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Don’t pause AI development, prioritize ethics instead

IBM Journey to AI blog

.” That is why IBM developed a governance platform that monitors models for fairness and bias, captures the origins of data used, and can ultimately provide a more transparent, explainable and reliable AI management process. The stakes are simply too high, and our society deserves nothing less.

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Generative AI in the Healthcare Industry Needs a Dose of Explainability

Unite.AI

Increasingly though, large datasets and the muddled pathways by which AI models generate their outputs are obscuring the explainability that hospitals and healthcare providers require to trace and prevent potential inaccuracies. In this context, explainability refers to the ability to understand any given LLM’s logic pathways.

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Generative AI Developers Harness NVIDIA Technologies to Transform In-Vehicle Experiences

NVIDIA

Personalization is paramount, with AI assistants learning driver and passenger habits and adapting its behavior to suit occupants’ needs. Li Auto unveiled its multimodal cognitive model, Mind GPT, in June.

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Qdrant, an open source vector database startup, wants to help AI developers leverage unstructured data

Flipboard

“Vector databases are the natural extension of their (LLMs) capabilities,” Zayarni explained to TechCrunch. Qdrant, an open source vector database startup, wants to help AI developers leverage unstructured data by Paul Sawers originally published on TechCrunch ” Investors have been taking note, too.

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Who Is Responsible If Healthcare AI Fails?

Unite.AI

Who is responsible when AI mistakes in healthcare cause accidents, injuries or worse? Depending on the situation, it could be the AI developer, a healthcare professional or even the patient. Liability is an increasingly complex and serious concern as AI becomes more common in healthcare. Not necessarily.

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Igor Jablokov, Pryon: Building a responsible AI future

AI News

The current incarnation of Pryon has aimed to confront AI’s ethical quandaries through responsible design focused on critical infrastructure and high-stakes use cases. “[We We wanted to] create something purposely hardened for more critical infrastructure, essential workers, and more serious pursuits,” Jablokov explained.

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Chuck Ros, SoftServe: Delivering transformative AI solutions responsibly

AI News

. “Our AI engineers built a prompt evaluation pipeline that seamlessly considers cost, processing time, semantic similarity, and the likelihood of hallucinations,” Ros explained. It’s obviously an ambitious goal, but it’s important to our employees and it’s important to our clients,” explained Ros.

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