Remove 2040 Remove Deep Learning Remove Large Language Models
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Generative AI use cases for the enterprise

IBM Journey to AI blog

Generative AI represents a significant advancement in deep learning and AI development, with some suggesting it’s a move towards developing “ strong AI.” This data is fed into generational models, and there are a few to choose from, each developed to excel at a specific task.

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AI’s Growing Power Needs: Tech Industry’s Move Towards Nuclear Power

Unite.AI

Training complex AI models, particularly deep learning models, requires significant computational power. For instance, training a large language model like GPT-4 involves processing vast amounts of data through multiple layers of neural networks.

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Harnessing Silicon: How In-House Chips Are Shaping the Future of AI

Unite.AI

This has led to substantial environmental implications for training and using AI models. OpenAI researchers note that : since 2012, the computing power required to train advanced AI models has doubled every 3.4 Trainium is specifically designed to enhance AI model training and is set to be incorporated into EC2 UltraClusters.

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The executive’s guide to generative AI for sustainability

AWS Machine Learning Blog

From an operational standpoint, you can embrace foundation model ops (FMOps) and large language model ops (LLMOps) to make sure your sustainability efforts are data-driven and scalable. This involves documenting data lineage, data versioning, automating data processing, and monitoring data management costs.

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