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How educators are using AI in the classroom

Flipboard

In 2018, ISTE and General Motors launched a professional development course to train educators on how to use AI for teaching and learning. In five years, we will have something that has been built without any input from teachers and without any shaping around the needs of education,” Culatta said.

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AI Decoded: Demystifying AI and the Hardware, Software and Tools That Power It

NVIDIA

With the 2018 launch of RTX technologies and the first consumer GPU built for AI — GeForce RTX — NVIDIA accelerated the shift to AI computing. And it can stay centered on the screen with eyes looking at the camera no matter where the user moves, using Auto Frame and Eye Contact. Magic Mask has completely changed that workflow.

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Building Generative AI and ML solutions faster with AI apps from AWS partners using Amazon SageMaker

AWS Machine Learning Blog

SageMaker AI makes sure that sensitive data stays completely within each customer’s SageMaker environment and will never be shared with a third party. Their validation capabilities include automatic scoring, version comparison, and auto-calculated metrics for properties such as relevance, coverage, and grounded-in-context.

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4 Key Risks of Implementing AI: Real-Life Examples & Solutions

Dlabs.ai

While AI systems can automate many tasks, they should not completely replace human judgment and intuition. The International Data Corporation predicts that the global datasphere will swell from 33 zettabytes in 2018 to a staggering 175 zettabytes by 2025.

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Optimize your machine learning deployments with auto scaling on Amazon SageMaker

AWS Machine Learning Blog

SageMaker supports automatic scaling (auto scaling) for your hosted models. Auto scaling dynamically adjusts the number of instances provisioned for a model in response to changes in your inference workload. When the workload increases, auto scaling brings more instances online. SageMaker supports three auto scaling options.

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ChatGPT & Advanced Prompt Engineering: Driving the AI Evolution

Unite.AI

In zero-shot learning, no examples of task completion are provided in the model. Chain-of-thought Prompting Chain-of-thought prompting leverages the inherent auto-regressive properties of large language models (LLMs), which excel at predicting the next word in a given sequence.

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Rad AI reduces real-time inference latency by 50% using Amazon SageMaker

AWS Machine Learning Blog

Since 2018, using state-of-the-art proprietary and open source large language models (LLMs), our flagship product— Rad AI Impressions — has significantly reduced the time radiologists spend dictating reports, by generating Impression sections. This post is co-written with Ken Kao and Hasan Ali Demirci from Rad AI.