Strategic advice to leverage new technologies

Technology is at the heart of nearly every enterprise, enabling new business models and strategies, and serving as the catalyst to industry convergence. Leveraging the right technology can improve business outcomes, providing intelligence and insights that help you make more informed and accurate decisions. From finding patterns in data through data science, to curating relevant insights with data analytics, to the predictive abilities and innumerable applications of AI, to solving challenging business problems with ML, NLP, and knowledge graphs, technology has brought decision-making to a more intelligent level. Keep pace with the technology trends, opportunities, applications, and real-world use cases that will move your organization closer to its transformation and business goals.

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Michael Eiden, Philippe Monnot, and Armand Rotaru illustrate several prominent, real-world KG applications, then detail how they designed a KG to ensure vertical traceability within a systems engineering context. They developed an ML model that consumed features derived from the KG and mimicked the way an independent safety assessment auditor would work in practice. Using precision and recall to evaluate the model’s accuracy resulted in finding previously incorrectly labeled software requirement specifications. They also found that combining graph-based features with text-based ones boosts the classification accuracy significantly, thus showing significant promise in augmenting human safety assessors in the future. They end the article with some specific advice on using KGs, including unlocking new insights, extracting more from the data you have, and starting small with the intention of scaling quickly.
Andy E. Williams looks at how human-centric functional modeling (a way to allow computers to solve general problems) could be used to create KGs capable of providing compete semantic models of systems, enabling us to transition to Industry 5.0. He defines Industry 5.0 as a world in which far greater integration is possible, including functional computing approaches like GCI. Although the emergence of GCI isn’t guaranteed (it could end up in a technology gravity well), it would bridge type 1 and type 2 reasoning and lead to a radical increase in our ability to solve every problem.
George Hurlburt details how a KG was used to assist a regional center of a major university system in its course selection process. The KG helped leaders more clearly see the array of educational pathways from K-12 to community college (CC) coursework that are the results of articulation agreements between universities and CCs. Hurlburt shares five figures from the KG that demonstrate its meaningful visualizations. He also explains how the KG was built, including limiting the number of arcs and emphasizing node unambiguity. Finally, Hurlburt concludes with five key academic relationships and trends that are clearly demonstrated by the regional center’s KG.
Cigden Z. Gurgur looks at KGs in the context of blockchain. The article begins with background information on how KGs have been used in advanced analytics and their role in helping AI developers. Gurgur then shows how blockchain’s immutability and verifiability offer designers a way to advance KGs to produce more reliable results. The blockchain/KG combination is an ideal one to build more explainable AI systems, she says. Finally, Gurgur explains how KG-enabled information systems can be used in industrial settings to enhance product development lifecycles, improve factory safety, and enhance information systems to the point where employees need less technical knowledge to perform their duties.
Lila Rajabion provides four examples of how KGs can help leaders advance their understanding of the business environment in which their company sits. These include merging data silos to create a company overview across divisions, connecting different types of data in meaningful ways, aiding informed decision making by narrowing searches and contextualizing information, and showing interconnections that help leaders gain perspective. Next, she dives into how Google, LinkedIn, eBay, and IBM are using KGs and explains how other companies could follow suit. She then addresses four challenges currently faced by companies looking to leverage KGs, followed by a look at specific business efficiencies enabled by KGs, including making data more accessible for employees, helping leaders make data-driven decisions, and assisting companies in deploying AI technology.
The increasing realization that deep learning alone cannot be the solution to build robust, reliable artificial intelligence (AI) systems, coupled with the ever-increasing need to make use of heterogeneous data sources for decision making, has led to a recent resurgence of knowledge graphs (KGs). KGs are now playing a seminal role in the emergent field of neuro-symbolic AI, which aims to integrate domain knowledge into AI systems. By combining AI’s statistical/machine learning (ML) side with KGs, we get more effective, more explainable cognitive results and begin creating logic-based systems that get better with each application. In other words, we can build the next generation of AI models, ones that support better human-machine collaboration.
Effectively managing AI’s carbon footprint requires a shift to a system like regenerative capitalism or doughnut economics that does not emphasize continuous growth or increased consumption. However, the novel opportunities AI offers society make it difficult for many to accept the idea that data consumption related to AI must be managed. The 3Rs framework presents an alternate system grounded in regenerative capitalism and doughnut economics as a way to reduce the carbon footprint of data.
Explainable AI (XAI) is the discipline of going deeper within the AI system, identifying the reasoning behind the recommendations, verifying the data, and making the algorithms and the results transparent. XAI attempts to make the analytical recommendations of a system understandable and justifiable — as much as possible. Such explainability reduces biases in AI-based decisions, supports legal compliance, and promotes ethical decisions.