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Pure attention architecture for vision? Not enough
IMHO there are many cool aspects to this work, but I’d like to call out page 16 - which contains the entire model code, and isn’t even full.
IMHO there are many cool aspects to this work, but I’d like to call out page 16 - which contains the entire model code, and isn’t even full.
If you are at #ICLR2019, make sure that you learn about our work, "Universal Transformers", w/ @sgouws, @OriolVinyalsML, @kyosu, and @lukaszkaiser (Thursday 11am-1pm, poster session at Great Hall BC, #62). You can also check out this blog post about UT: mostafadehghani.com/2019/05/05/uni…
Check out Universal Transformers, new research from the Google Brain team & @DeepMindAI that extends last year's Transformer (a neural network architecture based on a self-attention mechanism) to be computationally universal. goo.gl/j4jWnu
Ashish and Noam presenting our work on Transformers at NIPS right now! Come to our poster tonight! #NIPS2017
Check out the Transformer, a novel NN architecture based on a self-attention mechanism that is well-suited for NLU goo.gl/qxN6ej
AK @_akhaliq
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Delip Rao e/σ @deliprao
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(((ل()(ل() 'yoav))).. @yoavgo
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Delip Rao e/σ @deliprao
46K Followers 5K Following Busy inventing the shipwreck. @Penn. Past: @johnshopkins, @UCSC, @Amazon, @Twitter ||Art: #NLProc, Vision, Speech, #DeepLearning || Life: 道元, improv, running 🌈
George Hotz 🌑 @realGeorgeHotz
248K Followers 174 Following President @comma_ai. Founder @__tinygrad__
Sasha Rush @srush_nlp
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Oriol Vinyals @OriolVinyalsML
167K Followers 82 Following VP of Research & Deep Learning Lead, Google DeepMind. Gemini co-lead. Past: AlphaStar, AlphaFold, AlphaCode, WaveNet, seq2seq, distillation, TF.
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370K Followers 2 Following towards a plurality of humanity loving AGIs @openai
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61K Followers 2K Following a combination of a mediocre scientist, a mediocre manager, a mediocre advisor & a mediocre PC at @nyuniversity (@CILVRatNYU) & @genentech (@PrescientDesign).
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101K Followers 971 Following CEO @youSearchEngine Investing at @aixventuresHQ Before: Stanford Adj Prof in AI/NLP, Chief Scientist at Salesforce, MetaMind
Ofir Press @OfirPress
10K Followers 3K Following I build tough benchmarks for LMs and then I get the LMs to solve them. Postdoc @Princeton. PhD from @nlpnoah @UW. Ex-visiting researcher @MetaAI & @MosaicML.
Tara Basu Trivedi @tbt94
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Caglar Gulcehre @caglarml
4K Followers 1K Following ML Researcher Prof @ EPFL, PI @ CLAIRE lab Ex: Staff Research Scientist @ Deepmind, MSR, IBM Research Follow me on Mastodon: https://t.co/LZ5sWt7Asj
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5K Followers 991 Following making stuff 3D. formerly research scientist at Google, phd at Berkeley.
BioNTech SE @BioNTech_Group
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14K Followers 820 Following Assoc. Prof in ML @UniofOxford @StAnnesCollege @FLAIR_Ox, dad. Ex: {RS @MetaAI, (A)PM @Google, DivStrat @GS}, ex intern {@GoogleDeepmind, @GoogleBrain, @OpenAI}
Gandeeva Therapeutics @Gandeeva_Tx
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Elon Musk @elonmusk
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Mohit Bansal @mohitban47
9K Followers 651 Following Parker Distinguished Professor, UNC Chapel Hill (@unc). Director https://t.co/5qlPVgnrlN (@uncnlp). Prev: @Berkeley_AI, @TTIC_Connect @IITKanpur #NLP, #CV, #AI, #ML
Matthew Johnson @SingularMattrix
12K Followers 3K Following Researcher at Google Brain. I work on JAX (https://t.co/UGa5tGfinF).
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3K Followers 999 Following researcher/engineer @AnthropicAI | former @GoogleDeepMind @mitbrainandcog @zotero | @[email protected]
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ramsey homsany @rhomsany
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13K Followers 296 Following Assistant Professor @ MIT, leading the Scene Representation Group (https://t.co/h5gvhLYrtw). Neural scene reps., neural rendering, inverse graphics.
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70K Followers 282 Following Board of Governors Professor of Chemistry and Chemical Biology @RutgersU @BiosafetyNow
Sepp Hochreiter @HochreiterSepp
10K Followers 395 Following Pioneer of Deep Learning and known for vanishing gradient and the LSTM. I mostly tweet about random ArXiv papers which sparked my interest.
Lilian Weng @lilianweng
95K Followers 148 Following Working on AI safety, past on robotics, applied research @OpenAI; Writing ML blogs to help myself & others to learn; Ideas my own.
Sergey Levine @svlevine
80K Followers 122 Following Associate Professor at UC Berkeley Co-founder, Physical Intelligence
D. Sivakumar @dsivakumar
4K Followers 852 Following Co-Founder: https://t.co/CHdqCYS1My (Commerce Search with NLP Magic) Earlier: Member, @southpkcommons; NLP, ML, Algorithms at Google Research; CS Theory at IBM Almaden .
Byron Reese @byronreese
4K Followers 3K Following Futurist, technologist, entrepreneur, bestselling author. I write about AI & deciphering our destiny. New Book: We Are Agora https://t.co/VBCy6tuZYJ
Marie Vidal @mv_mvidal
254 Followers 723 Following Innovation & industry manager @BIMSB_MDC @Virchow2_0 @LifeTimeIni previously @circrtrain @CORBEL_eu @singlecellomics
Runway @runwayml
185K Followers 300 Following An applied AI research company building for the next era of art, entertainment and human creativity. We're hiring: https://t.co/Aj11xyhxOgIf you ever actually looked at these benchmarks, the model predictions, and what the claimed "human performance" means, you would know. Hint: it's unrelated to intelligence. Looks like many people, especially more prominent ones, are commenting and opining blindly.
Interesting how in all these domains AI is asymptoting at roughly human performance - where's the AI zooming past us to superintelligence that Kurzweil etc. predicted/feared?
> be me > on vacation > kid asleep, wife away > but I'm not tired! > whip out colab > load my model > import new benchmark > try my model > tfw sota, sota by far > double-check for bugs or leaks > no bug found > no leak found idk man, probably a bug. Also, twitter is reddit now.
At this point I feel like we understand pretty well what's going on with LLMs: - Outputs are roughly equivalent to kernel smoothing over positional embeddings (arxiv.org/pdf/1908.11775…) - The learned computation model is *probably* bounded by RASP-L (arxiv.org/pdf/2310.16028…) -…
So much potential🥹 1: One moment, let me state the vision No more sliding windows, we on a mission! udio.com/songs/mfsfmg5V… 2: No 1x1, that's just a view @ylecun can't argue, our models slew udio.com/songs/eb2bmgzT…
Happy to share - blah blah blah. Gemma + Griffin = RecurrentGemma Competitive quality with Gemma-2B and much better throughput, especially for long sequences. Cracked model from cracked team! Check it out below 👇
Releasing RecurrentGemma - one of the strongest 2B-param open models designed for fast inference on long sequences and massive throughput! Both pre-trained and IT checkpoints available + code - try them out here! Code: github.com/google-deepmin… Weights: kaggle.com/models/google/…
@DannyMcAteer8 I was in Bhutan and, unsurprisingly, there was a lot of meditating. An excellent place for that, and really loved the place/people/culture there.
Returning from an experimental ~2 week detox from the internet. Main takeaway is that I didn't realize how unsettled the mind can get when over-stimulating on problems/information (like a stirred liquid), and ~2 weeks is enough to settle into a lot more zen state. I'm struck by…
@demishassabis @GoogleDeepMind Congrats, Sir Demis!
Slowly but surely freeing us from the discretization in tokenization! Now the code and checkpoints of GIVT are available, so you can play around with them:
We just released a big 🎁GIVT update! 📈 Larger models and improved image generation results across the board 💡 Improved GMM formulation and adapter module 💻 Code, model checkpoints, and a colab are now available at github.com/google-researc… More details below... 1/
Decoder-only models only work with discrete tokens, right? 🤔 Excited to present 🎁GIVT: Generative Infinite-Vocabulary Transformers, a simple way to generate arbitrary vector sequences with real-valued entries using transformer decoder-only models! arxiv.org/abs/2312.02116 1/
We just released a big 🎁GIVT update! 📈 Larger models and improved image generation results across the board 💡 Improved GMM formulation and adapter module 💻 Code, model checkpoints, and a colab are now available at github.com/google-researc… More details below... 1/
Decoder-only models only work with discrete tokens, right? 🤔 Excited to present 🎁GIVT: Generative Infinite-Vocabulary Transformers, a simple way to generate arbitrary vector sequences with real-valued entries using transformer decoder-only models! arxiv.org/abs/2312.02116 1/
It was so great to see almost everyone (we missed you @nikiparmar09!!) from the Transformer paper again. We still haven't all been in the same room at the same time, but we'll make it happen one day. @lukaszkaiser @kyosu @ashVaswani @ilblackdragon @YesThisIsLion
@ylecun @StevenLevy @DBahdanau @kchonyc Self-attention is NOT the innovation in Transformers. Others had used it before as is cited in their background section. The contribution of the realization that you no longer need recurrence, which, together with causal masking, enables training parallelization.
I’m really excited to be starting a new adventure with multiple amazing friends & colleagues. Our company is called Physical Intelligence (Pi or π, like the policy). A short thread 🧵
I am incredibly proud to be able to put this paper out finally! This paper shows that hybrid linear RNNs (Griffin) combined with local attention (or sliding window attention) can be incredibly efficient at language modeling.
We present Griffin: A hybrid model mixing a gated linear recurrence with local attention. This combination is extremely effective: it preserves all the efficient benefits of linear RNNs and the expressiveness of transformers. Scaled up to 14B! arxiv.org/abs/2402.19427
I agree, this year will be wild for foundation world models! Thanks a lot Jim 🙏 I need to emphasize that I was lucky to work with an incredibly talented team: Jake Bruce, @MichaelD1729, @ashrewards, @jparkerholder, @YugeTen, @edwardfhughes, Matthew Lai, @aditimavalankar,…
Tim is one of the most imaginative researchers I know, and Genie is one of his most imaginative works. Unlike Sora, Genie is actually a proper action-driven world model with inferred actions. 2024 will also be the Year of Foundation World Models! Congrats 👏👏
Rather than adding inductive biases, we focus on scale. We use a dataset of >200k hours of videos from 2D platformers and train an 11B world model. In an unsupervised way, Genie learns diverse latent actions that control characters in a consistent manner.
I am really excited to reveal what @GoogleDeepMind's Open Endedness Team has been up to 🚀. We introduce Genie 🧞, a foundation world model trained exclusively from Internet videos that can generate an endless variety of action-controllable 2D worlds given image prompts.
Love letter to @obsdmd to which I very happily switched to for my personal notes. My primary interest in Obsidian is not even for note taking specifically, it is that Obsidian is around the state of the art of a philosophy of software and what it could be. - Your notes are…
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