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Minerva – Google’s Language Model for Quantitative Reasoning

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Recently, experimenters have developed a very sophisticated natural language […]. The model for natural language processing is called Minerva.

Modeling 399
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End to End Statistics for Data Science

Analytics Vidhya

This article was published as a part of the Data Science Blogathon Introduction to Statistics Statistics is a type of mathematical analysis that employs quantified models and representations to analyse a set of experimental data or real-world studies. Data processing is […].

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An Architecture of Participation for AI?

O'Reilly on Data

I suspected that Satya might be sympathetic because of past conversations wed had when his book Hit Refresh was published in 2017. Its become a kind of central planning. And I dont think we have that product-market fit for AI yet. Product-market fit isnt just getting lots of users.

Marketing 247
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It’s 2025. Are your data strategies strong enough to de-risk AI adoption?

CIO Business Intelligence

If 2023 was the year of AI discovery and 2024 was that of AI experimentation, then 2025 will be the year that organisations seek to maximise AI-driven efficiencies and leverage AI for competitive advantage. Primary among these is the need to ensure the data that will power their AI strategies is fit for purpose.

Risk 111
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Answers: Generative AI as Learning Tool

O'Reilly on Data

It is important to be careful when deploying an AI application, but it’s also important to realize that all AI is experimental. Answers are always attributed to specific content, which allows us to compensate our talent and our partner publishers. Most AI engines can’t say “Sorry, I don’t know.” Ours can and will.

Modeling 341
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What you need to know about product management for AI

O'Reilly on Data

You might establish a baseline by replicating collaborative filtering models published by teams that built recommenders for MovieLens, Netflix, and Amazon. It may even be faster to launch this new recommender system, because the Disney data team has access to published research describing what worked for other teams.

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How EUROGATE established a data mesh architecture using Amazon DataZone

AWS Big Data

To achieve this, EUROGATE designed an architecture that uses Amazon DataZone to publish specific digital twin data sets, enabling access to them with SageMaker in a separate AWS account. From here, the metadata is published to Amazon DataZone by using AWS Glue Data Catalog. This process is shown in the following figure.

IoT 110