Categories: AI/ML News

Leveraging silicon photonics for scalable and sustainable AI hardware

The emergence of AI has profoundly transformed numerous industries. Driven by deep learning technology and Big Data, AI requires significant processing power for training its models. While the existing AI infrastructure relies on graphical processing units (GPUs), the substantial processing demands and energy expenses associated with its operation remain key challenges. Adopting a more efficient and sustainable AI infrastructure paves the way for advancing AI development in the future.
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Some recent Chroma renders

Model: https://huggingface.co/silveroxides/Chroma-GGUF/blob/main/chroma-unlocked-v38-detail-calibrated/chroma-unlocked-v38-detail-calibrated-Q8_0.gguf Workflow: https://huggingface.co/lodestones/Chroma/resolve/main/simple_workflow.json Prompts used: High detail photo showing an abandoned Renaissance painter’s studio…

11 hours ago

A Gentle Introduction to Multi-Head Latent Attention (MLA)

This post is divided into three parts; they are: • Low-Rank Approximation of Matrices •…

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Converting Pandas DataFrames to PyTorch DataLoaders for Custom Deep Learning Model Training

Pandas DataFrames are powerful and versatile data manipulation and analysis tools.

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Securing America’s Defense Industrial Base

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12 hours ago

No-code data preparation for time series forecasting using Amazon SageMaker Canvas

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Beyond static AI: MIT’s new framework lets models teach themselves

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13 hours ago