Remove ROI Remove Statistics Remove Testing Remove Uncertainty
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Belcorp reimagines R&D with AI

CIO Business Intelligence

These circumstances have induced uncertainty across our entire business value chain,” says Venkat Gopalan, chief digital, data and technology officer, Belcorp. “As That, in turn, led to a slew of manual processes to make descriptive analysis of the test results.

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What you need to know about product management for AI

O'Reilly on Data

All you need to know for now is that machine learning uses statistical techniques to give computer systems the ability to “learn” by being trained on existing data. Machine learning adds uncertainty. This has serious implications for software testing, versioning, deployment, and other core development processes.

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Getting ready for artificial general intelligence with examples

IBM Big Data Hub

LLMs like ChatGPT are trained on massive amounts of text data, allowing them to recognize patterns and statistical relationships within language. Here are some areas where organizations are seeing a ROI: Text (83%) : Gen AI assists with automating tasks like report writing, document summarization and marketing copy generation.

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

The Unofficial Google Data Science Blog

This classification is based on the purpose, horizon, update frequency and uncertainty of the forecast. The ROI of human involvement When it comes to human involvement, the key difference is in the magnitude of costs associated with any one forecast cycle. This defines the ROI on the investment of human time.

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Predicting Movie Profitability and Risk at the Pre-production Phase

Insight

I held out 20% of this as a test set and used the remainder for training and validation. Building Models to Predict Movie Profitability Here I use profitability as the metric of success for a film and define profitability as the return on investment (ROI). ROI = Profit/Budget). 158% (median ROI of 82%)! A New Hope ).

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Product Management for AI

Domino Data Lab

As a result, Skomoroch advocates getting “designers and data scientists, machine learning folks together and using real data and prototyping and testing” as quickly as possible. And then you’ll do a lot of work to get it out and then there’ll be no ROI at the end. Testing is critical. It is similar to R&D.