We use LLMs for everyday tasks—research, writing, coding, decision-making. They remember our conversations, adapt to our needs and preferences. Naturally, we trust them more with repeated use. But this growing trust might be masking a hidden risk: what if their beliefs are

On #WorldQuantumDay, we highlight our research on the Vendi Score, which leverages the von Neumann entropy of quantum mechanics to tackle challenges in AI/ML and the natural sciences 🧵🧵🧵👇 1/n

So with all the advances in robotics and AI, when will Roombas stop “falling” into “ditches”? Asking for a friend 😅

Here’s an easy trick for improving the performance of gradient-boosted decision trees like XGBoost allowing them to read text column headers and to benefit from massive pretraining: replace the first tree with an LLM or TabPFN! 🧵 1/9

Our updated Responsible Foundation Model Development Cheatsheet (250+ tools & resources) is now officially accepted to @TmlrOrg 2025! It covers: - data sourcing, - documentation, - environmental impact, - risk eval - model release & licensing

Today, we are publishing the first-ever International AI Safety Report, backed by 30 countries and the OECD, UN, and EU. It summarises the state of the science on AI capabilities and risks, and how to mitigate those risks. 🧵 Link to full Report: https://t.co/k9ggxL7i66 1/16

We have to take the LLMs to school. When you open any textbook, you'll see three major types of information: 1. Background information / exposition. The meat of the textbook that explains concepts. As you attend over it, your brain is training on that data. This is equivalent

For something to be open source, we need to see 1. Data it was trained & evaluated on 2. Code 3. Model architecture 4. Model weights. DeepSeek only gives 3, 4. And I'll see the day that anyone gives us #1 without being forced to do so, because all of them are stealing data.

🚀Excited to share our latest paper, *ResearchTown*, a framework for research community simulation using LLMs and graphs 🌟We hope this marks a new step toward automated research 🧠Paper: https://t.co/xz18Kpc6q5 💻Code: https://t.co/y1Dn9A0LQZ 🤗Dataset:

Everything you love about generative models — now powered by real physics! Announcing the Genesis project — after a 24-month large-scale research collaboration involving over 20 research labs — a generative physics engine able to generate 4D dynamical worlds powered by a physics

New research reveals a worrying trend: AI's data practices risk concentrating power overwhelmingly in the hands of dominant technology companies. I spoke w/@ShayneRedford @sarahookr @sarahbmyers @GiadaPistilli about what this says about the state of AI

Announcing the NeurIPS 2024 Test of Time Paper Awards: https://t.co/Wyhx63A2Ek

Just finished my first ever review of a paper (NeurIPS workshop). Revelatory experience! Would certainly help in becoming a better researcher myself. Tried to ensure I review how I would want my own work to be reviewed: critically yet constructively! #NeurIPS #MachineLearning

Thank you for sharing our work @_akhaliq!! Further details about our work and results here: https://t.co/JTfQiOR2dN Would love to hear everyone’s thoughts And, we’ve tried hard to create an easy to use code repository here. Try it out yourself: https://t.co/nCswyxyrv9 🚀

A pleasure and honor to work with such an incredible group @jyx_su, @jeremy_irvin16, @ji_hun_wang on this paper, supervised by @jonc101x and @AndrewYNg! Would love to hear thoughts and feedback from everyone 🤗

Want to unlock the full potential of GPT-4o & Gemini 1.5 Pro? Give them **many** demonstrations and batch your queries! Our new work w/ @AndrewYNg shows these models can benefit substantially from lots of demo examples and asking many questions at once! https://t.co/SeAqBRFyy7