The recent release of ChatGPT has gained huge attention and discussion worldwide, with responsible AI being a crucial topic of discussion. One key question is how we can ensure that AI systems, like ChatGPT, are developed and adopted in a responsible way? To tackle the responsible AI challenges, various ethical principles have been released by governments, organisations, and companies. However, those principles are very abstract and not practical enough. Further, significant efforts have been put on algorithm-level solutions that only address a narrow set of principles, such as fairness and privacy. To fill the gap, we adopt a pattern-oriented responsible AI engineering approach and build a Responsible AI Pattern Catalogue to operationalise responsible AI from a system perspective. In this article, we first summarise the major challenges in operationalising responsible AI at scale and introduce how we use the Responsible AI Pattern Catalogue to address those challenges. We then examine the risks at each stage of the chatbot development process and recommend pattern-driven mitigations to evaluate the the usefulness of the Responsible AI Pattern Catalogue in a real-world setting.

With the continuous advancement of Industry 4.0 and intelligent manufacturing, remaining useful lifetime (RUL) prediction can forecast the future degradation state of machinery and then estimate the remaining service time before it loses its safe operation ability. Accordingly, a series of predictive maintenance strategies can be regulated in advance for equipment in the Industrial Internet of Things. To tackle the challenges of insufficiency of failure data and lack of confidence in RUL prediction results, a similarity-based relevance vector machine (SRVM) is proposed in this article. Primarily, the relationship among latent variables in the SRVM is learned adaptively through similarity computations to fully utilize the limited degradation data. Furthermore, these internal variables in the SRVM are treated as time-varying variables and re-estimated dynamically to provide RUL prediction with reliable confidence. The experiment results show that the prediction accuracy of the SRVM is higher than that of other baseline methods.

the computing community.

The recent development of Internet of Things (IoT) and Unmanned Aerial Vehicles has revolutionized traditional agriculture with intelligence and automation. In a typical Intelligent Agriculture (IA) ecosystem, massive and real-time data are generated, analyzed, and sent to the Cloud Server (CS) for the purpose of addressing complex agricultural issues, such as yield prediction, water feed calculation, and so on. This helps farmer and associated stakeholders to take correct decision that improves the yield and quality of agricultural product. However, the distributed nature of IA entities and the usage of insecure wireless communication open various challenges related to data sharing, monitoring, storage, and further makes the entire IA ecosystem vulnerable to various potential attacks. In this article, we exploit deep learning and smart contract to propose a new IoT-enabled IA framework for enabling secure data sharing among its various entities. Specifically, first we develop new authentication and key management scheme to ensure secure data transmission in IoT-enabled IA. The encrypted transactions are then used by the CS to analyze and further detect intrusions by a novel deep learning architecture. In CS, the smart contract (SC)-based consensus mechanism is executed on legitimate transactions that verifies and adds the formed blocks into blockchain by a peer-to-peer CSs network. In comparison to existing competing security solutions, a rigorous comparative research demonstrates that the proposed approach provides greater security and more utility characteristics.

Humans interact with the environment using a combination of perception—transforming sensory inputs from their environment into symbols, and cognition—mapping symbols to knowledge about the environment for supporting abstraction, reasoning by analogy, and long-term planning. Human perception-inspired machine perception, in the context of artificial intelligence (AI), refers to large-scale pattern recognition from raw data using neural networks trained using self-supervised learning objectives such as next-word prediction or object recognition. On the other hand, machine cognition encompasses more complex computations, such as using knowledge of the environment to guide reasoning, analogy, and long-term planning. Humans can also control and explain their cognitive functions. This seems to require the retention of symbolic mappings from perception outputs to knowledge about their environment. For example, humans can follow and explain the guidelines and safety constraints driving their decision making in safety-critical applications such as health care, criminal justice, and autonomous driving.