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Mastering the Unknown: Uncertainty Modeling in AI for Critical Safety

Analytics Vidhya

Introduction In our AI-driven world, reliability has never been more critical, especially in safety-critical applications where human lives are at stake. This article explores ‘Uncertainty Modeling,’ a fundamental aspect of AI often overlooked but crucial for ensuring trust and safety.

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

Rocket-Powered Data Science

I recently saw an informal online survey that asked users which types of data (tabular, text, images, or “other”) are being used in their organization’s analytics applications. The results showed that (among those surveyed) approximately 90% of enterprise analytics applications are being built on tabular data.

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Small Businesses Use Big Data to Offset Risk During Economic Uncertainty

Smart Data Collective

Big data technology used to be a luxury for small business owners. In 2023, big data Is no longer a luxury. One survey from March 2020 showed that 67% of small businesses spend at least $10,000 every year on data analytics technology. However, there are even more important benefits of using big data during a bad economy.

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Decision Making with Uncertainty Requires Wideward Thinking

Andrew White

COVID-19 and the related economic fallout has pushed organizations to extreme cost optimization decision making with uncertainty. As a result, Data, Analytics and AI are in even greater demand. Demand from all these organizations lead to yet more data and analytics. With data comes quality issues.

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Crisis Creates New Opportunities for Data-Driven Businesses

Sisense

Analytics and data are changing every facet of our world. In The State of BI & Analytics , we expand on our original research, keeping you ahead of the curve on the world of analytics, data, and business intelligence. Analytics are essential in a crisis.

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Business Strategies for Deploying Disruptive Tech: Generative AI and ChatGPT

Rocket-Powered Data Science

Third, any commitment to a disruptive technology (including data-intensive and AI implementations) must start with a business strategy. 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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Highlights from the Strata Data Conference in San Francisco 2019

O'Reilly on Data

Watch highlights from expert talks covering AI, machine learning, data analytics, and more. People from across the data world are coming together in San Francisco for the Strata Data Conference. The journey to the data-driven enterprise from the edge to AI. Data warehousing is not a use case.