The hottest Supercomputers Substack posts right now

And their main takeaways
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Import AI 367: Google's world-spanning model; breaking AI policy with evolution; $250k for alignment benchmarks

Import AI • 599 implied HN points • 01 Apr 24
🕹 Technology AI Supercomputers Benchmarks Cybersecurity
  1. Google is working on a distributed training approach named DiPaCo to create large neural networks that break traditional AI policy focusing on centralized models.
  2. Microsoft and OpenAI plan to build a $100 billion supercomputer for AI training, signaling the transition of AI industry towards capital intensive endeavors like oil extraction or heavy industry, touching on regulatory and industrial policy implications.
  3. Sakana AI has developed 'Evolutionary Model Merge' method to create advanced AI models by combining existing ones through evolutionary techniques, potentially changing AI policy by challenging the need for costly model development.

Import AI 359: $1 billion gov supercomputer; Apple’s good synthetic data technique; and a thousand-year old data library

Import AI • 339 implied HN points • 05 Feb 24
🕹 Technology AI Research Supercomputers Data Storage Language Models
  1. Google uses LLM-powered bug fixing that is more efficient than human fixes, highlighting the impact of AI integration in speeding up processes.
  2. Yoshua Bengio suggests governments invest in supercomputers for AI development to stay ahead in monitoring tech giants, emphasizing the importance of AI investment in the public sector.
  3. Microsoft's Project Silica showcases a long-term storage solution using glass for archiving data, which is a unique and durable alternative to traditional methods.
  4. Apple's WRAP technique creates synthetic data effectively by rephrasing web articles, enhancing model performance and showcasing the value of incorporating synthetic data in training.

No One AI Model Will Rule the World

The Algorithmic Bridge • 254 implied HN points • 28 Feb 24
🕹 Technology AI Computers Generative AI Supercomputers Industry
  1. The generative AI industry is diverse and resembles the automotive industry, with a wide range of options catering to different needs and preferences of users.
  2. Just like in the computer industry, there are various types and brands of AI models available, each optimized for different purposes and preferences of users.
  3. Generative AI space is not a single race towards AGI, but rather consists of multiple players aiming for different goals, leading to a heterogeneous and stable landscape.
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Why didn't we get GPT-2 in 2005?

DYNOMIGHT INTERNET NEWSLETTER • 3 HN points • 21 Mar 23
🕹 Technology AI Supercomputers Training Progress Competition
  1. GPT-2 likely required around 10^21 FLOPs to train, involving various estimates and approaches.
  2. The BlueGene/L supercomputer from 2005 could have trained GPT-2 in about 41 days, showcasing the progress in computing power.
  3. The development of large language models like GPT-2 was a gradual process influenced by evolving ideas, funding, and technology, distinct from targeted moon landing projects.