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Generative AI use cases for the enterprise

IBM Big Data Hub

Generative AI ( artificial intelligence ) promises a similar leap in productivity and the emergence of new modes of working and creating. Generative AI represents a significant advancement in deep learning and AI development, with some suggesting it’s a move towards developing “ strong AI.”

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The Rise of Large-Language-Model Optimization

Schneier on Security

The advent of AI threatens to destroy the complex online ecosystem that allows writers, artists, and other creators to reach human audiences. This article might be written very differently in an academic publication, for example, and publishing it here entailed pitching an editor, revising multiple drafts for style and focus, and so on.

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AI-Generated Steganography

Schneier on Security

New research suggests that AIs can produce perfectly secure steganographic images: Abstract: Steganography is the practice of encoding secret information into innocuous content in such a manner that an adversarial third party would not realize that there is hidden meaning. News article.

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Getting ready for artificial general intelligence with examples

IBM Big Data Hub

While AGI remains theoretical, organizations can take proactive steps to prepare for its arrival by building a robust data infrastructure and fostering a collaborative environment where humans and AI work together seamlessly. This gap highlights the vast difference between current AI and the potential of AGI.

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Leveraging generative AI on AWS to transform life sciences

IBM Big Data Hub

The exponential leap in generative AI is already transforming many industries: optimizing workflows , helping human teams focus on value added tasks and accelerating time to market. This digital data is coming at the industry in various formats, like unstructured text, images, PDFs and emails.

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Shedding light on AI bias with real world examples

IBM Big Data Hub

As companies increase their use of artificial intelligence (AI), people are questioning the extent to which human biases have made their way into AI systems. Companies are motivated to tackle the challenge of bias in AI not only to achieve fairness, but also to ensure better results. It also reduces AI’s potential.

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Why data governance is essential for enterprise AI

IBM Big Data Hub

The recent success of artificial intelligence based large language models has pushed the market to think more ambitiously about how AI could transform many enterprise processes. However, consumers and regulators have also become increasingly concerned with the safety of both their data and the AI models themselves.