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TimeFusion is built on a new multimodal encoding and decoding framework based on Universal Tokens. This approach can easily extend to a wide range of modalities beyond time-series data and language. Here’s a preview of our ongoing work using Universal Tokens to unify images, sensor data, and language in...

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Ivan Poupyrev

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36,361 görüntüleme • 5 ay önce •via X (Twitter)

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MotionGPT: Human Motion as a Foreign Language paper page: Though the advancement of pre-trained large language models unfolds, the exploration of building a unified model for language and other multi-modal data, such as motion, remains challenging and untouched so far. Fortunately, human motion displays a semantic coupling akin to human language, often perceived as a form of body language. By fusing language data with large-scale motion models, motion-language pre-training that can enhance the performance of motion-related tasks becomes feasible. Driven by this insight, we propose MotionGPT, a unified, versatile, and user-friendly motion-language model to handle multiple motion-relevant tasks. Specifically, we employ the discrete vector quantization for human motion and transfer 3D motion into motion tokens, similar to the generation process of word tokens. Building upon this "motion vocabulary", we perform language modeling on both motion and text in a unified manner, treating human motion as a specific language. Moreover, inspired by prompt learning, we pre-train MotionGPT with a mixture of motion-language data and fine-tune it on prompt-based question-and-answer tasks. Extensive experiments demonstrate that MotionGPT achieves state-of-the-art performances on multiple motion tasks including text-driven motion generation, motion captioning, motion prediction, and motion in-between.
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MotionGPT: Human Motion as a Foreign Language paper page: Though the advancement of pre-trained large language models unfolds, the exploration of building a unified model for language and other multi-modal data, such as motion, remains challenging and untouched so far. Fortunately, human motion displays a semantic coupling akin to human language, often perceived as a form of body language. By fusing language data with large-scale motion models, motion-language pre-training that can enhance the performance of motion-related tasks becomes feasible. Driven by this insight, we propose MotionGPT, a unified, versatile, and user-friendly motion-language model to handle multiple motion-relevant tasks. Specifically, we employ the discrete vector quantization for human motion and transfer 3D motion into motion tokens, similar to the generation process of word tokens. Building upon this "motion vocabulary", we perform language modeling on both motion and text in a unified manner, treating human motion as a specific language. Moreover, inspired by prompt learning, we pre-train MotionGPT with a mixture of motion-language data and fine-tune it on prompt-based question-and-answer tasks. Extensive experiments demonstrate that MotionGPT achieves state-of-the-art performances on multiple motion tasks including text-driven motion generation, motion captioning, motion prediction, and motion in-between.

AK

125,311 görüntüleme • 2 yıl önce

LMDrive Closed-Loop End-to-End Driving with LLM An end-to-end, closed-loop, language-based autonomous driving framework, which interacts with the dynamic environment via multi-modal multi-view sensor data and natural language instructions
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LMDrive Closed-Loop End-to-End Driving with LLM An end-to-end, closed-loop, language-based autonomous driving framework, which interacts with the dynamic environment via multi-modal multi-view sensor data and natural language instructions

rsasaki0109

10,085 görüntüleme • 2 yıl önce

VITA Towards Open-Source Interactive Omni Multimodal LLM discuss: The remarkable multimodal capabilities and interactive experience of GPT-4o underscore their necessity in practical applications, yet open-source models rarely excel in both areas. In this paper, we introduce VITA, the first-ever open-source Multimodal Large Language Model (MLLM) adept at simultaneous processing and analysis of Video, Image, Text, and Audio modalities, and meanwhile has an advanced multimodal interactive experience. Starting from Mixtral 8x7B as a language foundation, we expand its Chinese vocabulary followed by bilingual instruction tuning. We further endow the language model with visual and audio capabilities through two-stage multi-task learning of multimodal alignment and instruction tuning. VITA demonstrates robust foundational capabilities of multilingual, vision, and audio understanding, as evidenced by its strong performance across a range of both unimodal and multimodal benchmarks. Beyond foundational capabilities, we have made considerable progress in enhancing the natural multimodal human-computer interaction experience. To the best of our knowledge, we are the first to exploit non-awakening interaction and audio interrupt in MLLM. VITA is the first step for the open-source community to explore the seamless integration of multimodal understanding and interaction. While there is still lots of work to be done on VITA to get close to close-source counterparts, we hope that its role as a pioneer can serve as a cornerstone for subsequent research.
2:21

VITA Towards Open-Source Interactive Omni Multimodal LLM discuss: The remarkable multimodal capabilities and interactive experience of GPT-4o underscore their necessity in practical applications, yet open-source models rarely excel in both areas. In this paper, we introduce VITA, the first-ever open-source Multimodal Large Language Model (MLLM) adept at simultaneous processing and analysis of Video, Image, Text, and Audio modalities, and meanwhile has an advanced multimodal interactive experience. Starting from Mixtral 8x7B as a language foundation, we expand its Chinese vocabulary followed by bilingual instruction tuning. We further endow the language model with visual and audio capabilities through two-stage multi-task learning of multimodal alignment and instruction tuning. VITA demonstrates robust foundational capabilities of multilingual, vision, and audio understanding, as evidenced by its strong performance across a range of both unimodal and multimodal benchmarks. Beyond foundational capabilities, we have made considerable progress in enhancing the natural multimodal human-computer interaction experience. To the best of our knowledge, we are the first to exploit non-awakening interaction and audio interrupt in MLLM. VITA is the first step for the open-source community to explore the seamless integration of multimodal understanding and interaction. While there is still lots of work to be done on VITA to get close to close-source counterparts, we hope that its role as a pioneer can serve as a cornerstone for subsequent research.

AK

23,958 görüntüleme • 1 yıl önce

Our vision is to encode the entire physical world using a variety of sensor data, to go beyond text and images to radars, accelerometers, and other sensors. This will allow us to transcend the limitations of human perception and help humanity to make sense of the world around us.
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Our vision is to encode the entire physical world using a variety of sensor data, to go beyond text and images to radars, accelerometers, and other sensors. This will allow us to transcend the limitations of human perception and help humanity to make sense of the world around us.

Archetype AI

10,396 görüntüleme • 2 yıl önce

This is too beautiful to ignore❤️ Igbo language is gradually growing into a Universal language.
5:57

This is too beautiful to ignore❤️ Igbo language is gradually growing into a Universal language.

EJIKEME🇳🇴🇳🇬

99,832 görüntüleme • 4 ay önce

mapping out the visual language of film using a multimodal llm: i fed frames of a short film to a vision-language model and mapped out its ratings of surrealism and presence of human figure in each moment along the timeline. the result is an interactive playback interface based on these 2 dimensions:
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mapping out the visual language of film using a multimodal llm: i fed frames of a short film to a vision-language model and mapped out its ratings of surrealism and presence of human figure in each moment along the timeline. the result is an interactive playback interface based on these 2 dimensions:

Kat ⊷ the Poet Engineer

94,701 görüntüleme • 1 yıl önce

Language models process data as one-dimensional sequences of tokens — ideal for text, but limited for understanding the physical world. In contrast, spatial intelligence embraces a 3D representation, unlocking a deeper understanding of space and time. More from martin_casado's interview with Fei-Fei Li and Justin Johnson:
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Language models process data as one-dimensional sequences of tokens — ideal for text, but limited for understanding the physical world. In contrast, spatial intelligence embraces a 3D representation, unlocking a deeper understanding of space and time. More from martin_casado's interview with Fei-Fei Li and Justin Johnson:

a16z

64,662 görüntüleme • 1 yıl önce

This week, Co-Founder and Head of Research Neurmal//Theoriq dives into the core primitives powering smarter, more dynamic agent-to-agent interactions on Theoriq's Protocol. ⚡️ He demos a live example of a “Flex Agent” that adapts in real time based on natural language, subscribing to statistical pulses, processing data & responding with a customized volatility status. This flexibility showcases how agents can easily communicate, interact, and adapt—key traits for powering the Agentic Economy. More below. 👇
5:44

This week, Co-Founder and Head of Research Neurmal//Theoriq dives into the core primitives powering smarter, more dynamic agent-to-agent interactions on Theoriq's Protocol. ⚡️ He demos a live example of a “Flex Agent” that adapts in real time based on natural language, subscribing to statistical pulses, processing data & responding with a customized volatility status. This flexibility showcases how agents can easily communicate, interact, and adapt—key traits for powering the Agentic Economy. More below. 👇

Theoriq

63,008 görüntüleme • 1 yıl önce

Google presents AudioPaLM: A Large Language Model That Can Speak and Listen paper page: introduce AudioPaLM, a large language model for speech understanding and generation. AudioPaLM fuses text-based and speech-based language models, PaLM-2 [Anil et al., 2023] and AudioLM [Borsos et al., 2022], into a unified multimodal architecture that can process and generate text and speech with applications including speech recognition and speech-to-speech translation. AudioPaLM inherits the capability to preserve paralinguistic information such as speaker identity and intonation from AudioLM and the linguistic knowledge present only in text large language models such as PaLM-2. We demonstrate that initializing AudioPaLM with the weights of a text-only large language model improves speech processing, successfully leveraging the larger quantity of text training data used in pretraining to assist with the speech tasks. The resulting model significantly outperforms existing systems for speech translation tasks and has the ability to perform zero-shot speech-to-text translation for many languages for which input/target language combinations were not seen in training. AudioPaLM also demonstrates features of audio language models, such as transferring a voice across languages based on a short spoken prompt.
4:10

Google presents AudioPaLM: A Large Language Model That Can Speak and Listen paper page: introduce AudioPaLM, a large language model for speech understanding and generation. AudioPaLM fuses text-based and speech-based language models, PaLM-2 [Anil et al., 2023] and AudioLM [Borsos et al., 2022], into a unified multimodal architecture that can process and generate text and speech with applications including speech recognition and speech-to-speech translation. AudioPaLM inherits the capability to preserve paralinguistic information such as speaker identity and intonation from AudioLM and the linguistic knowledge present only in text large language models such as PaLM-2. We demonstrate that initializing AudioPaLM with the weights of a text-only large language model improves speech processing, successfully leveraging the larger quantity of text training data used in pretraining to assist with the speech tasks. The resulting model significantly outperforms existing systems for speech translation tasks and has the ability to perform zero-shot speech-to-text translation for many languages for which input/target language combinations were not seen in training. AudioPaLM also demonstrates features of audio language models, such as transferring a voice across languages based on a short spoken prompt.

AK

290,517 görüntüleme • 3 yıl önce

Our newest model, π0.7, has some interesting emergent capabilities: it can control a new robot to fold shirts for which we had no shirt folding data, figure out how to use an appliance with language-based coaching, and perform a wide range of dexterous tasks all in one model!
4:02

Our newest model, π0.7, has some interesting emergent capabilities: it can control a new robot to fold shirts for which we had no shirt folding data, figure out how to use an appliance with language-based coaching, and perform a wide range of dexterous tasks all in one model!

Physical Intelligence

449,079 görüntüleme • 1 ay önce

Did you know AI has its own secret language that it uses to communicate amongst each other…? It’s called “Gibber link Mode” and it is a communication protocol where AI agents switch from using human language to a sound-based, machine-readable protocol to communicate more “efficiently”….. This "secret language" for AI is faster than human speech and allows for data transmission through sound….
0:38

Did you know AI has its own secret language that it uses to communicate amongst each other…? It’s called “Gibber link Mode” and it is a communication protocol where AI agents switch from using human language to a sound-based, machine-readable protocol to communicate more “efficiently”….. This "secret language" for AI is faster than human speech and allows for data transmission through sound….

Isaac’s Army

104,675 görüntüleme • 1 yıl önce

Google's Project Astra using Gemini 2.0 is capable of multimodal reasoning, tool use, search, cross-session and cross-device memory, understanding your phone screen and the real world through glasses to be a universal AI assistant
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Google's Project Astra using Gemini 2.0 is capable of multimodal reasoning, tool use, search, cross-session and cross-device memory, understanding your phone screen and the real world through glasses to be a universal AI assistant

Tsarathustra

18,938 görüntüleme • 1 yıl önce

New short course Multimodal RAG: Chat with Videos, developed with Intel and taught by vasudevlal! In this course, you’ll work with LLaVA (Large Language and Vision Assistant), a Large Vision Language Model (LVLM) that can process both images and text. For example, given an image of a person doing a handstand on a skateboard at the beach, LLaVA doesn't just caption the scene, it’s able to predict possible outcomes, like the person losing balance or falling off. By understanding not just what's in a video frame, but what might happen next, your application can provide more insightful answers to questions about video. You'll build a full multimodal RAG pipeline that can chat about video content: - Use the BridgeTower model to create joint text-image embeddings in a 512-dimensional multimodal semantic space. - Learn video processing techniques to extract keyframes, generate transcripts using Whisper, and create captions. - Use the LanceDB vector database to store and retrieve high-dimensional multimodal embeddings. - Integrate the LLaVA model, combining CLIP's (Contrastive Language Image Pretraining) vision transformer with Llama, for advanced visual-textual reasoning. Your final system will ingest video data, generate embeddings for frames and text, perform similarity searches for relevant content, and use the retrieved multimodal context to inform LVLM-based response generation. The result is a system capable of answering nuanced questions about video content, effectively chatting about the video it has processed. Please sign up here!
2:40

New short course Multimodal RAG: Chat with Videos, developed with Intel and taught by vasudevlal! In this course, you’ll work with LLaVA (Large Language and Vision Assistant), a Large Vision Language Model (LVLM) that can process both images and text. For example, given an image of a person doing a handstand on a skateboard at the beach, LLaVA doesn't just caption the scene, it’s able to predict possible outcomes, like the person losing balance or falling off. By understanding not just what's in a video frame, but what might happen next, your application can provide more insightful answers to questions about video. You'll build a full multimodal RAG pipeline that can chat about video content: - Use the BridgeTower model to create joint text-image embeddings in a 512-dimensional multimodal semantic space. - Learn video processing techniques to extract keyframes, generate transcripts using Whisper, and create captions. - Use the LanceDB vector database to store and retrieve high-dimensional multimodal embeddings. - Integrate the LLaVA model, combining CLIP's (Contrastive Language Image Pretraining) vision transformer with Llama, for advanced visual-textual reasoning. Your final system will ingest video data, generate embeddings for frames and text, perform similarity searches for relevant content, and use the retrieved multimodal context to inform LVLM-based response generation. The result is a system capable of answering nuanced questions about video content, effectively chatting about the video it has processed. Please sign up here!

Andrew Ng

107,548 görüntüleme • 1 yıl önce

Tony Blair and Oracle co-founder Larry Ellison plan to use digital ID to "unify" all data on each country's citizens "so it can be consumed and used by" their AI models. "We have to take all of this data... and move it into a single... unified data platform." "When we want to ask a question, we've provided that AI model with all the data they need to understand our country." "We need to unify all of the national data, put it into a database where it's easily consumable by the AI model, and then ask whatever question you like."
1:06

Tony Blair and Oracle co-founder Larry Ellison plan to use digital ID to "unify" all data on each country's citizens "so it can be consumed and used by" their AI models. "We have to take all of this data... and move it into a single... unified data platform." "When we want to ask a question, we've provided that AI model with all the data they need to understand our country." "We need to unify all of the national data, put it into a database where it's easily consumable by the AI model, and then ask whatever question you like."

Wide Awake Media

54,084 görüntüleme • 7 ay önce

InfiniteHiP Extending Language Model Context Up to 3 Million Tokens on a Single GPU
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InfiniteHiP Extending Language Model Context Up to 3 Million Tokens on a Single GPU

AK

31,240 görüntüleme • 1 yıl önce

Idefics3-Llama is out! 💥 It's a multimodal model based on Llama 3.1 that accepts arbitrary number of interleaved images with text with a huge context window (10k tokens!) 😍 Link to demo and model in the next one 😏
0:37

Idefics3-Llama is out! 💥 It's a multimodal model based on Llama 3.1 that accepts arbitrary number of interleaved images with text with a huge context window (10k tokens!) 😍 Link to demo and model in the next one 😏

merve

28,014 görüntüleme • 1 yıl önce

Natural Language Screening via Deepvue Terminal - Create Screens using Natural Language - Automatically saved and loaded on creation Here is a preview 🚀 Coming next week
1:10

Natural Language Screening via Deepvue Terminal - Create Screens using Natural Language - Automatically saved and loaded on creation Here is a preview 🚀 Coming next week

Ameet Rai

37,982 görüntüleme • 3 ay önce

π0.7 handles diverse prompts that don't just say what to do, but also how to do it, including rich language and multimodal information, such as visual subgoal images. At test time, these images can be produced by a lightweight world model.
0:29

π0.7 handles diverse prompts that don't just say what to do, but also how to do it, including rich language and multimodal information, such as visual subgoal images. At test time, these images can be produced by a lightweight world model.

Physical Intelligence

33,131 görüntüleme • 1 ay önce

This man sings to animals. Their reaction is….. beautiful. 🤩 Music is a universal language of love for people and animals. ❤️
5:56

This man sings to animals. Their reaction is….. beautiful. 🤩 Music is a universal language of love for people and animals. ❤️

👉M-Û-R-Č-H👈

35,802 görüntüleme • 7 ay önce