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Opening the black box
Everything we know about the upsides and limits of LLMs
We’re back with another edition of Machine Learnings, brought to you by the folks at Heyday.
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What we're reading.
1/ Tyler Cowan addresses a giant elephant in the room – what should happen legally when AI leads us astray? Well-reasoned and digestible. Learn more at Bloomberg >
2/ Sam Altman’s been talking to the public a whole lot of late. This time, on his belief that further advances to AI will come outside the techniques we’ve used to this point. Learn more at WIRED >
3/ Understanding how LLMs work is core to actually innovating on them, and while many of us feel we understand, there’s much more to uncover. This interview with Amazon’s distinguished scientists removes some confusion from the mysterious black box that is an LLM. Learn more at All Things Distributed >
4/ Stablility AI’s launched their first LLM, and the playing field for high-quality open-source foundational models continues to entertain. Get the details. Learn more at Stability >
5/ Putting knowledge to practice is the key to understanding anything, but especially AI. If you’ve been holding off on using ChatGPT to help you build software, here’s a step-by-step guide to using different LLM prompting styles to generate code. Learn more at Martin Fowler’s personal site >
6/ The Economist’s look at the changing economy born in the wake of ChatGPT. Pair with their take on how things could go wrong. You know what you’re getting here, and the visualizations are top notch. Learn more at The Economist >
7/ Dan Shipper takes on everything he’s learned about AI to predict what the near future will look like. Helpful framing for founders, investors, and builders everywhere. Learn more at Every’s Chain of Thought >
Research for this edition of Machine Learnings was enhanced by Heyday, the AI-powered memory assistant.
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