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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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.
If the world’s big data is a virtual mountain of dots, how can you connect them to extract their value? Knowledge graphs will help. The authors showcase several real-world KG applications, detail how they designed a KG to ensure vertical traceability in a systems engineering context, and offer specific advice on using KGs.
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.
Cigdem 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.