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Generative AI: Eight fundamental security risks and considerations

CIO

This is particularly true for Generative AI, which presents several inherent security challenges. Here are some of the key risks related to AI that organizations need to bear in mind. No Delete Button The absence of a delete button in Generative AI technologies poses a serious security threat.

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This startup wants to train art-generating AI strictly on licensed images

TechCrunch

Generative AI, particularly text-to-image AI, is attracting as many lawsuits as it is venture dollars. Two companies behind popular AI art tools, Midjourney and Stability AI, are entangled in a legal case that alleges they infringed on the rights of millions of artists by training their tools on web-scraped images.

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Why thinking like a tech company is essential for your business’s survival

CIO

A great example of this is the semiconductor industry. We developed clear governance policies that outlined: How we define AI and generative AI in our business Principles for responsible AI use A structured governance process Compliance standards across different regions (because AI regulations vary significantly between Europe and U.S.

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Reduce ML training costs with Amazon SageMaker HyperPod

AWS Machine Learning - AI

Training a frontier model is highly compute-intensive, requiring a distributed system of hundreds, or thousands, of accelerated instances running for several weeks or months to complete a single job. For example, pre-training the Llama 3 70B model with 15 trillion training tokens took 6.5 During the training of Llama 3.1

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‘Just-in-time’ AI: Has its moment arrived?

CIO

Just as Japanese Kanban techniques revolutionized manufacturing several decades ago, similar “just-in-time” methods are paying dividends as companies get their feet wet with generative AI. We activate the AI just in time,” says Sastry Durvasula, chief information and client services officer at financial services firm TIAA.

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Build a multi-tenant generative AI environment for your enterprise on AWS

AWS Machine Learning - AI

While organizations continue to discover the powerful applications of generative AI , adoption is often slowed down by team silos and bespoke workflows. To move faster, enterprises need robust operating models and a holistic approach that simplifies the generative AI lifecycle.

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Multi-LLM routing strategies for generative AI applications on AWS

AWS Machine Learning - AI

Organizations are increasingly using multiple large language models (LLMs) when building generative AI applications. For example, consider a text summarization AI assistant intended for academic research and literature review. An example is a virtual assistant for enterprise business operations.