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Is Your Data Ready to Support Your AI Efforts?
Mounir Adada, Eclipse Mining Technologies, USA, assesses the importance of advanced knowledge systems to help mining companies unlock the full potential of AI technologies.
In today’s rapidly changing technological landscape, artificial intelligence (AI) has emerged as a game-changing force, reshaping industries and redefining business processes. Two particularly impactful branches of AI – generative AI and predictive AI – are at the forefront of this revolution. For companies aiming to stay competitive and improve their performance, understanding and implementing these technologies is no longer optional – it is imperative. This article will explore the fundamentals of generative and predictive AI, emphasising the crucial role of advanced knowledge systems in AI adoption, and will outline the steps companies need to take to prepare their data and infrastructure for successful AI implementation.
GENERATIVE AI
Generative AI refers to AI systems capable of generating new content, including text, images, audio, and even code. These systems ‘learn’ patterns from existing data and use that knowledge to generate novel, original outputs. The most well-known examples include large language models like GPT (Generative Pre-trained Transformer) for text generation and DALL-E for image generation. In a business context, generative AI has many applications, including content generation, such as marketing copy, product descriptions, and reports; code generation, such as assisting developers by generating code snippets or even entire functions; and customer service, by powering chatbots and virtual assistants capable of human-like interactions.
Many large companies have already leveraged generative AI for producing marketing content, while others have applied a copilot-like capability that uses AI to assist programmers with code suggestions.
PREDICTIVE AI
Predictive AI, on the other hand, focuses on analysing historical and current data to make predictions about systems’ performance and behaviours. These systems use statistical algorithms and machine learning techniques to identify patterns and forecast outcomes. Some of the key applications of predictive AI in business include operations modelling, demand forecasting, risk assessment, preventive maintenance, and customer behaviour prediction, to list a few. As an example, a large online retailer uses predictive AI for its anticipatory shipping model, preparing items for dispatch before customers even place orders.
While generative AI generates new content, predictive AI provides insights and forecasts based on existing data. Both technologies complement each other and can be powerful tools when implemented correctly. But what does that entail?
PREPARING DATA FOR AI IMPLEMENTATION
The foundation of any successful AI implementation is high-quality, well-prepared data. For companies to be successful in implementing AI to improve their overall operations, they must start by gathering relevant, diverse, and representative data from the right sources. Next, they need to clean that data by removing inconsistencies, duplicates, and other errors found in these datasets. After that, they need to integrate all of the important data collected from various disparate systems into one unified view, and then apply centrally defined data governance, establishing well-defined policies for data access, usage, privacy, and security. These are the common and well-understood elements of good data management. However, preparing data for AI goes beyond these basic steps. To truly leverage the power of AI, especially predictive AI, companies need to embrace advanced knowledge systems.
THE ROLE OF ADVANCED KNOWLEDGE SYSTEMS IN AI ADOPTION
While traditional data management focuses on storing and organising data, advanced knowledge systems go a step further by adding context, relationships, and domain-specific understanding to the data. This approach is particularly valuable for predictive AI applications, as it enhances the AI’s ability to make accurate and meaningful predictions.
DOMAIN ONTOLOGIES
Domain ontologies are formal representations of knowledge within a specific field or industry. They define concepts, categories, and relationships between entities in a structured manner. For AI implementations, domain ontologies provide crucial context that helps machines understand and interpret data more effectively. There are many benefits to incorporating domain ontologies, including: improved data interpretation, where AI systems can understand industry-specific terminology and concepts; enhanced data integration, as ontologies provide a common language for merging data from diverse sources; and better reasoning capabilities where AI can make more accurate inferences based on domain-specific relationships.
KNOWLEDGE GRAPHS
Knowledge graphs are interconnected networks of entities, their attributes, and the relationships between them. They provide a powerful way to represent and query complex, interconnected data.
In the context of AI implementation, knowledge graphs offer several advantages, including:
- Contextual understanding, as they capture the intricate relationships between different data points.
- Flexible querying, where complex ‘questions’ can be answered by traversing the graph.
- Improved machine learning, as knowledge graphs can enhance the performance of AI models by providing structured background knowledge.
A FOCUS ON DATA ANALYTICS FOR PREDICTIVE AI
Advanced knowledge systems significantly boost the capabilities of predictive analytics by identifying hidden patterns through representing data in a more contextual and interconnected manner, which can help uncover non-obvious relationships. They also help in improving feature engineering where domain ontologies can guide the selection and definition of relevant features for predictive models. The structured nature of knowledge graphs makes it easier to trace and explain AI predictions.
EXAMPLES OF ADVANCED KNOWLEDGE SYSTEMS IN ACTION
Here are two examples from different engineering and scientific domains to illustrate the power of advanced knowledge systems in AI implementation.
EXAMPLE 1: PREDICTIVE ANALYTICS IN MINING OPERATIONS
A large mining company implemented an AI-driven predictive analytics system enhanced by domain ontologies and knowledge graphs to optimise their operations and improve safety. The system integrated data from various sources, including geological surveys, equipment sensors, production data, and historical safety records. The key features of this system include a comprehensive representation of the mining environment, such as geological formations, equipment characteristics, and operational processes; the integration of real-time sensor data with historical information and domain knowledge; and the ability to reason over complex relationships between geological conditions, equipment performance, and safety risks.
The knowledge graph-based system enabled and improved ore grade prediction by considering the spatial and temporal relationships between geological features. It allowed more accurate equipment failure forecasting, taking into account the specific operating conditions of each mine site. It also enhanced safety risk assessment by identifying potential hazards based on the combination of environmental factors, equipment status, and historical incident data.
This methodology helped the business on many fronts, including an increase in ore recovery rates through more precise targeting of high-grade deposits, a reduction in unplanned equipment downtime, leading to significant productivity improvements, and an expected decrease in safety incidents due to proactive risk identification and mitigation.
EXAMPLE 2: MANUFACTURING OPTIMISATION THROUGH INTEGRATED KNOWLEDGE SYSTEMS
Another example is a large manufacturer that implemented an AI-driven predictive maintenance system enhanced by domain ontologies and knowledge graphs. The system integrated data from various sources, such as sensor data, maintenance records, and supply chain information. The key derived benefits included an improved fault diagnosis by leveraging domain-specific knowledge about vehicle components and their interactions; a more accurate prediction of part failures, considering complex factors like usage patterns and environmental conditions; and optimised maintenance scheduling by understanding the ripple effects of component failures across the production line. Consequences of such an implementation include an expected reduction in unplanned downtime and a decrease in maintenance costs.
IMPROVING COMPANY PERFORMANCE WITH AI
By leveraging both generative and predictive AI, enhanced by an advanced knowledge system, companies can significantly improve their overall performance, including enhanced decision-making by leveraging AI-driven insights grounded in domain knowledge, automation of complex and repetitive tasks, and optimised operations.
Companies that successfully implement AI, particularly those empowered by advanced knowledge systems, can gain a significant competitive advantage by capitalising on faster time-to-market, accelerated innovations grounded in domain expertise and fostering a culture of data-driven decision-making across the organisation.
CONCLUSION
The integration of generative and predictive AI, empowered by an advanced knowledge system, such as SourceOne EKPS, presents a transformative opportunity for companies across different domains. By focusing on building robust domain ontologies, comprehensive knowledge graphs, and AI-ready data infrastructure, organisations can unlock the full potential of these technologies. The journey towards AI adoption may be complex, but the rewards – improved performance, innovation, and competitive advantage – make it an essential undertaking for forward-thinking companies. As the AI landscape continues to evolve, those who invest in strong knowledge foundations will be best positioned to adapt and thrive in an increasingly AI-driven business world.