The hottest Compute Substack posts right now

And their main takeaways
Top Technology Topics
SemiAnalysis 7576 implied HN points 27 Sep 23
  1. Eroom's Law and Moore's Law are critical in Semiconductors and Drug Research, analyzing time, money, and output.
  2. Healthcare, a $4 trillion industry, lags behind in technological progress driven by Moore's Law.
  3. Illumina acquisition by Nvidia could bridge the gap in genomics, addressing bottlenecks and enabling full-stack healthcare solutions.
Import AI 1238 implied HN points 15 Jan 24
  1. Today's AI systems struggle with word-image puzzles like REBUS, highlighting issues with abstraction and generalization.
  2. Chinese researchers have developed high-performing language models similar to GPT-4, showing advancements in the field, especially in Chinese language processing.
  3. Language models like GPT-3.5 and 4 can already automate writing biological protocols, hinting at the potential for AI systems to accelerate scientific experimentation.
Axis of Ordinary 39 implied HN points 12 Jan 24
  1. AI advancements showcased in different domains like video models, AI glasses for the visually impaired, and AI-powered cough tracking apps.
  2. Exciting developments in astronomy with potential signs of life on exoplanets.
  3. Innovation in computing with faster nanotechnology, graphene spintronics, and Silicon Photonics breaking bandwidth limitations.
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  1. Using scaling laws can help predict how much better language models will get with more computational power or data.
  2. The majority of the error in language models comes from limited data, rather than limited model size.
  3. To improve language models significantly, more data and compute are needed, but there may be a limit to how much more can be added with current technology.
Joshua Gans' Newsletter 0 implied HN points 06 Mar 24
  1. Massive investments are going into AI for developing foundational models like GPT-4 and beyond, with accelerating costs speculated to reach mind-boggling amounts.
  2. Considering basic investment principles, it may be wise to invest in AI when costs are low, demand is known, and there is potential for repurposing resources like chips to maximize value.
  3. There are concerns about the economic justification and practical utility of rapidly escalating AI investments, suggesting a need for a more measured and thoughtful approach.