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Navigating Today’s Data and AI Market Uncertainty

KDnuggets

Are you choosing technologies that will stand the test of time? It’s more important than ever to think long-term about the analytics partnerships you forge. Are you choosing companies with proven track records?

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Regulatory uncertainty overshadows gen AI despite pace of adoption

CIO Business Intelligence

It’s no surprise, then, that according to a June KPMG survey, uncertainty about the regulatory environment was the top barrier to implementing gen AI. So here are some of the strategies organizations are using to deploy gen AI in the face of regulatory uncertainty. We’re still in the pilot phases of evaluating LLMs,” he says.

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Uncertainties: Statistical, Representational, Interventional

The Unofficial Google Data Science Blog

by AMIR NAJMI & MUKUND SUNDARARAJAN Data science is about decision making under uncertainty. Some of that uncertainty is the result of statistical inference, i.e., using a finite sample of observations for estimation. But there are other kinds of uncertainty, at least as important, that are not statistical in nature.

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Decision-Making in a Time of Crisis

O'Reilly on Data

But when making a decision under uncertainty about the future, two things dictate the outcome: (1) the quality of the decision and (2) chance. This essay is about how to take a more principled approach to making decisions under uncertainty and aims to provide certain conceptual and cognitive tools for how to do so, not what decisions to make.

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Easily Build an Optimization App and Empower Your Data

Speaker: Gertjan de Lange

If the last few years have illustrated one thing, it’s that modeling techniques, forecasting strategies, and data optimization are imperative for solving complex business problems and weathering uncertainty. Discover how the AIMMS IDE allows you to analyze, build, and test a model. Don't let uncertainty drive your business.

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Business Strategies for Deploying Disruptive Tech: Generative AI and ChatGPT

Rocket-Powered Data Science

Those F’s are: Fragility, Friction, and FUD (Fear, Uncertainty, Doubt). Keep it agile, with short design, develop, test, release, and feedback cycles: keep it lean, and build on incremental changes. Test early and often. Encourage and reward a Culture of Experimentation that learns from failure, “ Test, or get fired!

Strategy 289
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Bridging the Gap: How ‘Data in Place’ and ‘Data in Use’ Define Complete Data Observability

DataKitchen

The uncertainty of not knowing where data issues will crop up next and the tiresome game of ‘who’s to blame’ when pinpointing the failure. In the context of Data in Place, validating data quality automatically with Business Domain Tests is imperative for ensuring the trustworthiness of your data assets.

Testing 176