Calling Bullshit: The Art of Skepticism in a Data-Driven World
@CT_Bergstrom and I wrote a general-audience book on how to spot and refute misinformation, especially the kind wrapped in data.
Coming Aug. 4th, available here: https://t.co/b0P3ny9w67
Honored to be quoted alongside my advisor @jevinwest in the latest @Nature news article by @dcastelvecchi! It’s exciting to contribute to the conversation on @SakanaAILabs’s AI scientist and automating science in general. Check it out here: https://t.co/JjPpC76nS8
A full commentary is coming soon.
Sharing one of the preliminary results related to popularity bias in cited references with AI generated papers disproportionately citing highly cited papers (~26000 citations per paper on average) in comparison to human written papers (~7500 citations) — an observation that was recently made by @jimmykoppel as well.
#AcademicTwitter #Science @SciencePlusAI #ScienceOfAIMediatedScience
🧵@jevinwest and I recently wrote a commentary: "Search engines post-#ChatGPT: How generative artificial intelligence could make search less reliable."
We discuss 6 critical issues as search engines transition from "helping" you find the answer to "answering" your question.
New nonpartisan AI nonprofit @truemediadotorg, led by Oren @Etzioni and backed by Uber co-founder Garrett Camp (@gc), is making a political deepfake detector https://t.co/nQ4DM5CEYd via @GeekWire
Ironic that Ioannidis led a study on extremely productive authors, which "has become worryingly common across scientific fields." From my count he has published more than 150 papers in the last 2 years. That is about a paper every 5 days. https://t.co/3Lw610Hcvm
Silicon Valley hype oozes from this video (eg, ‘understanding of science’, ‘reasoning capabilities’, ‘reads 200k papers’) but the re-graphing tool is pretty nice. If it can do that consistently and reliably, worth trying out. https://t.co/q1OZx52RnK via @YouTube
🧐Can language models generate new scientific ideas?
Introducing contextualized literature-based discovery (C-LBD):
1. (L)LMs are given specific contexts/motivations + retrieved *inspirations* from papers💡
2. Goal is to generate text with novel hypotheses.
New preprint: 🧵⬇️
Science published a hopelessly credulous news story entitled "Fake scientific papers are alarmingly common" about one of the worst preprints we've seen in years.
This is some next-level bullshit. Over at Mastodon, a full thread: https://t.co/9CfIxS30y8
"I’ve wanted to type questions into a search bar and, in seconds, read the consensus from decades of scientific research. Such a tool is already in development and is called Consensus." @DKThomp
AI is redefining search. Sign up at https://t.co/zh713NqPWq
https://t.co/naYJvo4kB3