The hottest Compute Substack posts right now

Eroom's Law and Moore's Law are critical in Semiconductors and Drug Research, analyzing time, money, and output.
Healthcare, a $4 trillion industry, lags behind in technological progress driven by Moore's Law.
Illumina acquisition by Nvidia could bridge the gap in genomics, addressing bottlenecks and enabling full-stack healthcare solutions.

AI models are growing in size, straining the current hardware's ability to support them.
The memory wall problem arises due to limitations in memory capacity and processing speed.
To address AI hardware challenges, innovative solutions like Compute-in-Memory are being explored.

AI advancements showcased in different domains like video models, AI glasses for the visually impaired, and AI-powered cough tracking apps.
Exciting developments in astronomy with potential signs of life on exoplanets.
Innovation in computing with faster nanotechnology, graphene spintronics, and Silicon Photonics breaking bandwidth limitations.

Data and compute values have changed, affecting software and data business models.
The data explosion in the decade led to new successful business models downstream.
AI impact on data and compute leads to increased data value and the need for new tools and ecosystem in the AI-first world.

Using scaling laws can help predict how much better language models will get with more computational power or data.
The majority of the error in language models comes from limited data, rather than limited model size.
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.

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Massive investments are going into AI for developing foundational models like GPT-4 and beyond, with accelerating costs speculated to reach mind-boggling amounts.
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.
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.