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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Biases in AI models can crop up due to data bias, and biases in algorithms can result from a developer’s viewpoints. Natural intelligence (NI) provides a countermeasure to these biases, challenging the data and algorithm biases with its understanding of the context in which decisions are made. NI is the investigation of the underlying causes of decisions and the superimposition of values on AI recommendations. The intuition, experience, expertise, and associated knowledge of NI help alleviate the impact of biases in AI-based decision making. Thus, one way forward in providing explanations in AI systems is to complement their recommendations with NI.
AI art generators combine machine learning (ML) and natural language processing to generate images from natural language text prompts input by users. These tools offer exciting possibilities for novices and professionals to create incredible art for many uses. They also raise several issues for business and society as their use becomes more mainstream.
Companies can’t choose between growth and sustainability — they must have both. But what kind of technology-led business model transformations will ensure “sweet spots” between the pursuit of growth and sustainability?
Examples of deep fakes range from videos of politicians or celebrities saying or doing things they never actually said or did and so-called “revenge porn” to fake photos of soldiers appearing to commit atrocities they didn't commit. However, as we explore in this Advisor, this trend is changing: hackers now use deep fake techniques to target commercial enterprises and government agencies for nefarious purposes.
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.