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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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Proposals for model vulnerability and security

O'Reilly on Data

Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors. Like many others, I’ve known for some time that machine learning models themselves could pose security risks.

Modeling 219
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Generative AI readiness is shockingly low – these 5 tips will boost it

CIO Business Intelligence

4 Additionally, while 63% have guardrails in place to use AI safely, these organizations worry about its role in misinformation, ethical bias and job loss among other risks, Wavestone found. You’ll also convene workshops articulating strategy and build consensus around what organizational readiness will look like.

IT 123
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AI governance is rapidly evolving — Here’s how government agencies must prepare

IBM Big Data Hub

In the context of AI, it can refer to the safety and ethics guardrails of AI tools and systems, policies concerning data access and model usage or the government-mandated regulation itself. They identify international coordination and safety regulation as critical to preventing risks related to an “AI race.”

Risk 76
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Dump the RFP to reap better outsourcing results

CIO Business Intelligence

Embracing a collaborative Request for Solution model transforms the procurement journey by incorporating a supplier “dialogue” phase where the buyer and supplier collaborate on the best possible solution. Stepping into this model means inviting suppliers to step up and present innovative solutions tailored to the buyer’s needs.

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Upskilling ramps up as gen AI forces enterprises to transform

CIO Business Intelligence

Most recently, in June, it spent $650 million to buy Casetext, a 104-employee company that offers an AI assistant for legal professionals powered by OpenAI’s GPT-4, the same large language model (LLM) behind ChatGPT. Generative AI systems carry a lot of risk for enterprises,” he says. And then there are the legal risks.”

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Creating value with generative AI in manufacturing

CIO Business Intelligence

Others are weighing the advantages of subscription-based business models where industrial equipment, automation, and processes are delivered as a service. Nall comments: “Copilot can help manufacturers de-risk their supply chains by helping predict potential disruption so they can put mitigations in place ahead of time.”