Over the past decade, traditional artificial intelligence has evolved significantly, primarily focusing on number crunching for predictive analysis and pattern identification based on probabilities. The emergence of conceptual AI introduces a new dimension, offering access to quantitative AI assessments and research, thereby enabling more interactive verbal inquiries.
Peter Zornio, the senior vice president and CTO at Emerson, highlights how AI advancements are demystifying the once opaque realm of AI across various business functions. During a recent discussion in New York, Zornio elaborated on the distinct characteristics of conceptual AI and quantitative AI, representing two ends of a spectrum in terms of models and language-based approaches.
While both versions share a common professional foundation, Zornio emphasizes the unique operational methodologies associated with each. He illustrates that numerical-driven manufacturing models rely on quantitative data, whereas language-based designs draw insights from vast repositories of documents, images, and diverse sources.
The convergence of these two AI paradigms is reshaping conventional AI practices, introducing novel applications and synergies. Zornio envisions a scenario where language-based AI complements numerical models, enhancing operational efficiency and problem-solving capabilities in industrial settings. This integration facilitates natural language interactions akin to consulting a seasoned industry expert for insights and solutions.
Zornio underscores the importance of human expertise in tandem with AI capabilities, citing the example of leveraging accumulated knowledge and experience for enhanced decision-making. Relational AI plays a pivotal role in bridging the gap between human intuition and data-driven analytics, offering historical insights and recommending optimal solutions based on past scenarios.
The holistic approach of end-to-end AI solutions presents a transformative framework for developing robust product support systems. By consolidating manuals, support interactions, and relevant data into an accessible platform, organizations can leverage AI for streamlined information retrieval and problem-solving.
The applicability of AI spans diverse manufacturing sectors, from petrochemicals to automotive industries, with potential benefits extending to niche domains like winemaking. Zornio envisions winemakers harnessing AI to analyze key factors influencing wine quality, such as environmental conditions, fermentation processes, and soil characteristics, thereby enhancing production outcomes.
In addition to operational enhancements, AI fosters collaboration between operational technology (OT) and information technology (IT) teams, traditionally segregated within organizations. Zornio advocates for data harmonization and integration efforts to bridge the divide between OT and IT functions, emphasizing the need for cohesive strategies to leverage AI effectively.
Looking ahead, Zornio emphasizes the imperative of designing adaptable architectures to facilitate seamless data exchange between OT and IT domains, especially in the context of cloud-based AI systems. Embracing language-based AI models like OpenAI can further enhance interoperability and streamline communication across diverse technological landscapes.
