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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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.
In this Advisor, we identify a number of important trends involving the application of artificial intelligence (AI), big data analysis, collaboration, mobile, and other technologies in insurtech.
In the shift toward data-driven healthcare, focusing only on new technologies is not sufficient. Indeed, how data is gathered and how stakeholders’ interests are managed can enable or hinder the transformation. This Advisor explores eight drivers of data-driven healthcare.
As we explore in this Advisor, machine vision systems employing machine learning and other artificial intelligence techniques are now bringing major benefits to automakers, dealers, and repair shops in the form of camera-based automated vehicle inspection systems.
This Advisor looks at the key roles that AI can play in cybersecurity operations: behavioral analytics, threat intelligence, ransomware attack detection, smart identity governance, strengthening security of cloud applications, online fraud detection, defending against deepfakes, and risk assessment.
Cigdem Z. Gurgur describes how a blockchain-based Internet of Things (IoT) can push market systems toward sustainability. Blockchain offers new opportunities relevant to systems design. It connects stakeholders with multiple sources of verified information, generates a richer informational landscape for executing business processes, and enables secure transactions between untrusted actors. Trusted networks can reduce transaction costs, simplify processes, and reduce resource intensity compared to traditional transaction technologies. Gurgur explores the conditions needed to facilitate blockchain deployment in the next generation of supply chains, specifically through IoT technologies that have attractive applications for creating, monitoring, and enforcing sustainability standards.