There is a group of people quietly sabotaging all frontier language models…. People with bad taste. We show that we can get human-preference reward models to do better by finding and removing bad examples

Data is the most important determinant of model behavior. Just like we are conscious of what we show our kids, we should be conscious of what we show our models. Shoutout Gabriela Aránguiz-Dias and CS361 by @aiprof_mykel. Read the paper!!.

Turns out, for bad examples, the approaches tend to agree, but for finding good examples, the second order information isn’t as important.

HOWEVER!!. Gradient similarity is actually BETTER than influence functions for finding GOOD examples 🤨🤨.

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How do we know an example is bad? . We use influence functions to evaluate whether it helps or hurts on a small validation set. We compare this approach to gradient similarity between train and test examples, and find that it doesn’t work.

Most hard problems do not have verifiable answers. Happy to share this work on AI for creative writing to push forward how we think about rewarding models to perform well in divergent domains.

RT @sebbrusso: seeing as im graduating in 2 weeks, I wanted to share a piece i wrote about leaving stanford

RT @tszzl: a common cope among the classes blessed to work on or with ai, but we are not blessed for long. there is no conceptual divide be….

I made an app!. It’s called Receipts and it gives you all the data you could ever want about your text messages. It also has some really cool AI features like conversation simulation and text recommendations. Shoutout @kabirjolly_ for working through tough battles to make this

RT @deliprao: If this were a science paper, you would expect a country that picks its science workforce at random as a “weak baseline” and….

RT @mollycantillon: only at stanford😭

RT @therecount: Fox News’ Peter Doocy uses all his time at the White House press briefing to ask about an assessment that “literally everyo….

Business insider 2 for 2 on entirely misunderstanding what Sam Altman is saying

The result is that google can basically either have a worse product than bing, potentially losing marketshare, or have an equally expensive product, which would destroy their business.

But if you’re google, your whole business is paid for by a big search margin, not to mention the technical difficulty of scaling inference of a LLM to everyone in the world.

@bing and @satyanadella have google in checkmate. With 3% of search marketshare it’s easy to put out a 150B parameter language model, whatever it costs - Bing isn’t a major profit center anyway.

One of the underrated design innovations of chatGPT is the removal of the traditional left/right chat interface that unnecessarily wastes a bunch of space for the response.

How is this real. “Altman and OpenAI's chief scientist, Ilya Sutskever, said the move to focus on large language models is the best way for the company to reach AGI, or adjusted gross income.”.

The chrome search box is a dramatically underutilized text input. I should be able to type “am I free at 4 on Friday” and it checks with my calendar, or “order sushi” and it takes me through to doordash.

Eventually each person will have personal models that generate and evaluate based on their taste. Like a rotten tomatoes that’s just for yourself or a tiktok where each video is generated based on your preferences and emotions.