Ajeya Cotra

Apr 25, 2024 | aiascendant.substack.com | Jon Evans |Ajeya Cotra

Some are enthralled by generative AI, and advocate a future defined by it. Some fear it will rot our society or even destroy us all. And many loathe it, refuse to even call it "AI,” and do not conceal their contempt for both the believers and the frightened. What’s more, all of these attitudes are moving from ‘personal beliefs’ to ‘pervasive unquestioned assumptions,’ becoming the water in which entire communities swim.

Nov 10, 2023 | lesswrong.com | Daniel Kokotajlo |Ajeya Cotra |Ege Erdil |Zach Stein-Perlman

Here is a summary of the discussion so far:Daniel made an argument against Hofstadter's law for trend extrapolation and we discussed the validity of that for a bit. A key thing that has come up as an interesting crux/observation is that Ege and Ajeya both don't expect a massive increase in transfer learning ability in the next few years. For Ege this matters a lot because it's one of the top reasons why AI will not speed up the economy and AI development that much.

Oct 10, 2023 | planned-obsolescence.org | Ajeya Cotra

Kelsey Piper co-drafted this post. Thanks also to Isabel Juniewicz for research help. In January 2022, language models were still a pretty niche scientific interest. Once ChatGPT was released in November 2022, it accumulated a record-breaking 100 million users by February 2023. Many of those users were utterly flabbergasted by how far AI had come, and how fast. And every way we slice it, most experts were very surprised as well.

Aug 29, 2023 | planned-obsolescence.org | Ajeya Cotra

Kelsey Piper co-drafted this post. Thanks also to Isabel Juniewicz for research help. If you read media coverage of ChatGPT — which called it ‘breathtaking’, ‘dazzling’, ‘astounding’ — you’d get the sense that large language models (LLMs) took the world completely by surprise. Is that impression accurate? Actually, yes.

Apr 4, 2023 | planned-obsolescence.org | Ajeya Cotra

Researchers could potentially design the next generation of ML models more quickly by delegating some work to existing models, creating a feedback loop of ever-accelerating progress. The concept of an “intelligence explosion” has played an important role in discourse about advanced AI for decades.