Loading video...

Video Failed to Load

There was a problem loading this video. This could be due to a temporary network issue or the video might be unavailable.

Scaling laws describe how loss changes with scale. Do neurons inside models change predictably too? We study vision and language models up to 30B params and find systematic scaling in neuron universality, specialization, and selectivity. Paper+code: 1/n

Amil Dravid

1,275 subscribers

202,298 views • 9 days ago •via X (Twitter)

Education Science & Technology

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

neuron trying in vain to find and connect with other neurons

neuron trying in vain to find and connect with other neurons

microscopic images.

79,466 views • 1 year ago

Can Vision-Language Models Solve the Shell Game? paper:

Can Vision-Language Models Solve the Shell Game? paper:

AK

38,459 views • 3 months ago

Does off-policy value-based RL scale? In LLMs, larger scale predictably improves performance. Value-based RL learns from arbitrary data and is sample-efficient, but folk wisdom says it doesn't scale 🧵⬇️We show predictability for scaling value-based RL!

Does off-policy value-based RL scale? In LLMs, larger scale predictably improves performance. Value-based RL learns from arbitrary data and is sample-efficient, but folk wisdom says it doesn't scale 🧵⬇️We show predictability for scaling value-based RL!

Oleg Rybkin

23,968 views • 1 year ago

Ever wondered how training dynamics differ between LLMs 🖋️ and Vision 👁️ models? We explore this and close the gap between VMs and LLMs in our #NeurIPS2024 paper "TrAct: Making First-layer Pre-Activations Trainable". Paper📜 Video🎥

Ever wondered how training dynamics differ between LLMs 🖋️ and Vision 👁️ models? We explore this and close the gap between VMs and LLMs in our #NeurIPS2024 paper "TrAct: Making First-layer Pre-Activations Trainable". Paper📜 Video🎥

Felix Petersen

20,898 views • 1 year ago

How do time series foundation models forecast unseen dynamical systems? In new experiments, we find that small transformers learn to approximate transfer operators in-context. (1/N)

How do time series foundation models forecast unseen dynamical systems? In new experiments, we find that small transformers learn to approximate transfer operators in-context. (1/N)

William Gilpin

29,190 views • 3 months ago

E11 Bio is excited to unveil PRISM technology for mapping brain wiring with simple light microscopes. Today, brain mapping in humans and other mammals is bottlenecked by accurate neuron tracing. PRISM uses molecular ID codes and AI to help neurons trace themselves. We discovered a new cell barcoding approach exceeding comparable methods by more than 750x. This is the heart of PRISM. We integrated this capability with microscopy and AI image analysis to automatically trace neurons at high resolution and annotate them with molecular features. This is a key advance towards economically viable brain mapping - 95% of costs stem from neuron tracing. It is also an important step towards democratizing neuron tracing for everyday neuroscience. Solving these problems is critical for curing brain disorders, building safer and human-like AI, and even simulating brain function. In our first pilot study, we acquired a unique dataset in mouse hippocampus. Barcodes improved the accuracy of tracing genetically labelled neurons by 8x – with a clear path to 100x or more. They also permit tracing across spatial gaps – essential for mitigating tissue section loss in whole-brain scaling. Using molecular annotation, we uncover an intriguing feature of synaptic organization, demonstrating how PRISM can be used for systematic discovery 🧵

E11 Bio is excited to unveil PRISM technology for mapping brain wiring with simple light microscopes. Today, brain mapping in humans and other mammals is bottlenecked by accurate neuron tracing. PRISM uses molecular ID codes and AI to help neurons trace themselves. We discovered a new cell barcoding approach exceeding comparable methods by more than 750x. This is the heart of PRISM. We integrated this capability with microscopy and AI image analysis to automatically trace neurons at high resolution and annotate them with molecular features. This is a key advance towards economically viable brain mapping - 95% of costs stem from neuron tracing. It is also an important step towards democratizing neuron tracing for everyday neuroscience. Solving these problems is critical for curing brain disorders, building safer and human-like AI, and even simulating brain function. In our first pilot study, we acquired a unique dataset in mouse hippocampus. Barcodes improved the accuracy of tracing genetically labelled neurons by 8x – with a clear path to 100x or more. They also permit tracing across spatial gaps – essential for mitigating tissue section loss in whole-brain scaling. Using molecular annotation, we uncover an intriguing feature of synaptic organization, demonstrating how PRISM can be used for systematic discovery 🧵

Andrew Payne

111,736 views • 8 months ago

(1/6) X-Humanoid 🤖: Scaling up data for Humanoid Robots. We convert human daily activity videos (from Ego-Exo4D) into humanoid videos (i.e., Tesla Optimus) performing tasks like cooking or fixing a bike. This data can be potentially used to train robot policies and world models. 🔥 Project page: Paper link:

(1/6) X-Humanoid 🤖: Scaling up data for Humanoid Robots. We convert human daily activity videos (from Ego-Exo4D) into humanoid videos (i.e., Tesla Optimus) performing tasks like cooking or fixing a bike. This data can be potentially used to train robot policies and world models. 🔥 Project page: Paper link:

Mike Shou

87,622 views • 6 months ago

Artificial Intelligence (#AI) models that process and interpret visual data are called large vision models. These models, like large language models, are trained on large amounts of data. #midjourney #dalle #stablediffusion (1/8) 🧵 ⤵️

Artificial Intelligence (#AI) models that process and interpret visual data are called large vision models. These models, like large language models, are trained on large amounts of data. #midjourney #dalle #stablediffusion (1/8) 🧵 ⤵️

Nuklai

13,614 views • 2 years ago

My first PhD paper!🎉We learn *diffusion* models for code generation that learn to directly *edit* syntax trees of programs. The result is a system that can incrementally write code, see the execution output, and debug it. 🧵1/n

My first PhD paper!🎉We learn diffusion models for code generation that learn to directly edit syntax trees of programs. The result is a system that can incrementally write code, see the execution output, and debug it. 🧵1/n

Shreyas Kapur

742,394 views • 2 years ago

NVIDIA's CUDA Moat is becoming a Copper Moat with scale-up networking. Bigger models demand more from the scale-up network, and we❤️ Copper

NVIDIA's CUDA Moat is becoming a Copper Moat with scale-up networking. Bigger models demand more from the scale-up network, and we❤️ Copper

SemiAnalysis

38,731 views • 9 months ago

🤖 Does VLA models really listen to language instructions? Maybe not 👀 🚀 Introducing our RSS paper: CodeDiffuser -- using VLM-generated code to bridge the gap between **high-level language** and **low-level visuomotor policy** 🎮 Try the live demo: (1/9)

🤖 Does VLA models really listen to language instructions? Maybe not 👀 🚀 Introducing our RSS paper: CodeDiffuser -- using VLM-generated code to bridge the gap between high-level language and low-level visuomotor policy 🎮 Try the live demo: (1/9)

Yixuan Wang

26,648 views • 1 year ago

Imagine you want to learn about the technology behind LLMs. You instantly get an 100 page book with chapters, content, and structure. What if you find the language too technical? You can change the prompt and the book – all 100 pages – adapts to your needs. What if you want to learn how to code models in python? You can ask for that too, and in seconds, every chapter has code examples alongside the instructional content. That’s the power of an Infinite Bookshelf. Open source, Powered by Groq Inc

Imagine you want to learn about the technology behind LLMs. You instantly get an 100 page book with chapters, content, and structure. What if you find the language too technical? You can change the prompt and the book – all 100 pages – adapts to your needs. What if you want to learn how to code models in python? You can ask for that too, and in seconds, every chapter has code examples alongside the instructional content. That’s the power of an Infinite Bookshelf. Open source, Powered by Groq Inc

Ben Klieger

62,593 views • 1 year ago

Introducing DeepSeek Coder! - SOTA large coding models with params ranging from 1.3B to 33B. - Building games, testing code, fixing bugs, and analyzing data... You dream it, we make it. - Free for commercial use and fully open-source. Try it out now at

Introducing DeepSeek Coder! - SOTA large coding models with params ranging from 1.3B to 33B. - Building games, testing code, fixing bugs, and analyzing data... You dream it, we make it. - Free for commercial use and fully open-source. Try it out now at

DeepSeek

162,557 views • 2 years ago

We just shipped React Native ExecuTorch v0.8.0 – our biggest release yet! 👏🏼 ➡️ Vision Camera integration that runs computer vision models directly on your camera feed, in realtime. Also new: multiple CV models (including RF-DETR (Roboflow) and Liquid AI's Vision Language), Bare React Native support, and more. Takes minutes to try – check out the repo! ⬇️

We just shipped React Native ExecuTorch v0.8.0 – our biggest release yet! 👏🏼 ➡️ Vision Camera integration that runs computer vision models directly on your camera feed, in realtime. Also new: multiple CV models (including RF-DETR (Roboflow) and Liquid AI's Vision Language), Bare React Native support, and more. Takes minutes to try – check out the repo! ⬇️

Software Mansion

36,833 views • 2 months ago

My favorite paper this year: "Video models are zero-shot learners and reasoners" It illustrates that video models show emergent visual reasoning at scale - they can solve vision tasks they weren't trained for. This may be the "GPT moment" for vision. Let's break it down 👇

My favorite paper this year: "Video models are zero-shot learners and reasoners" It illustrates that video models show emergent visual reasoning at scale - they can solve vision tasks they weren't trained for. This may be the "GPT moment" for vision. Let's break it down 👇

Justine Moore

185,001 views • 5 months ago

Chat GPT inside VS Code! Find bugs, add comments, explain code and more! (how to do it yourself in the next tweet)

Chat GPT inside VS Code! Find bugs, add comments, explain code and more! (how to do it yourself in the next tweet)

Jarrod Watts

256,447 views • 3 years ago

TOMORROW, 9:30AM PT We go live with an Odyssey announcement (or two) I've been looking forward to for some time. I think these releases will make where world models are headed, and how powerful they can become, much more obvious. So hyped. GPUs scaling as we speak.

TOMORROW, 9:30AM PT We go live with an Odyssey announcement (or two) I've been looking forward to for some time. I think these releases will make where world models are headed, and how powerful they can become, much more obvious. So hyped. GPUs scaling as we speak.

Oliver Cameron

77,910 views • 28 days ago

🚨New paper! Generative models are often “miscalibrated”. We calibrate diffusion models, LLMs, and more to meet desired distributional properties. E.g. we finetune protein models to better match the diversity of natural proteins.

🚨New paper! Generative models are often “miscalibrated”. We calibrate diffusion models, LLMs, and more to meet desired distributional properties. E.g. we finetune protein models to better match the diversity of natural proteins.

Brian L Trippe

20,445 views • 8 months ago

HOW TO CREATE ANIME EYEBALL EFFECT IN 3D - thread This assumes you already know how to model in Blender and set up materials in Unity! This method works with .vrm models. 1/?

HOW TO CREATE ANIME EYEBALL EFFECT IN 3D - thread This assumes you already know how to model in Blender and set up materials in Unity! This method works with .vrm models. 1/?

刻矛 MAO 🔆🪱🔆 3D Modeler

64,540 views • 3 years ago

ok but now combine it with Apple vision models and Expo Agent

ok but now combine it with Apple vision models and Expo Agent

Evan Bacon 🥓

78,788 views • 2 months ago