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You cannot really train all these models to cater to different preferences. Can you have one model that caters to all?

@furongh.bsky.social unveils a technique to customize AI models on-the-fly to user goals, reducing the computational cost of tailoring AI systems to individual needs.

Come to our #NeurIPS #Competition on Erasing The Invisible Workshop on Stress Testing Image Watermarks today, Dec 15, 1:30-4:30pm PST, West Meeting Room 208.

erasinginvisible.github.io/workshop.html

This is Michael-Andrei Panaitescu-Liess presenting!

Mitigation?
We show that adaptive methods (e.g., methods that know information about the watermarking scheme) can improve the detection performance under watermarking.

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On the other hand, watermarking reduces the efficacy of training data detection methods.

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On the one hand, watermarking reduces the generation of copyrighted text.

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In this paper, we showed that watermarking can be a double-edged sword for copyright regulators since
1⃣ it promotes compliance during generation time
2⃣ but can make training time copyright violations harder to detect.​

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Can Watermarking Large Language Models Prevent Copyrighted Text Generation and Hide Training Data? Large Language Models (LLMs) have demonstrated impressive capabilities in generating diverse and contextually rich text. However, concerns regarding copyright infringement arise as LLMs may inadverten...

Congratulations to Michael-Andrei Panaitescu-Liess, Zora Che, Bang An, Yuancheng Xu, Pankayaraj Pathmanathan, Souradip Chakraborty, Sicheng Zhu, Tom Goldstein for winning the best paper award at the AdvML Frontiers workshop at #NeurIPS2024.

Paper: arxiv.org/abs/2407.17417

A short thread 👇

I hope NeurIPS and the broader academic community take this as a wake-up call to address the biases and systemic issues that enable such comments to go unchallenged. We must do better.

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Racism has no place in academia, and incidents like this tarnish the principles of inclusion and respect that we, as a global research community, should uphold.

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I regret that this happened at NeurIPS. I regret that this happened in my research community—a place I have cherished and contributed to for over 14 years. I regret that this happened at MIT, an institution of excellence and aspiration for many Chinese scholars.

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What is most heartbreaking is that Professor Picard couldn’t even acknowledge something as simple as: “Most Chinese scholars are honest and upright.” Instead, she focused on the singular exception and added, “Of course, with this one exception in this case” in her response.

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2.Even if the student’s school didn’t teach ethics (which is false for schools in China), other sources like family and community often instill strong ethical values. Ignoring this nuance is careless and reinforces stereotypes.

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There are glaring logical flaws in this argument:
1.If the student cheated, why would their excuse about ethics education be taken at face value? A serious scholar would investigate the claim before making it a central part of their argument.

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Professor Picard reinforced her remarks by quoting the student’s excuse —that ethics wasn’t taught in their school—and generalized this as a broader issue with Chinese education. This statement is both factually incorrect and deeply offensive.

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This was a generous and high-EQ question, offering Professor Picard an opportunity to reconsider or clarify her comments. Unfortunately, she doubled down instead.

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Are you calling out the student’s nationality because you find most Chinese scholars honest, and the fact that the cheating student was Chinese is rare? Is that why you emphasized nationality?

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What made this incident worse was how it unfolded during the Q&A session. A Chinese attendee asked a professional and thoughtfully articulated question. She began by thanking Professor Picard for her talk and posed this question:

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This needs to change. Asians, like everyone else, have the right to speak out and demand accountability when racism occurs. We will ensure that being racist against Asians has consequences, including here, Professor Picard.

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This choice perpetuates harmful stereotypes about Chinese scholars and reflects a broader bias against Asians, often rooted in the assumption that we “work hard, avoid conflict, and don’t push back.”

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First, it was entirely unnecessary to mention the student’s nationality when discussing an incident of cheating. The point about academic integrity could have been made without emphasizing nationality. Yet, Professor Picard chose to highlight it.

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I deeply respect Professor Picard’s scholarship and contributions to the field. However, her comments during the talk reflected a deeply troubling and racist view of Chinese scholars. This was not just inappropriate but also profoundly disheartening.

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After learning more, I feel compelled to address what I witnessed during an invited talk at NeurIPS 2024 by Professor Rosalind Picard.

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I saw a slide circulating on social media last night while working on a deadline. I didn’t comment immediately because I wanted to understand the full context before speaking.

x.com/xwang_lk/sta...

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Thanks for dropping by!

🌟 Excited to be in Vancouver for #NeurIPS2024 next week! 🌟
Let’s connect about:
- Research 🧠:
🔍 watermarks
🤖 LLM alignment & adversarial robustness
🧩 Multi-agent learning & causality in recommendations
📊 Difficulty profiling for benchmarking LLM performance
- PhD openings at UMD 🎓

As someone who calls herself an AI explorer, always keeping up with the latest technology and trends, today an incredibly engaging 15 min conversation between ChatGPT and a 5 year-old was an eye-opener.
AI has quietly transformed our everyday lives in ways we may not know yet.

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AI even taught him about black holes, the speed of light, and how bacteria can be both helpful and harmful.
The best part? It kept him curious, engaged, and learning in ways I’d never think to explain so simply. 🧠✨

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Leo had the most fascinating chat with AI today! He said things I’ve never heard him say before, like:
🌍 “I live on Earth.”
🐙 Asking if the kraken is real.
🚗💨 “Can Sonic run faster than a car?”
☀️🔥 Comparing meteors to the Sun’s heat.

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