration theory approach. Summarily, this approach can be used to recognise AI systems as agents that actively participate in the duality of structure, and the subsequent shaping of society. It acknowledges the direct negotiation of agency granted to machines alongside their ability to determine an understanding from given inputs, which then qualifies AI as an active participant in the recursive structuration of society. This agentic view of AI in the structuration theory approach complements ongoing efforts to develop a comprehensive and balanced AI regulation.

Text classifiers are Artificial Intelligence (AI) models used to classify new documents or text vectors into predefined classes. They are typically built using supervised learning algorithms and labelled datasets. Text classifiers produce a predefined class as an output, which also makes them susceptible to adversarial attacks. Text classifiers with high accuracy that are trained using complex deep learning algorithms are equally susceptible to adversarial examples, due to subtle differences that are indiscernible to human experts. Recent work in this space is mostly focused on improving adversarial robustness and adversarial example detection, instead of detecting adversarial attacks. In this paper, we propose a novel approach, explainable AI with integrated gradients (IGs) for the detection of adversarial attacks on text classifiers. This approach uses IGs to unpack model behavior and identify terms that positively and negatively influence the target prediction. Instead of random substitution of words in the input, we select the top p% words with the greatest positive and negative influence as substitute candidates using attribution scores obtained from IGs to generate k samples of transformed inputs by replacing them with synonyms. This approach does not require changes to the model architecture or the training algorithm. The approach was empirically evaluated on three benchmark datasets, IMDB, SST-2, and AG News. Our approach outperforms baseline models on word substitution rate, detection accuracy, and F1 scores while maintaining equivalent detection performance against adversarial attacks.

Despite gender diversity being driven by institutional bodies, companies have been relatively slow to diversify. In this study, it is investigated that how Australian boards select new directors, and how those selection processes affect their recruitment of women. In‐depth interviews are conducted of those with first‐hand experience of board appointments, followed by the thematic analysis and the application of natural language processing techniques to identify emotions and sentiment associated with these themes. The findings indicate that boards are adopting a social rather than rational approach to board selection. They are using networks, recruitment agencies, skills matrices and pools which on the surface appear to broaden the diversity of board members. But if they are not actively seeking gender diversity these methods can still limit diversity. For women, the lack of progress and barriers of access are resulting in high intensity of negative emotions. A key contribution of the research is the intersection of social approaches to board appointment and social identity theory with the dynamics of gender. Boards need to prioritize diversity for it to be achieved. There is a need for more active methods of recruitment and expansion of the networks and pools where directors are traditionally sought. Institutions can drive change through increasing targets and requiring enhanced reporting.

Highlights What are the main findings? The proposed AI approach is effective at hypervector approximation of complex manifolds in smart city settings. The Hyperseed algorithm can generate fine-grained local variations that can be tracked for anomalies and temporal changes, as well as incremental changes in dynamic data streams. What is the implication of the main finding? This approach can be integrated into AI digital twins that have to process complex manifolds of high-dimensional datasets and data streams generated by smart cities. The interplay between digital twins and novel AI approaches is crucial in unpacking the complexities of urban systems and shaping sustainable and resilient smart cities. Abstract The United Nations Sustainable Development Goal 11 aims to make cities and human settlements inclusive, safe, resilient and sustainable. Smart cities have been studied extensively as an overarching framework to address the needs of increasing urbanisation and the targets of SDG 11. Digital twins and artificial intelligence are foundational technologies that enable the rapid prototyping, development and deployment of systems and solutions within this overarching framework of smart cities. In this paper, we present a novel AI approach for hypervector approximation of complex manifolds in high-dimensional datasets and data streams such as those encountered in smart city settings. This approach is based on hypervectors, few-shot learning and a learning rule based on single-vector operation that collectively maintain low computational complexity. Starting with high-level clusters generated by the K-means algorithm, the approach interrogates these clusters with the Hyperseed algorithm that approximates the complex manifold into fine-grained local variations that can be tracked for anomalies and temporal changes. The approach is empirically evaluated in the smart city setting of a multi-campus tertiary education institution where diverse sensors, buildings and people movement data streams are collected, analysed and processed for insights and decisions.