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ChatGPT, Author of The Quixote

O'Reilly on Data

TL;DR LLMs and other GenAI models can reproduce significant chunks of training data. Researchers are finding more and more ways to extract training data from ChatGPT and other models. And the space is moving quickly: SORA , OpenAI’s text-to-video model, is yet to be released and has already taken the world by storm.

Modeling 275
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Edmunds sets stage for AI with data infrastructure consolidation

CIO Business Intelligence

Rokita has been with Edmunds for more than 18 years, starting as executive director of technology in 2005. His role now encompasses responsibility for data engineering, analytics development, and the vehicle inventory and statistics & pricing teams. The data warehouse is about past data, and models are about future data.

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Burnout: An IT epidemic in the making

CIO Business Intelligence

The stages of burnout Developing over time, burnout builds in distinct stages that lead employees down a path of low motivation, cynicism, and eventually depersonalization, according to Yerbo’s The State of Burnout in Tech report, which points to 2005 research by Salanova and Schaufeli on the subject.

IT 130
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Modernize Using The BI & Analytics Magic Quadrant

Rita Sallam

Or when Tableau and Qlik’s serious entry into the market circa 2004-2005 set in motion a seismic market shift from IT to the business user creating the wave of what was to become the modern BI disruption. After five minutes of seeing these products back then, I just knew they would change everything! Answer: Better than every other vendor?

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Data Science, Past & Future

Domino Data Lab

how “the business executives who are seeing the value of data science and being model-informed, they are the ones who are doubling down on their bets now, and they’re investing a lot more money.” He was saying this doesn’t belong just in statistics. Key highlights from the session include. Transcript. Tukey did this paper.

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Building a Named Entity Recognition model using a BiLSTM-CRF network

Domino Data Lab

In this blog post we present the Named Entity Recognition problem and show how a BiLSTM-CRF model can be fitted using a freely available annotated corpus and Keras. The model achieves relatively high accuracy and all data and code is freely available in the article. How to build a statistical Named Entity Recognition (NER) model.

Modeling 111
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Using random effects models in prediction problems

The Unofficial Google Data Science Blog

KUEHNEL, and ALI NASIRI AMINI In this post, we give a brief introduction to random effects models, and discuss some of their uses. Through simulation we illustrate issues with model fitting techniques that depend on matrix factorization. Random effects models are a useful tool for both exploratory analyses and prediction problems.