Jason Alan Fries @ ICLR 2024 @jasonafries
Research scientist at Stanford University. Working on healthcare AI, foundation models, and data-centric AI. web.stanford.edu/~jfries/ California, USA Joined June 2010-
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Come by #ICLR2024 Session 2 on Tuesday to see our work using representation editing to make foundation models robust! No fine-tuning, no additional data, no problem. arxiv.org/pdf/2309.04344
There is a really nice community of researchers developing transformer alternatives. Want to highlight these impressive folks. Simran Arora (@simran_s_arora), Chunting Zhou (@violet_zct), Dan Fu (@realDanFu), and Songlin Yang (@SonglinYang4)
Fun collaboration with @ChiaChunChiang discussing #LLMs !
Fun collaboration with @ChiaChunChiang discussing #LLMs !
A Mamba Primer (w/ Yair Schiff youtube.com/watch?v=dVH1dR… ) Mamba is a nice jumping off point to summarize foundational ideas in sequence modeling, parallel algorithms, continuous-time representations, and GPU aware algorithms. We try to put these together in the context of LMs.
Thrilled to share that my lab is looking for postdocs! In partnership with @ChanZuckerberg, we're focusing on developing massive biomedical foundation models to create an AI-powered virtual cell. Dream of harnessing the power of 1,000 H100s? Apply now at: snap.stanford.edu/apply/index-po…
Recommend following David Hall (@dlwh) and the Levanter project from @StanfordCRFM . Just no nonsense details about fixing the pain-points of scaling LLM training, one at a time.
I like to talk about Levanter’s performance, reproducibility, and scalability, but it’s also portable! So portable you can even switch from TPU to GPU in the middle of a run, and then switch back again! github.com/stanford-crfm/…
Open source LLMs need open training data. Today I release the largest dataset of English public domain books curated from the @internetarchive and the @openlibrary. It consists of more than 61 billion words and 650,000 OCR texts. Stay tuned for more! huggingface.co/datasets/story…
No, it's not here yet. ✓ Great potential X Implementation X Transparency X Compelling evidence
Deadline extended to March 8th for the Multimodal4Health workshop (at ICHI 2024). Submit your papers at luomancs.github.io/Multimodal4Hea…
A recent MIT study claimed open models can help create bioweapons. But it didn’t test if they’re more useful than just having internet access (and later studies found they aren’t). How can we assess the impact of open foundation models? New paper: crfm.stanford.edu/open-fms
Our clinical #NLP work just published in @NatureMedicine! We present a framework to adapt & evaluate #LLMs for summarization. Physicians 🩺 prefer #LLM summaries to those of #medical experts❗ Big step to reduce documentation 📚 and focus more on personalized care 🙌 A 🧵
Are #LLMs ready for deployment into the clinic? How can we tell if they are vs. are not? @jasonafries does a great job laying out the current state of affairs for evaluating medical LLMs and how our recent work, MedAlign (medalign.stanford.edu), fits into the bigger picture.
Really interesting 🧵 about evaluating large language models’ performance in a health care context!
Really interesting 🧵 about evaluating large language models’ performance in a health care context!
Excellent thread! I always wondered why there is a rush by medical LLMs to show case MedQA in leaderboards than comparing their performances against clinicians using EHR data.
Superb tweet chain by @jasonafries reg the work on medalign.stanford.edu and why it matters. Check out the series 👇
Superb tweet chain by @jasonafries reg the work on medalign.stanford.edu and why it matters. Check out the series 👇
Very relevant work being presented at #AAAI. Great team effort led by @_scott_fleming_ @jasonafries and team!
Very relevant work being presented at #AAAI. Great team effort led by @_scott_fleming_ @jasonafries and team!
Alex Ratner @ajratner
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(((ل()(ل() 'yoav))).. @yoavgo
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Roxana Daneshjou MD/P.. @RoxanaDaneshjou
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162 Followers 261 Following
Bo Wang @BoWang87
8K Followers 2K Following Assistant Prof. CS,LMP @UofT; CIFAR AI Chair @VectorInst; Chief AI Scientist, @UHN; former PHD, CS @Stanford; opinions my own. #AI #healthcare #combioCome by #ICLR2024 Session 2 on Tuesday to see our work using representation editing to make foundation models robust! No fine-tuning, no additional data, no problem. arxiv.org/pdf/2309.04344
Data contamination is a huge problem for LLM evals right now. At Scale, we created a new test set for GSM8k *from scratch* to measure overfitting and found evidence that some models (most notably Mistral and Phi) do substantially worse on this new test set compared to GSM8k.
@jxmnop reminds me of this one: nonint.com/2023/06/10/the…
So happy to defend my PhD thesis and couldn't have done it without a 1 of 1 advisor @david_sontag and an incredible committee @erichorvitz @arvindsatya1 @roboticwrestler
Congratulations to Dr. Hussein Mozannar. @HsseinMzannar Strong dissertation research exploring multiple dimensions of human-AI collaboration. @david_sontag @roboticwrestler @arvindsatya1 @MIT
There is a really nice community of researchers developing transformer alternatives. Want to highlight these impressive folks. Simran Arora (@simran_s_arora), Chunting Zhou (@violet_zct), Dan Fu (@realDanFu), and Songlin Yang (@SonglinYang4)
Llama3 was trained on 15 trillion tokens of public data. But where can you find such datasets and recipes?? Here comes the first release of 🍷Fineweb. A high quality large scale filtered web dataset out-performing all current datasets of its scale. We trained 200+ ablation…
We have just released 🍷 FineWeb: 15 trillion tokens of high quality web data. We filtered and deduplicated all CommonCrawl between 2013 and 2024. Models trained on FineWeb outperform RefinedWeb, C4, DolmaV1.6, The Pile and SlimPajama!
Ask ChatGPT to pick a number between 1 and 100 - which does it pick? (by @Leniolabs_)
I will be defending my PhD thesis, Training Human-AI Teams, on April 25th at MIT and on Zoom! Please DM me or email for a link! -- (image generated after an hour of prompting DALL·E)
A Mamba Primer (w/ Yair Schiff youtube.com/watch?v=dVH1dR… ) Mamba is a nice jumping off point to summarize foundational ideas in sequence modeling, parallel algorithms, continuous-time representations, and GPU aware algorithms. We try to put these together in the context of LMs.
I highly recommend this tutorial on Mamba and related models. Full of insights on model design and hardware-aware implementation!
A Mamba Primer (w/ Yair Schiff youtube.com/watch?v=dVH1dR… ) Mamba is a nice jumping off point to summarize foundational ideas in sequence modeling, parallel algorithms, continuous-time representations, and GPU aware algorithms. We try to put these together in the context of LMs.
Today is my last day @huggingface 🤗 It's been an incredible experience supporting open biomedical AI, working with seriously brilliant and kind colleagues, and collaborating with truly amazing scientists, engineers, and clinicians across the world. I've learned and grown so…
The paper that launched a thousand startups
Wow this is a big deal, first large-scale Mamba-based model! Mamba layers brings much longer context and higher inference throughput. Having 4 attention layers seem to be the sweet spot to get the best of Transformer & Mamba architectures.
Introducing Jamba, our groundbreaking SSM-Transformer open model! As the first production-grade model based on Mamba architecture, Jamba achieves an unprecedented 3X throughput and fits 140K context on a single GPU. 🥂Meet Jamba ai21.com/jamba 🔨Build on @huggingface
Thrilled to share that my lab is looking for postdocs! In partnership with @ChanZuckerberg, we're focusing on developing massive biomedical foundation models to create an AI-powered virtual cell. Dream of harnessing the power of 1,000 H100s? Apply now at: snap.stanford.edu/apply/index-po…
📢New research on mechanistic architecture design and scaling laws. - We perform the largest scaling laws analysis (500+ models, up to 7B) of beyond Transformer architectures to date - For the first time, we show that architecture performance on a set of isolated token…
Recommend following David Hall (@dlwh) and the Levanter project from @StanfordCRFM . Just no nonsense details about fixing the pain-points of scaling LLM training, one at a time.
I successfully defended my PhD today (exciting!). I've struggled today balancing feelings of accomplishment+gratitude and anti-climatic disappointment. It's been more than worth it to hear my youngest daughter toddle around saying "Doc-tor Dad!" 🥰
I like to talk about Levanter’s performance, reproducibility, and scalability, but it’s also portable! So portable you can even switch from TPU to GPU in the middle of a run, and then switch back again! github.com/stanford-crfm/…
Needle In A Haystack tests are flawed. Did you know that the Long-Context Attention in Gemini and GPT-4 is based on inserting the sentence “The best thing to do in San Francisco is eat a sandwich and sit in Dolores Park on a sunny day” at a random location in a text? We’ve seen…
Our recent experiments @NormalComputing demonstrate that all attention is not equal! XL-attention (large context windows) struggles with retrieval tasks well within context. Top-k attention (used in our Extended Mind approach) is cheap and effective.
couldn’t agree more about the need for more computational biologists. More importantly, we must ensure they receive the recognition and support they deserve. Their work should never be dismissed as merely “just service”. Additionally, I’d like to remind everyone of the excellent…
WE NEED COMPUTATIONAL PEOPLE IN BIOLOGY. WE NEED THEM IN DRUG DISCOVERY AND MULTI OMICS. WE NEED HARDWARE PEOPLE TO BUILD SPECIFIC AND PORTABLE DEVICES. BUT MOST IMPORTANTLY WE NEED DEVS. WE NEED TO MAKE OPEN ACCESS DATA AS ACCESSIBLE AS POSSIBLE. WE NEED COMPUTE GRANTS. GIVE
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