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Beyond “Prompt and Pray”

O'Reilly on Data

The Evolution of Expectations For years, the AI world was driven by scaling laws : the empirical observation that larger models and bigger datasets led to proportionally better performance. This fueled a belief that simply making models bigger would solve deeper issues like accuracy, understanding, and reasoning.

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RLHF For High-Performance Decision-Making: Strategies and Optimization

Analytics Vidhya

RLHF for high performance focuses on understanding human behavior, cognition, context, knowledge, and interaction by leveraging computational models and data-driven approaches […] The post RLHF For High-Performance Decision-Making: Strategies and Optimization appeared first on Analytics Vidhya.

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Escaping POC Purgatory: Evaluation-Driven Development for AI Systems

O'Reilly on Data

Weve seen this across dozens of companies, and the teams that break out of this trap all adopt some version of Evaluation-Driven Development (EDD), where testing, monitoring, and evaluation drive every decision from the start. Two big things: They bring the messiness of the real world into your system through unstructured data.

Testing 174
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Aiding Architecture & Engineering Firms with Data-Driven Learning

Smart Data Collective

Data analytics is incredibly valuable for helping people. More institutions are recognizing this, so the market for data analytics in education is projected to be worth over $57 billion by 2030. We have previously talked about the many ways that big data is disrupting education.

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AR/VR Simulations for Sustainable, Regenerative, Circular Cities

Speaker: Nik Gowing, Brenda Laurel, Sheridan Tatsuno, Archie Kasnet, and Bruce Armstrong Taylor

This conversation considers how today's AI-enabled simulation media, such as AR/VR, can be effectively applied to accelerate learning, understanding, training, and solutions-modeling to sustainability planning and design. This is a panel discussion you won't want to miss! May 5, 2021 at 9:30 am PDT, 12:30 pm EDT, 5:30 pm GMT.

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The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

datapine

1) What Is Data Quality Management? 4) Data Quality Best Practices. 5) How Do You Measure Data Quality? 6) Data Quality Metrics Examples. 7) Data Quality Control: Use Case. 8) The Consequences Of Bad Data Quality. 9) 3 Sources Of Low-Quality Data. 10) Data Quality Solutions: Key Attributes.

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Answers: Generative AI as Learning Tool

O'Reilly on Data

Answers enables active learning: interacting with content by asking questions and getting answers, rather than simply ingesting a stream from a book or video. It would have been very difficult to develop the expertise to build and train a model, and much more effective to work with a company that already has that expertise.

Modeling 340
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Monetizing Analytics Features: Why Data Visualizations Will Never Be Enough

Think your customers will pay more for data visualizations in your application? Five years ago they may have. But today, dashboards and visualizations have become table stakes. Discover which features will differentiate your application and maximize the ROI of your embedded analytics. Brought to you by Logi Analytics.