Remove Measurement Remove Testing Remove Uncertainty
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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. What breaks your app in production isnt always what you tested for in dev! How will you measure success?

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Generative AI: A Self-Study Roadmap

KDnuggets

Understanding concepts like probability distributions, sampling, and uncertainty helps you design better prompts, interpret model outputs, and build robust applications. Quality Evaluation and Testing : Unlike traditional ML models with clear accuracy metrics, evaluating generative AI requires more sophisticated approaches.

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AI, align thyself

CIO Business Intelligence

Techniques like Bayesian uncertainty modeling and confidence calibration allow AI to assign probabilities to its own mistakes, deferring decisions when confidence is low. AI can stress-test itself. Companies like OpenAI and Anthropic use automated adversarial prompts to test for bias exploitation and reward hacking.

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AI governance gaps: Why enterprise readiness still lags behind innovation

CIO Business Intelligence

This measured approach underscores a broader trend: most companies are in exploration mode, seeking to understand where AI can drive value before committing to widespread rollout. Speed vs. safety The top barrier to effective AI governance isn’t regulatory uncertainty or technical complexity — it’s pressure to move fast.

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Quantum machine learning (QML) is closer than you think: Why business leaders should start paying attention now

CIO Business Intelligence

Early-stage experimentation across industries is already demonstrating measurable improvements: Accelerated training: Complex models that typically require extensive computational resources can be trained more efficiently using quantum-enhanced algorithms, reducing both time-to-insight and energy consumption.

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Why HR professionals struggle with big data

CIO Business Intelligence

This is due, on the one hand, to the uncertainty associated with handling confidential, sensitive data and, on the other hand, to a number of structural problems. If a database already exists, the available data must be tested and corrected. Companies should then monitor the measures and adjust them as necessary.

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The CIO is on the verge of burnout

CIO Business Intelligence

While at other levels burnout is related to operational workload or repetitive tasks,CIOs must make critical decisions in environments of high uncertainty and constant change, he says. A first step would therefore be to measure all these aspects with the appropriate instruments. Gmez adds to this the lack of resources.