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Even though there are some AI/ML applicatins in industry, implementing AI/ML into companies' workflows has still been slow. These data-bases are rarely analyzed in detail and effectively leveraged because of some reasons:
         i) Operators tend to only analyze incidents with severe consequences to prevent recurrence, while minor incidents are only stored without any further evaluation. However, high-frequency and low-consequence incidents often display leading indicators that are overlooked but would still be useful to predict high-consequence incidents. [1-2]
         ii) While detailed data is used to create HAZOPs, PHAs, LOPAs, and bowties, there are issues with the data itself. [3-4].
         iii) Leading operators have invested in developing internal AI/ML skills through training or hiring, but many operators outsource their AI/ ML services in their particular fields. [4].

To address such barriers, researchers and consultants often aggregate data across operators to create more complete, consistent, and larger datasets to enhance algorithm training and 'delectability' of leading indicators. [7] On the other hand, cross-organizational aggregation and collaboration introduces other barriers such as differences in representativeness, context, and content which makes the data incommensurate [5-6] and model over-fitting that can lead to inaccurate predictions when the model is used on different or more general data. [8]

To develop AI applications effectively for industry, integrating both theoretical and practical aspects of artificial intelligence is crucial:

• Theoretical Aspects of AI

  • Fundamental Research and Theory: This includes understanding the underlying mathematical models and algorithms that power AI, such as neural networks, deep learning, reinforcement learning, and probabilistic reasoning. Theoretical knowledge is crucial for innovating and improving AI technologies.

  • Algorithm Development: Theoretical AI involves developing new algorithms or refining existing ones to enhance their efficiency, accuracy, and scalability. This is essential for tackling more complex problems or optimizing performance. Adapting AI methodologies to new business contexts or creating methods tailored to specific problems is a core part of developing practical applications. This includes designing solutions that fit within the operational constraints of business environments and that can be integrated effectively with existing systems.

  • Keeping Informed on Research Developments: It involves delving into the latest academic findings, research papers, and theoretical advancements in the field of AI. This activity is critical for understanding new theories, algorithms, and methodologies that could potentially be applied to solve practical problems.
  • thical and Social Implications: Theoretical discussions around AI also cover its ethical use, potential biases in AI systems, and the broader social impacts, guiding the responsible development and deployment of AI technologies.
  • Expertise in AI Methods and Tools: Deep understanding of a wide range of AI methods such as machine learning, deep learning, reinforcement learning, Support Vector Machines (SVM), natural language processing, computer vision, timeseries prediction, tabular data modeling, pattern recognition, and probabilistic models and Bayesian networks.
• Practical Aspects of AI

  • Application Development: Applying AI theories to real-world problems in various industries such as healthcare, finance, automotive, and more. This involves integrating AI into existing systems or developing new applications from scratch.

  • Data Management: Practical AI work includes handling data acquisition, cleaning, and processing to train AI models. Effective data management practices are essential to ensure the quality and efficacy of AI applications.

  • Collaboration and Interdisciplinary Approach: While solving practical problems using AI, there often is a need for a blend of skills and knowledge from various fields, which is a hallmark of practical AI implementation. It involves engineers, developers, business analysts, and more working together to creatively apply AI technologies.
  • System Integration and Deployment: This includes the technical aspects of deploying AI models into production environments, ensuring they operate efficiently, and maintaining them over time. The creative integration of AI into existing systems to enhance or transform their capabilities is a practical aspect. This involves not only technical skills but also an innovative approach to ensure that AI solutions work effectively within the broader system architecture. This focuses on the technical work of deploying AI models into production environments and ensuring they perform efficiently and reliably under real-world conditions. This involves practical tasks such as optimizing the model for speed and resource usage, ensuring it scales appropriately with increasing data or user load, and maintaining performance over time.
  • Application Development: It involves applying AI theories to solve real-world problems, which often are new or complex. This necessitates not just using existing solutions but also innovating and creating new ones that are specifically tailored to the unique challenges of a particular application.
  • Delivery of a Production-Ready AI Model: This is firmly in the realm of practical AI. It involves preparing the AI system for real-world deployment, which includes tasks such as optimizing the model for performance and scalability, ensuring it meets industry standards, and integrating it into the existing technological infrastructure.
  • Developing and Implementing AI Models: Create and refine artificial intelligence models to take on significant business challenges, leveraging the latest advancements in AI.
• Bridging Theory and Practice
  • Experimentation and Development of a Proof of Concept: This phase marks the transition from theory to practice. Experimentation often involves applying the theoretical knowledge gained in academic research to solve practical problems, testing hypotheses, and iteratively refining the approach based on real-world data and feedback.
  • Collaborative Teams: Building teams that include both researchers who understand AI at a theoretical level and engineers who can implement these theories practically. Encouraging collaboration and knowledge sharing between these roles is key. Applied research typically requires collaboration between theoreticians who understand the fundamental principles of AI and practitioners who understand the business context and practical constraints.
  • Continuous Learning and Adaptation: Encouraging ongoing education and training for all team members to keep up with the latest AI advancements and research findings. This helps in continuously improving and updating the applications as new theories and techniques emerge.
  • Proofs of Concept and Piloting: Testing theoretical models on smaller scales before full deployment can help bridge the gap between theory and practice. Pilot projects provide valuable insights into how a model performs in real-world scenarios and what adjustments are necessary. Testing these adapted or new methodologies in real-world scenarios is essential to refine them and ensure they meet business needs. This step is crucial in translating theoretical advancements into practical, deployable solutions.
  • Feedback Loops: Establishing mechanisms to feed practical experiences and results back to researchers. This helps in refining theoretical models and aligning research more closely with practical needs and challenges.
  • By translating cutting-edge research into actionable insights and techniques, developers and engineers can implement these findings in real-world scenarios, thereby enhancing the effectiveness and innovation of AI applications.
  • The entire described cycle bridges theoretical research and practical application. It ensures that innovations in AI research are not just theoretical but are also translated into tangible solutions that can be deployed in real-world environments. This involves continuous feedback loops between theory and practice, allowing for refinement and improvement of AI models based on practical experiences and results.

By addressing both the theoretical and practical aspects of AI and fostering a collaborative environment that promotes the transfer of knowledge from research to application, organizations can effectively drive innovation and create robust AI solutions tailored to real-world needs.

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[1] Aven, T., 2011. On some recent definitions and analysis frameworks for risk, vulnerability, and resilience. Risk Anal. An Int. J. 31 (4), 515–522.
[2] Steen, R., Aven, T., 2011. A risk perspective suitable for resilience engineering. Saf. Sci. 49 (2), 292–297.
[3] Dong, C., Dong, X., Gehman, J., Lefsrud, L., 2017. Using BP neural networks to prioritize risk management approaches for China’s unconventional shale gas industry. Sustainability 9 (6), 979.
[4] Ransbotham, S., Kiron, D., Gerbert, P., Reeves, M., 2017. Reshaping business with arti- ficial intelligence: Closing the gap between ambition and action. MIT Sloan Manage. Rev. 59 (1).
[5] Zuboff, S., 2015. Big other: surveillance capitalism and the prospects of an information civilization. J. Info. Technol. 30 (1), 75–89.
[6] Kellogg, K.C., Valentine, M.A., Christin, A., 2020. Algorithms at work: The new contested terrain of control. Academy Manage. Ann. 14 (1), 366–410.
[7] Kurian, D., Ma, Y., Lefsrud, L., Sattari, F., 2020. Seeing the Forest and the Trees: Using Machine Learning to Categorize and Analyze Incident Reports for Alberta Oil Sands Operators. J. Loss Prev. Process Ind. 64, 104069. https://doi.org/10.1016/j.jlp.2020. 104069.
[8] Bengio, Y., Goodfellow, I., Courville, A., 2017. Deep learning. MIT press.