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

CIO Business Intelligence

As Belcorp considered the difficulties it faced, the R&D division noted it could significantly expedite time-to-market and increase productivity in its product development process if it could shorten the timeframes of the experimental and testing phases in the R&D labs.

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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. This has serious implications for software testing, versioning, deployment, and other core development processes. Machine learning adds uncertainty.

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10 Books that Data Analyst Should Read

FineReport

. – Head First Data Analysis: A learner’s guide to big numbers, statistics, and good decisions. You’ll learn about the concept of big data and how to use big data—from computing ROI and big data strategies that drive business cases to the overall development and specific projects. By Michael Milton.

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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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Knowledge

Occam's Razor

Customer Lifetime Value ROI, Buzz Monitoring, Click Fraud. PPC / SEM Analytics: 5 Actionable Tips To Improve ROI. Google Analytics Maximized: Deeper Analysis, Higher ROI & You. Five Reasons And Awesome Testing Ideas. Lab Usability Testing: What, Why, How Much. Experimentation and Testing: A Primer.

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Building Smarter Financial Services: The Role of Semantic Technologies, Knowledge Graphs and Generative AI

Ontotext

Nimit Mehta: I think that 2024 is going to be a buckle-down year, but, at the same time, we’ll see a rapid explosion of experimentation. Show me the ROI.” ” So, maybe they are open to some testing and exploration, but when it’s prime time, larger organizations won’t run the risk of putting something untested.

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Product Management for AI

Domino Data Lab

Skomoroch proposes that managing ML projects are challenging for organizations because shipping ML projects requires an experimental culture that fundamentally changes how many companies approach building and shipping software. And then you’ll do a lot of work to get it out and then there’ll be no ROI at the end.