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Marsh McLennan IT reorg lays foundation for gen AI

CIO Business Intelligence

One of the world’s largest risk advisors and insurance brokers launched a digital transformation five years ago to better enable its clients to navigate the political, social, and economic waves rising in the digital information age. Gen AI is quite different because the models are pre-trained,” Beswick explains.

IT 122
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Business Strategies for Deploying Disruptive Tech: Generative AI and ChatGPT

Rocket-Powered Data Science

3) How do we get started, when, who will be involved, and what are the targeted benefits, results, outcomes, and consequences (including risks)? Those F’s are: Fragility, Friction, and FUD (Fear, Uncertainty, Doubt). These changes may include requirements drift, data drift, model drift, or concept drift.

Strategy 290
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Marsh McLellan IT reorg lays foundation for gen AI

CIO Business Intelligence

One of the world’s largest risk advisors and insurance brokers launched a digital transformation five years ago to better enable its clients to navigate the political, social, and economic waves rising in the digital information age. Gen AI is quite different because the models are pre-trained,” Beswick explains.

IT 105
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Humans-in-the-loop forecasting: integrating data science and business planning

The Unofficial Google Data Science Blog

by THOMAS OLAVSON Thomas leads a team at Google called "Operations Data Science" that helps Google scale its infrastructure capacity optimally. This classification is based on the purpose, horizon, update frequency and uncertainty of the forecast. A single model may also not shed light on the uncertainty range we actually face.

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Regulatory uncertainty overshadows gen AI despite pace of adoption

CIO Business Intelligence

Gen AI has the potential to magnify existing risks around data privacy laws that govern how sensitive data is collected, used, shared, and stored. We’re getting bombarded with questions and inquiries from clients and potential clients about the risks of AI.” The risk is too high.”

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The trinity of errors in financial models: An introductory analysis using TensorFlow Probability

O'Reilly on Data

They trade the markets using quantitative models based on non-financial theories such as information theory, data science, and machine learning. Whether financial models are based on academic theories or empirical data mining strategies, they are all subject to the trinity of modeling errors explained below.

Modeling 210
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Three Emerging Analytics Products Derived from Value-driven Data Innovation and Insights Discovery in the Enterprise

Rocket-Powered Data Science

The latter is associated primarily with “watching” the data for interesting patterns, while precursor analytics is associated primarily with training the business systems to quickly identify those specific patterns and events that could be associated with high-risk events, thus requiring timely attention, intervention, and remediation.