Remove Measurement Remove Modeling Remove Risk
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UK launches platform to help businesses manage AI risks, build trust

CIO Business Intelligence

The UK government has introduced an AI assurance platform, offering British businesses a centralized resource for guidance on identifying and managing potential risks associated with AI, as part of efforts to build trust in AI systems. Meanwhile, the measures could also introduce fresh challenges for businesses, particularly SMEs.

Risk 130
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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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Managing risk in machine learning

O'Reilly on Data

Considerations for a world where ML models are becoming mission critical. As the data community begins to deploy more machine learning (ML) models, I wanted to review some important considerations. Before I continue, it’s important to emphasize that machine learning is much more than building models. Model lifecycle management.

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Why you should care about debugging machine learning models

O'Reilly on Data

Not least is the broadening realization that ML models can fail. And that’s why model debugging, the art and science of understanding and fixing problems in ML models, is so critical to the future of ML. Because all ML models make mistakes, everyone who cares about ML should also care about model debugging. [1]

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What are model governance and model operations?

O'Reilly on Data

A look at the landscape of tools for building and deploying robust, production-ready machine learning models. We are also beginning to see researchers share sample code written in popular open source libraries, and some even share pre-trained models. Model development. Model governance. Source: Ben Lorica.

Modeling 230
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Dulling the impact of AI-fueled cyber threats with AI

CIO Business Intelligence

Take for instance large language models (LLMs) for GenAI. From prompt injections to poisoning training data, these critical vulnerabilities are ripe for exploitation, potentially leading to increased security risks for businesses deploying GenAI. This puts businesses at greater risk for data breaches.

Risk 128
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Escaping POC Purgatory: Evaluation-Driven Development for AI Systems

O'Reilly on Data

Throughout this article, well explore real-world examples of LLM application development and then consolidate what weve learned into a set of first principlescovering areas like nondeterminism, evaluation approaches, and iteration cyclesthat can guide your work regardless of which models or frameworks you choose. Which multiagent frameworks?

Testing 174