MoreRSS

site iconNicholas Carlini

A research scientist at Google DeepMind working at the intersection of machine learning and computer security.
Please copy the RSS to your reader, or quickly subscribe to:

Inoreader Feedly Follow Feedbin Local Reader

Rss preview of Blog of Nicholas Carlini

How I use "AI"

2024-08-01 08:00:00

I don't think that "AI" models [a] (by which I mean: large language models) are over-hyped.

Why I attack

2024-06-24 08:00:00

Yesterday I was forwarded a bunch of messages that Prof. Ben Zhao (a computer science professor [a] A full professor with tenure, so I feel entirely within my rights to call him out here. at the University of Chicago) wrote about me on a public Discord server with 15,000 members, including this gem:

(yet another) Broken Adversarial Example Defense at IEEE S&P 2024

2024-05-06 08:00:00

IEEE SP 2024 (one of the top computer security conferences) has, again, accepted an adversarial example defense paper that is broken with simple attacks. It contains claims that are mathematically impossible, does not follow recommended guidance on evaluating adversarial robustness, and its own figures present all the necessary evidence that the evaluation was conducted incorrectly.

My benchmark for large language models

2024-02-19 08:00:00

A benchmark of ~100 tests for language models, collected from actual questions I've asked of language models in the last year.

My research idea logfile, 2016-2019

2024-01-21 08:00:00

How do I pick what research problems I want to solve? I get asked this question often, most recently in December at NeurIPS, and so on my flight back I decided to describe the only piece of my incredibly rudimentary system that's at all a process. I maintain a single file called ideas.txt, where I just append a new entry every time I think of something that might be an interesting topic for a paper. When it's time to pick my next project, I skim through the list, and pick whichever I think is most interesting. or exciting. or important. or whatever I'm looking for at the moment. (Or find something new entirely if nothing looks compelling.)