What are your favorite recent papers on using LMs for annotation (especially in a loop with human annotators), synthetic data for task-specific prediction, active learning, and similar? Looking for practical methods for settings where human annotations are costly. A few examples in thread ↴

I am once again pitching my romantic comedy: - two academics start dating - discover they are each other's terrible reviewer - hijinks ensue Working title: Love is Double-Blind

i wish i could shout this from the rooftops. relatedly, there's no need for robots to be limited by the human form. similar/tangential thing came up in the 2010s with respect to self-driving: just because people only sense using their eyes doesn't mean cars have to only use cameras!

The Wikimedia Foundation, which owns Wikipedia, says its bandwidth costs have gone up 50% since Jan 2024 — a rise they attribute to AI crawlers. AI companies are killing the open web by stealing visitors from the sources of information and making them pay for the privilege

we are living in an empirical world and we are empirical girls

No labels, no problem! I am so excited for this release. We have been working on it for many months, and it's motivated by a common customer roadblock: insufficient labeled examples.

has anyone successfully gotten very involved with their local library system and, if so, how does one do so? i know there are volunteer opportunities and it is my dream to one day organize a crafting circle, but i'm talking about how the library actually organizes / functions / prioritizes things!

🧵 Super proud to finally share this work I led last quarter - the @databricks.bsky.social Domain Intelligence Benchmark Suite (DIBS)! TL;DR: Academic benchmarks ≠ real performance and domain intelligence > general capabilities for enterprise tasks. 1/3

very demure, very mindful, very 2019-era mujoco humanoid learning to walk

"technology built to address people's needs" is the north star. side note: it would be amazing to see this attitude in the physical, embodied world as well. it's amazing to see how older adults in dense, walkable areas have such different lifestyles than those in car-centric suburbs.

this is incredible research, and beautiful. would love to know more about what it's like to meaningfully interact with genie 2, or similar models, e.g. to modify the outputs of such a model in the service of a design vision.

i often talk about the importance of aligning both the magnitude AND direction of a workstream vector. 1/5

i do not study this, but i did just finish reading the anxious generation and so i'm very grateful that there are so many people who do indeed study such important things!

When you fail to parse your data that’s a jsonl

😙🤌

that's just a growth mindset!

svg claude is giving ms paint in the best way