Aaron Roth

3.8K posts

Aaron Roth

Aaron Roth

@Aaroth

CS prof at Penn. Amazon Scholar at AWS. Author of The Ethical Algorithm (w/ Michael Kearns). I study machine learning, privacy, game theory, and uncertainty.

Philadelphia, PA Katılım Mayıs 2007
649 Takip Edilen11.6K Takipçiler
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Aaron Roth
Aaron Roth@Aaroth·
Aligning an AI with human preferences might be hard. But there is more than one AI out there, and users can choose which to use. Can we get the benefits of a fully aligned AI without solving the alignment problem? In a new paper we study a setting in which the answer is yes.
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Ken Chiu
Ken Chiu@kjw_chiu·
@BlackHC So far at least, AI is incapable of asking dumb questions as well as I can.
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Aaron Roth
Aaron Roth@Aaroth·
Neural networks are highly non-convex, so approximate error minimizers need not look anything like each other in parameter space. But we show that nevertheless (for many model sizes) approximate error minimizers must closely agree in function/prediction space despite this!
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Aaron Roth
Aaron Roth@Aaroth·
@unsorsodicorda (because there are many different ways to predict things incorrectly. Good question btw! :-)
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Aaron Roth
Aaron Roth@Aaroth·
@unsorsodicorda This doesn't require small error, just near optimal error in the model class. For most problems, small error is not possible. If you and I both have 40% error even in distribution, that doesn't imply similar predictions on its own.
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Jeremias Sulam
Jeremias Sulam@Jere_je_je·
Belated professional update 🔊 I’ve been promoted to Associate Professor with tenure at @JohnsHopkins @JHUBME. I’m incredibly thankful to my mentors over the past two decades, to my past and current collaborators (and friends!), and immensely proud of my students!
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Aaron Roth
Aaron Roth@Aaroth·
Via this simple argument, we get out-of-the-box agreement theorems about neural networks (parameterized by size), trees (parameterized by depth), gradient boosting (parameterized by iterations), and stacking (parameterized by ensemble size). No distributional assumptions needed.
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Aaron Roth retweetledi
Rajesh K. Gupta
Rajesh K. Gupta@GuptaUcsd·
What happens when looks are deceiving to all but very few experts? A thought provoking commentary on individual productivity acceleration with significant serious side effects to the community and its scientific evaluation culture.
Aaron Roth@Aaroth

Michael and I wrote a blog post about our experiences using AI for research and our thoughts on what these developments mean for research, publication, and education: amazon.science/blog/how-ai-is…

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Aaron Roth retweetledi
Amazon Science
Amazon Science@AmazonScience·
AI is bringing a sea change in scientific research methodology, training, and peer review. Amazon Scholars and Penn professors @mkearnsupenn and @Aaroth on what agentic AI tools mean for the next generation of researchers: amzn.to/47o1ZGR
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Aaron Roth
Aaron Roth@Aaroth·
Michael and I wrote a blog post about our experiences using AI for research and our thoughts on what these developments mean for research, publication, and education: amazon.science/blog/how-ai-is…
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Aaron Roth
Aaron Roth@Aaroth·
@perrymetzger @bindureddy As someone who uses AI extensively for mathematical research, I actually agree with the original post. AI has become an incredibly useful tool, but to use it well, you need expertise. If you let it lure you outside of your expertise, it's easy to produce slop.
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Perry E. Metzger
Perry E. Metzger@perrymetzger·
It is extremely difficult to convince people that a tool they use all day every day doesn’t work when they know that it does work. It’s equivalent to trying to convince people that automobiles are a hoax, or that the sky is green. After a while, all that happens is that you convince your audience that something is wrong with you, not with the tool they’re using.
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Bindu Reddy
Bindu Reddy@bindureddy·
99% of all AI generation - code, content, media - are ALL SLOP AND LARGELY USELESS - code is buggy - content is artificial - it doesn't really do anything well You need to be an expert user with a lot of patience to actually get it to do things well
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Aaron Roth
Aaron Roth@Aaroth·
@lreyzin We just need to start writing better papers. In the new equilibrium, nothing that that GPT can prove directly will be publication worthy. But there will be a larger set of things that we can prove with 6 months worth of effort using gpt.
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Lev Reyzin
Lev Reyzin@lreyzin·
Now is basically the last chance to finish up your theorems. A few more months, and everyone will assume the real work was done by ChatGPT.
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