Our general approach is to automate the experimental loop. We think this approach is broadly applicable across many different fields of science and engineering. Weβll initially focus on ML research and engineering, but believe the approach can help with important subproblems in nearly every one of the fourteen <at>NAE Grand Challenge problems. We think doing this well requires strong expertise in machine learning as well as large-scale systems.
I kept forgetting mac shortcuts, so I built a tiny app that shows them only while you hold a key. If you wanna pin it, pin it.
hold right β -> every shortcut for the app you're in. let go -> gone.
Try it here. π
Anthropic published "A General Language Assistant as a Laboratory for Alignment" paper in 2021 which has a definition for AI alignment which is like this:
We will define an AI as βalignedβ if it is, in three words, helpful, honest, and harmless or βHHHβ.
code + download: https://t.co/I3smHpfXvf
- native swift, no electron β starts instantly
- reads shortcuts straight from each app's menu bar, so it works with every app you have and never goes stale
- nothing ever leaves your mac
what trigger key did you pick? i'm right β.
I kept forgetting mac shortcuts, so I built a tiny app that shows them only while you hold a key. If you wanna pin it, pin it.
hold right β -> every shortcut for the app you're in. let go -> gone.
Try it here. π
You can now ask Claude about the Anthropic Economic Index, our public dataset measuring how AI is used across the economy.
Ask which occupations use AI the most, or what kinds of tasks people are automating, and the answers draw directly from the Index data.