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5 year old folded her laundry, kept count of each type of clothing, made me write down the final inventory, got excited about the word “inventory,” double checked the inventory, reported it back to us, and is now reciting it in bed after lights out.

“5 shirts…16 socks…4 skirts…7 underwear…”

I know it’s a mug’s game to guess what your little kid will do when they grow up, but I’ll be damned if she’s not an accountant in 20 years. 🤷‍♂️

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“Humanity is still driving towards hell, but we have taken our foot off the accelerator.”

Key clarification: this is only energy emissions, which historically make up just half of total CO2e emissions. (EPA, C2ES, Our World in Data.) Regardless, good news, and desperately needed.

Details, Twitter thread.

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AI vs ML

Are there any common forms of AI now that aren’t machine learning?

Historically we had expert systems, formal logic, hand-authored decision trees (eg old school machine translation), domain-specific algorithms (eg A* pathfinding), and more.

Nowadays it seems like everything is some form of ML trained on real world data.

(I’m broadly lumping everything generated automatically from data into the “ML” bucket, including HMMs, Bayesian networks, stochastic modeling, etc.)

Maybe this is a good thing? The results definitely speak for themselves. I might buy that the scale of most modern problems simply overwhelms hand-authored approaches. Are we losing anything by putting all our eggs in the big-data-and-opaque-emergent-models basket, though? Are there any other forms of AI that we’re neglecting? Or am I just being old fashioned and naive?

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Fascinating: distributed systems techniques now also apply to performance sensitive applications on high end multi-core computers, eg AAA game engines on modern consoles, as well as to actual distributed systems.

Notably, serializing distributed transactions algorithmically, offline.

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