If we just make reasonable comparisons instead of crazy ones
More Than a Tool
What We Learned from a Year of Conversations About AI
Andrew Ng: The Biggest Opportunities in AI Aren't Where You Think
We Don't Understand Neural Networks At The Algorithmic Level
The largest ongoing debate about AI is “Are Large Language Models (LLMs) intelligent?” That makes sense, at least: the evidence is ambiguous and the stakes a...
The Eternal Sloptember
I’m calling it now, the adoption of AI agents into software development will be one of the most costly mistakes in the field’s history. Agents cannot program, and it’s taking longer and longer to realize that they can’t. They are a highly sophisticated statistical model designed to mimic the distribution of programming. The output is broken, but in a way that’s getting harder and harder to detect. Which is exactly what you’d expect from an increasingly accurate statistical model.
The 50 Percent Problem
Empirical evidence of the educational harm of generative AI
The AI Industry Has a Major Problem: Most People Aren’t Freaks Who Want an AI Agent Running Their Whole Life
There's a problem for AI agents like Meta's Muse: ordinary people don't seem very interested in what they're offering.
Quantifying the Carbon Emissions of Machine Learning
From an environmental standpoint, there are a few crucial aspects of training a neural network that have a major impact on the quantity of carbon that it emits. These factors include: the location of the server used for training and the energy grid that it uses, the length of the training procedure, and even the make and model of hardware on which the training takes place. In order to approximate these emissions, we present our Machine Learning Emissions Calculator, a tool for our community to better understand the environmental impact of training ML models. We accompany this tool with an explanation of the factors cited above, as well as concrete actions that individual practitioners and organizations can take to mitigate their carbon emissions.
Import AI 446: Nuclear LLMs; China's big AI benchmark; measurement and AI policy
Will AIs be jealous of one another?
Everybody needs a personal AI policy. Just ask Hank Green.
How can we reap AI’s benefits without melting our brains in the process?
Why AI hasn’t replaced software engineers, and won’t
Coding agents as normal technology
What AI coding costs you | Tom Wojcik
What's the effect of the prolonged AI usage among coders and is it tracked correctly, if it all?
Substackers Say New AI Detection Tool Is a ‘Witch Hunt’
“I’m not going to apologize for using AI in the creation process.”
How does AI impact skill formation?
Two days ago, the Anthropic Fellows program released a paper called How AI Impacts Skill Formation. Like other papers on AI before it, this one is being treated as proof that AI makes you slower and dumber. Does it prove that?
The AI "Evaluation Crisis" Is an Opportunity to Get Data Flow Right
Why the AI evaluation crisis could force a reckoning on dataset provenance, attribution, and consent.
The machines are fine. I'm worried about us.
On AI agents, grunt work, and the part of science that isn't replaceable.
