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What Is Trustworthy AI?

NVIDIA

Trustworthy AI initiatives recognize the real-world effects that AI can have on people and society, and aim to channel that power responsibly for positive change. What Is Trustworthy AI? Trustworthy AI is an approach to AI development that prioritizes safety and transparency for those who interact with it.

AI 127
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Five open-source AI tools to know

IBM Journey to AI blog

The diversity and accessibility of open-source AI allow for a broad set of beneficial use cases, like real-time fraud protection, medical image analysis, personalized recommendations and customized learning. This availability makes open-source projects and AI models popular with developers, researchers and organizations.

AI Tools 161
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Active learning is the future of generative AI: Here’s how to leverage it

Flipboard

More posts by this contributor 4 questions to ask before building a computer vision model During the past six months, we have witnessed some incredible developments in AI. These advancements in generative AI offer further evidence that we’re on the precipice of an AI revolution. He holds an S.M.

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Art and Science of Image Annotation: The Tech Behind AI and Machine Learning

Becoming Human

The 1950s saw the development of neural networks that were trained by using hand-labeled images. Computer vision algorithms had become widespread by the 1970s , and researchers used annotated images to train AI algorithms. Cuboid Annotation In computer vision, cubic annotations are used as an image annotation method.

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Synthetic Data: A Model Training Solution

Viso.ai

Organizations can easily source data to promote the development, deployment, and scaling of their computer vision applications. Viso Suite is the End-to-End, No-Code Computer Vision Platform – Learn more What is Synthetic Data? An example is a privacy-preserving solution for developing healthcare AI models.

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Getting ready for artificial general intelligence with examples

IBM Journey to AI blog

Most experts categorize it as a powerful, but narrow AI model. Current AI advancements demonstrate impressive capabilities in specific areas. A key trend is the adoption of multiple models in production. This multi-model approach uses multiple AI models together to combine their strengths and improve the overall output.

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TensorFlow with MATLAB

TensorFlow

Today’s post will show you how to use these features, and give you examples of when you might want to use them and how they connect the work of AI developers and engineers to enable domain-specific AI system design. Simulink users have expressed interest in the ability to bring in AI models and simulate entire systems.