Remove Data Processing Remove Experimentation Remove Risk
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Generative AI: A Self-Study Roadmap

KDnuggets

Part 6: Responsible GenAI Development Understanding Limitations and Risks Hallucination Detection : Foundation models sometimes generate confident-sounding but incorrect information. Regular hands-on experimentation helps you understand new capabilities and identify practical applications.

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CIOs tackle the AI change management challenge

CIO Business Intelligence

There are deep dives into the implications of new gen AI models, discussions of compliance and ethical risks, and knowledge sharing around emerging use cases and technical best practices. One of the biggest barriers is change management — getting employees invested and committed to reimagining how they work. There are no pre-set expectations.

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Lower your Large Language Model costs with Graphwise GraphDB

Ontotext

Building a RAG prototype is relatively easy, but making it production-ready is hard with organizations routinely getting stuck in experimentation mode. GraphDB allows experimentation and optimization of the different tasks. Why not vanilla RAG? It also results in cost overruns due to multiple complex GPT4 queries. GraphDBs v 10.8

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Product-based IT: 6 key steps for making the switch

CIO Business Intelligence

Product management is crucial for businesses looking to drive innovation and leverage technology as a differentiator, shared Roman Dumiak, executive-in-residence at the DePaul University Innovation Development Lab, at a recent Coffee With Digital Trailblazers event I hosted on the topic.

IT
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Ascending Levels of Nerd

O'Reilly on Data

The risk is you start with here are six tools I use in my own complex workflow in a language you dont recognise to solve problems you don’t have and lose people in a flood of random tool names. Ideally, you will not only give us the full version but also distill particular, usable lessons from it.

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What you need to know about product management for AI

O'Reilly on Data

But there’s a host of new challenges when it comes to managing AI projects: more unknowns, non-deterministic outcomes, new infrastructures, new processes and new tools. The need for an experimental culture implies that machine learning is currently better suited to the consumer space than it is to enterprise companies.

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3 steps to eliminate shadow AI

CIO Business Intelligence

These same decision-makers identify a host of challenges in implementing generative AI, so chances are that a significant portion of use is “unsanctioned.” The perils of unsanctioned generative AI The added risks of shadow generative AI are specific and tangible and can threaten organizations’ integrity and security.