Integrating Artificial Intelligence (AI) in industrial systems is one of the emerging trends in the applications of data science.
Once an increasing number of AI agents are deployed in complex cyber-physical systems, a hybrid form of intelligent algorithms is required for advanced analytics. Many industries prioritize human-in-the-loop control systems and prefer the integration of domain-specific human knowledge in AI-enabled decision-making. This often involves combining various types of AI methodologies, such as rule-based systems using domain expertise, classical machine learning (ML), deep learning, physics-based models, and more, to achieve specific objectives.
The integration of these different technologies allows for the development of more robust and versatile systems, optimized to capitalize on the strengths of each approach while mitigating their individual weaknesses. For instance, rule-based systems excel at managing explicit, codified knowledge but often lack flexibility.
On the other hand, machine learning models can adapt and generalize from data, although they often require substantial datasets for training. In a hybrid AI setup, these two methodologies can complement each other. The rule-based component could handle scenarios that are already well-understood, while machine learning could be used for novel or unanticipated situations.
With the recent advancements in language models and generative AI, the landscape is rapidly shifting toward a future where the communication gap between humans and machines could be significantly narrowed. AI will gradually evolve into providing unprecedented capabilities for understanding context, generating human-like text, and providing more intuitive interfaces.
Hybrid AI serves as a bridge that brings together diverse AI methodologies to solve complex problems. In an Industrial Internet of Things (IIoT) or digital twin environment, communication among sensors, control systems, and AI models can generate what’s often called collective intelligence. This is a dynamic form of knowledge that evolves as the components interact and share information. AI-enabled collective intelligence can be the differentiator that sets a highly efficient, self-adaptive system apart from a merely functional one. This is particularly vital in industries where conditions are continually changing, and the cost of errors can be extremely high. This collective intelligence within the domains of IIoT and digital twins will exponentially grow in the near future and often exists in a format that is not inherently accessible to human understanding. This data usually manifests in numerical parameters, matrices, or state values that are critical for AI models and sensors but can be perplexing for human operators. However, industries are adopting a range of methodologies to bridge this gap, ensuring that this complex data landscape can be decoded into actionable insights.
One of the most straightforward methods to interpret this complex data or knowledge is through visualization. In energy management systems (EMS), for instance, real-time dashboards visually display various metrics such as energy consumption patterns, peak load times, and efficiency metrics. Basic tools like heatmaps and graphs act as translators, converting raw, machine-readable data into a format that human operators can understand and act upon for optimizing energy usage.
However, with the advances in ML algorithms, more sophisticated visualization techniques will be needed. For example, the use of natural language generation (NLG), which converts analytical findings into text summaries or reports that are easy for humans to digest. This is especially crucial in sectors like industrial safety, where NLG can transform intricate trading data into straightforward reports. Alerts and notifications are another critical component, providing real-time, contextual information along with recommended actions.
Developing interactive user interfaces that allow exploring some of the relevance of the model inputs and outputs, or gaining visibility into feature importance in complex ML models or attention maps in transformers can provide important insight into the decision-making or knowledge-creation process of AI-enabled systems.
Some of the efforts in explaining the black-box approach in ML (sometimes referred to as XAI) are algorithms like LIME (local interpretable model-agnostic explanations) and SHAP (Shapley Additive Explanations). In the healthcare diagnostics sector, these algorithms can clarify the reasoning behind a particular medical diagnosis made by a ML model. This enhanced transparency allows healthcare providers to trust and better understand the machine-generated diagnosis, bridging the gap between human and machine intelligence.
Knowledge graphs also offer an innovative way to store and display information in an interconnected graph format. In the context of supply chain management, these graphs visually represent the relationships between different suppliers, products, and logistics channels. By understanding these dependencies through knowledge graphs, managers can make more informed and effective decisions.
While the language of AI-enabled knowledge in industrial contexts might initially seem inaccessible to human operators, a variety of the above-mentioned techniques are being employed to make this data interpretable. However, it is important to understand that while the recent advancements offer a spectrum of tools to make this category of emergent knowledge more understandable for human operators, we must acknowledge that a gap remains as some facets of this knowledge will likely stay elusive for human understanding. This can happen due to a range of factors.
The sheer complexity of these large, interconnected systems can make it difficult to simplify or visualize the relationships between variables. These are often multidimensional systems where altering one variable can have a non-linear effect, making it hard for human operators to intuitively understand the full scope. The dynamic nature of this AI-enabled emergent knowledge presents another challenge. This form of intelligence is continually adapting, sometimes at a pace that surpasses human cognition. By the time an operator gets a grasp on the current data or state of the system, new data might have already changed the landscape, requiring a new set of actions or interpretations.
From a data volume standpoint, the expansive amount of data and interactions among semi-automated AI agents in an IIoT environment can easily overwhelm human cognitive capabilities. While ML models can process and analyze large datasets at speeds incomprehensible to humans, operators may find it impossible to absorb and act on this information in real-time.
Additionally, the logic or reasoning of some models, particularly those based on deep learning, is often beyond human interpretation. Even when explanation algorithms like LIME or SHAP are applied, they may not entirely clarify the model’s decision-making process. This is especially challenging in critical applications like healthcare diagnostics or financial trading where understanding the rationale behind decisions is crucial. Some aspects of the innate logic in ML models are often the topic of ethical AI debates.
Given these challenges, it is unlikely that human operators in a smart factory of the future fully comprehend all the knowledge generated by AI. However, this is less a limitation than it is a recognition of the inherent complexities in a data-rich industrial landscape. By understanding these constraints, we can more effectively plan for a future where human and machine intelligence coexist.
To meet the future demands of IIoT, industries need a structured approach to utilizing the collective intelligence generated by AI. This requires adopting a methodical framework that begins with clearly defining the problem and proceeds through stages such as data collection, model development, and continuous refinement. Interpretability is essential when dealing with AI-enabled emergent knowledge, as any misunderstanding could have serious implications. Therefore, models should undergo validation processes to confirm their accuracy and ability to convey complex data in a form that human operators can understand and act upon.
The deployment phase is crucial, involving not just the initiation of the system but also iterative improvements for adapting to changing data landscapes. This adaptability is vital for capturing the fluid intelligence that characterizes modern, data-rich industrial settings. Technologies like edge computing offer promising advancements in real-time data processing, enhancing the speed and efficiency of data interpretation. Automated decision systems add a new layer of capability by allowing for autonomous actions based on the data at hand. Current research in areas like data fusion, multi-agent systems coupled with deep reinforcement learning, and adaptive learning platforms could further improve our ability to interpret and act on this emergent knowledge.
Governments or policymakers can accelerate this transition by allocating research grants and regulatory support, as well as by encouraging public-private partnerships around innovation and responsible AI development in the industrial sectors. While the complexities of AI-enabled collective intelligence and emergent knowledge in industrial IoT systems pose challenges for complete human comprehension, adopting a methodical framework integrated with the latest technologies and research can enable industries to capitalize on this new category of intelligence.
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