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Will generative AI live up to its hype?

IBM Journey to AI blog

Many larger businesses and local governments have already successfully adopted gen AI to answer some of their challenges, whether to facilitate the analysis of customer data , enhance customer care , or improve knowledge modeling efficiency. “How can we operationalize AI if you’re already behind on trust?

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Child support systems modernization: The time is now

IBM Journey to AI blog

Asset Analysis Renovation Catalyst (AARC): This tool automatically extracts business rules from application source code and generates a knowledge model that can be deployed into a rules engine such as Operational Decision Manager (ODM).

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Modernizing child support enforcement with IBM and AWS

IBM Journey to AI blog

IBM Consulting Cloud Accelerator (ICCA) recommends client journeys without deep engineering knowledge, covering execution and modernization steps that take a workload from a source to a cloud destination.

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From Deep Knowledge Tracing to DKT2: A Leap Forward in Educational AI

Marktechpost

However, recent advancements in deep sequential KT models, such as Attentive Knowledge Tracing (AKT), have increasingly prioritized predictive performance over practical applicability and comprehensive knowledge modeling.

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Copyright, AI, and Provenance

O'Reilly Media

Any of these prompts might generate book sales—but whether or not sales result, they will have expanded my knowledge. Models that are trained on a wide variety of sources are a good; that good is transformative and should be protected.

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Best practices to build generative AI applications on AWS

AWS Machine Learning Blog

The automated data processing and API calling also enables FM to deliver updated, tailored answers and perform actual tasks by using proprietary knowledge. Developers can now focus on their core applications rather than routine plumbing.

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What is Retrieval Augmented Generation (RAG)?

Pickl AI

This collaboration bridges the gap between static knowledge models and dynamic query resolution, ensuring relevance and fluency. The retriever gathers relevant data and feeds it to the generator, which then uses this context to construct a precise answer.