Florian Toqué

[Florian Toqué @floriantoque]

@RemiCadene Let’s go 🦾

[Remi Cadene @RemiCadene]

I am starting a venture on top of LeRobot!

We’re at a pivotal time. AI is moving beyond digital to the physical world. Embodied AI will change our surroundings in ways we can barely imagine. This technology holds the potential to empower everyone. It must not be controlled by just a few.

This conviction led me to propose an ambitious open-source AI robotics project to Thom, Clem, and Julien back in 2024. Hugging Face, home to a community of millions of AI builders and a team of experts who brought us transformers, datasets, and the Hugging Face Hub, was the perfect place to launch LeRobot.

I’m incredibly grateful for all the support that allowed me to build LeRobot alongside an amazing team and community. In such a short time, we built one of the most adopted open-source robotics platforms, used by startups, universities, and research labs. It is helping countless people take their first steps in robotics. Together, we’ve even assembled the world’s largest open robotics dataset. And this is only the beginning for LeRobot!

Building on this momentum, I now feel the urgency to start something new on top of LeRobot. It will push the limit of what robots are capable of and commoditize them within society. Like LeRobot, it will start in Paris, leveraging its vibrant international AI scene. Stay tuned!

As LeRobot continues to expand, it’s now in the best possible hands with @AractingiMichel, @pepijn2233 and Steven Palma taking the lead. Watching the team deliver exceptional results over the last weeks has been one of the most rewarding experiences. Their creativity, dedication, and capability to ship fast is proving just how strong the team is today!

I am extremely grateful to the many people who contributed to making LeRobot at Hugging Face and within its powerful community. Many thanks to Thom, Clem, Julien, Simon, Rob, Michel, Pepijn, Steven, Gloria, Adil, Martino, Caroline, Marine, Mishig, Guillaume, Pablo, Lysandre, Arthur, Quentin, Florent, Brigitte, Victor, Marina, Mustafa, Francesco, Jess, Jade, Ville, Leo, Max, Julien, Alexander, Flavien, Raphael, Adina, Tao, Dana, Batu, Olivier, Matthieu, Eugene, Theo, Guilherme, Hynek, Loubna, Clémentine, Merve, Vaibhav, Anna, Jeff, Adrien, Emily, Johanne, Adrien and others. There are too many of you to be all named!

Thanks again and see you soon!!! :)

~ Remi

[Florian Toqué @floriantoque]

@Ligne8_RATP J’ai arrêté de prendre la 8 il y a deux semaines. Maintenant c’est ligne 6 puis ligne 9. C’est un tout petit peu plus long mais ça évite les galères qui arrivent tous les jours sur cette horrible ligne 8. Courage à ceux qui n’ont pas d’itinéraire de secours! #ligne8 #pireligne

[Yann LeCun @ylecun]

This is huge: Llama-v2 is open source, with a license that authorizes commercial use!

This is going to change the landscape of the LLM market.
Llama-v2 is available on Microsoft Azure and will be available on AWS, Hugging Face and other providers

Pretrained and fine-tuned models are available with 7B, 13B and 70B parameters.

Llama-2 website: https://t.co/PKrrXgHdem
Llama-2 paper: https://t.co/aINNrXNhMb

A number of personalities from industry and academia have endorsed our open source approach: https://t.co/N7HwgW9Suh

[Sebastian Raschka @rasbt]

Weight decay (with AdamW) can yield a significant improvement in data efficiency compared to other methods (like Dropout), reducing the required amount of samples by over 50%
(via "Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets, https://t.co/WT4mhyk3ww)

[AI Pub @ai__pub]

Best of AI Twitter (Jan 2 - 11):

- Microsoft investing $10B to own 49% of OpenAI,
- MedPaLM matches human doctors in medical accuracy,
- The "LLM uncanny valley",
- SOTA Muse and VALL-E models,
- Stability AI's "DeepFloyd": a mysterious new research lab...

... and more:

1/17

[Sebastian Raschka @rasbt]

Convolutional networks strike back, again. The fully convolutional ConvNeXt v2 extends the successful ConvNeXt architecture by adding self-supervised learning capabilities.

What's new?
1/5

[AK @_akhaliq]

Box2Mask: Box-supervised Instance Segmentation via Level-set Evolution
abs: https://t.co/qaJuEd6D2g
github: https://t.co/Gu7DpTTUWf

[Jim Fan @DrJimFan]

Transformers are all the rage today. But neither DALLE nor Stable Diffusion uses Transformer for image generation. Instead, they rely on a 7-year-old, Jurassic-era neural architecture. Why? 🤷🏾‍♀️⁉️ It’s finally time for Transformer and Diffusion to join forces! Quick 🧵👇:

[Alexandre Ramé @ramealexandre]

Ready to give your deep models a second life? Introducing model ♻️ recycling (https://t.co/ReB2ruXxsY), improving generalization by reusing weights fine-tuned on various vision tasks. Just like you recycle your bottles and cardboards, it's time to start recycling your models too!

[Frank Dellaert @fdellaert]

One of the last things I did at Google AI was writing a minimal jax/flax version of voxel-based NeRF, with Pedro Velez, and it was finally open-sourced: https://t.co/Tsv1vz5nL2

[AK @_akhaliq]

Riffusion, real-time music generation with stable diffusion

@huggingface model: https://t.co/on5deEcQS1
project page: https://t.co/FcPhFpzKmB

[317070 @317070]

Did you know, that you can build a virtual machine inside ChatGPT? And that you can use this machine to create files, program and even browse the internet? https://t.co/15IwHwr2on

[Julien Colin @juliencolin_]

[1/5] Much of the progress on attribution methods has been driven by theoretical metrics --without much consideration for human end-users

Our #NeurIPS22 paper investigates whether progress has translated to explanations more useful in real-world scenarios
https://t.co/NOnAhBKk0p

[Thomas Fel @thomas_fel_]

Hi 👋 !

Interested in explainability or planning to use XAI in your future projects?

Then you might be interested in this 🧵, let me introduce you to Xplique! https://t.co/JXi8LjKa7p

with @tserre, @RemiCadene, @ChalviM, @juliencolin_, @LouisBAlgue, @Novelloop, @ANITI_Toulouse

[AK @_akhaliq]

DiffusionDet: Diffusion Model for Object Detection
abs: https://t.co/GBu4bZPwKp
github: https://t.co/bWHxGxayjB

[Rishabh (in SF) @rishabh16_]

Interesting paper from CVPR 2021 that shows the efficacy embedding faces in *spherical spaces* as opposed to general Euclidean. Their "Sphere Confidence Face" (SCF) embeddings beat existing embedding techniques for face recognition with a ResNet🤔

https://t.co/ECBYJTKpXf

[Dave @dmvaldman]

AI-powered pull requests in GitHub demoed at #GitHubUniverse

In a year we went from autocomplete to auto PR. Auto app is probably similar in magnitude. ~100s of completions in a PR, ~100s of PRs in an app.

https://t.co/URFxsJMATz

[Drew Linsley @DrewLinsley]

Check out our new paper, to appear at NeurIPS. We show that DNNs are becoming progressively *less* aligned with human perception as their ImageNet accuracy increases. Ignore the elections, Elon, and FTX for a moment — this is important!
https://t.co/w3HJFpzxIt

[Sebastian Raschka @rasbt]

Are you profiling your deep learning code to find performance bottlenecks? If yes, what are your go-to tools?

1) official PyTorch Profile, operator-call level: https://t.co/J7DjfMJwzf

2) a new, helpful community project for profiling at the layer level: https://t.co/JDNy7M4hKj