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No hype. No doom.
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News Roundup
Secretly Training on YouTubers’ Content - The AI video startup Runway faces backlash following a report that it copied training data from thousands of YouTube videos without permission.
First AI-Voice Speech on House Floor - Rep. Jennifer Wexton of Virginia made history as the first member of Congress to use an AI-generated voice to speak on the House floor.
$500M to be the Next AI Epicenter - New Jersey is enacting a hefty new tax credit to attract AI companies, aiming to establish itself as a hub for AI innovation.
Airtable’s AI Cobuilder - Airtable’s Cobuilder AI tool generates apps in seconds, potentially transforming enterprise software development by empowering non-technical users to create complex applications using natural language prompts.
Todays Top Story
Elon Musk announces launch of xAI’s “Memphis Supercluster,” claiming it will be the world’s most powerful AI training system by the end of 2024.
In collaboration with X and Nvidia, Musk has begun training on a massive supercomputer cluster in Memphis, Tennessee, with the goal of developing the world’s most powerful AI by every metric by December 2024.
The “Memphis Supercluster” features 100,000 liquid-cooled Nvidia H100 GPUs on a single RDMA fabric, which Musk claims gives xAI a significant advantage in training.
However, given past issues with xAI’s Grok chatbot and Musk’s tendency for grandiose statements, skeptics may question whether these claims will match reality.
Research
OpenAI Finds Groundbreaking AI Scaling Laws: Bigger Models Learn Faster, Use Less Data.
OpenAI's comprehensive study showcases that scaling up neural language models can lead to a significant boost in learning speed and sample efficiency, a critical insight for AI development.
The study explores the impact of model size, data volume, and computational power on neural language model efficiency. It aims to optimize AI performance while minimizing costs—a crucial advantage as business investment in AI is projected to hit $110 billion by 2024
Model performance scales predictably with the number of non-embedding parameters, dataset size, and compute, showing power-law relationships. Overfitting can be managed by scaling model size and dataset size in tandem; for every 8x increase in model size, a 5x increase in data is needed.
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