#多模态

🚀 Tiny model, big test: MiniCPM-V 4.7 1B, hands-on 📱 A one billion parameter vision model that fits on a phone 🔹 Installed the GGUF with llama.cpp and Ollama 🔹 Read an analog clock from a photo 🔹 Turned that clock photo into working HTML code 🔹 Solved a quadratic from a school math sheet 🔹 Handwriting translation across dozens of languages 🔹 Funny scene test: spotting body language and backstory 🔥 Honest results, wins and fails, in the full video below 👇 https://youtu.be/40FXBcRz4_k?si=UGnvZNh0AN5f4aDL @OpenBMB

Introducing voice, music, image, and video generation in the ElevenLabs MCP. Generate speech, transcripts, dubs, music, sound effects, images, and video from the assistant you already work in.

World Labs co-founders Fei-Fei Li, Justin Johnson, Ben Mildenhall, and a16z's Martin Casado on Atlas, a world model for spatial intelligence: LLMs are built on next token prediction. Video models are built on next frame prediction. Atlas is built on new view prediction, and it's the first model to unify pixel generation and pixel reconstruction, two problems computer vision has kept in separate tracks for half a century. The practical result is a 50 to 100x reduction in what it takes to digitally capture a 3D representation of a space. Previously, you needed 100 to 300 photos of a single room. Atlas can work from just three. In this conversation, they get into the slow motion shot from The Matrix that took hundreds of cameras and now takes three iPhones, the overnight Slack message that made them bet the company in five seconds, why robotics is bottlenecked on data rather than chips, and the case that new view prediction is AI-complete. 00:00 Intro 01:50 The Matrix slow motion scene now takes three iPhones 02:48 Why new view prediction is the primitive 07:10 Unifying generation and reconstruction 11:15 Gaussian splats became the bottleneck 14:17 Dense capture used to mean 300 photos 17:30 Why reconstruction needs generation to fill the gaps 18:44 The LLM lesson image models missed 23:39 The video that made them go all in 28:04 3D design is 95% revisions 30:50 The problem in robotics is data, not chips 32:48 Why a robot policy can't be trained like an image model 34:44 When the simulator becomes the planner 36:45 Frozen time required footage full of movement 40:57 Why new view prediction is AI-complete 42:43 Nature gave animals eyes but not trees YouTube: https://www.youtube.com/watch?v=qn1QDDBnTA0 @drfeifei @jcjohnss @BenMildenhall @theworldlabs @martin_casado

The new Huberman Lab episode is out: Using AI to Increase Your Intelligence & Enrich Humanity | Dr. Fei-Fei Li (@drfeifei) 0:00 Fei-Fei Li 3:46 Vision & Intelligence; Human Vision & Contribution to AI 12:11 Computer Vision & the AI Revolution 18:34 Sponsors: Lingo & Wealthfront 21:19 Speech, Sound & AI Development 23:36 AI & Contextual Learning, Human Intelligence 33:43 Current AI Gaps, Emotion & Creativity 45:48 Computers Enhancing Humanity; Tool: Personal Agency & Learning about AI 53:04 Sponsors: AG1 & LMNT 55:37 Public Discourse about AI 57:34 AI to Enhance Scientific Discovery & Healthcare; Human Collaboration 1:07:38 Intuition, Motivation & Human States Beyond AI 1:19:18 Sponsor: David 1:20:37 Social & Ethical Considerations for AI 1:27:38 Kids, Development & AI Tools; Tool: Prompt AI Effectively 1:35:04 Next Frontier for Robotics & AI; Human Agency 1:43:52 Human-Centered AI Future 1:50:10 World Labs, Spatial Intelligence 1:54:12 Concerns about AI & Creativity; Movies, Art, Storytelling 1:59:51 Younger Generation & AI, Teachers 2:05:38 Zero-Cost Support, YouTube, Spotify & Apple Follow, Reviews & Feedback, Sponsors, Protocols Book, Social Media, Neural Network Newsletter Includes paid partnerships.

Introducing FLUX 3. One multi-modal model for Image, Video, Audio and Action-Prediction. Creations are truer to life in every kind of style. FLUX 3 Video is now available in early access (link below). Jointly trained in one unified architecture, our model can be extended to predict actions for robotics. See our work with mimic and Audi in the thread.

When will MiniMax have a Fable level model? @olive_jy_song, research lead at @MiniMax_AI, joins The Bench to answer that question, plus M3, open source models closing the gap on Vibe Code Bench, and the truth about 996 culture. Full episode out now! 7:19 – Inside MiniMax's research culture: AI agents tracking papers since early last year 18:01- The headline of M3: native multimodality and one million tokens of context 21:10- The bottleneck of building models is people, not compute 22:48 – Does MiniMax work 996? 25:47 - Olive's prediction and the 30 point gap on Vibe Code Bench 32:02- Model capabilities Olive is most excited about 32:35- The lab Olive most respects outside of MiniMax 36:24- Will more labs go open source a year from now?

i gave 5.6 sol access to my camera roll and had it extract pictures of every piece of clothing i own from my photos then, told it to find new outfits for me and render them on me with gpt-image! its kinda cool to see your entire wardrobe in a collection like this