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Introducing Meta Omnilingual Automatic Speech Recognition (ASR), a suite of models providing ASR capabilities for over 1,600 languages, including 500 low-coverage languages never before served by any ASR system. While most ASR systems focus on a limited set of languages that are well-represented on the internet, this release marks... show more

AI at Meta

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545,079 次观看 • 9 个月前 •via X (Twitter)

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Introducing Voxtral WebGPU: Real-time speech transcription entirely in your browser. This demo runs Voxtral-Mini-4B, a powerful streaming ASR model from Mistral AI, locally on WebGPU. The model supports 13 languages and is capable of <500 ms latency. Fully private. Zero cost.

Introducing Voxtral WebGPU: Real-time speech transcription entirely in your browser. This demo runs Voxtral-Mini-4B, a powerful streaming ASR model from Mistral AI, locally on WebGPU. The model supports 13 languages and is capable of <500 ms latency. Fully private. Zero cost.

Xenova

94,356 次观看 • 5 个月前

ZAI is on fire. Z.ai Hot on the heels of GLM-4.6V, the team has dropped two more models on Hugging Face: - AutoGLM-Phone-9B: a 9-billion-parameter “foundation model for smartphones” that reads the screen and acts on your behalf. Small enough for on-device inference, big enough to feel like future. - GLM-ASR-Nano-2512: a pocket-sized (2B) speech-recognition powerhouse that beats Whisper v3 on English, Mandarin, and Cantonese - It even picks up your whisper. Grab them here: - AutoGLM-Phone-9B → - GLM-ASR-Nano-2512 →

ZAI is on fire. Z.ai Hot on the heels of GLM-4.6V, the team has dropped two more models on Hugging Face: - AutoGLM-Phone-9B: a 9-billion-parameter “foundation model for smartphones” that reads the screen and acts on your behalf. Small enough for on-device inference, big enough to feel like future. - GLM-ASR-Nano-2512: a pocket-sized (2B) speech-recognition powerhouse that beats Whisper v3 on English, Mandarin, and Cantonese - It even picks up your whisper. Grab them here: - AutoGLM-Phone-9B → - GLM-ASR-Nano-2512 →

Tiezhen WANG

45,514 次观看 • 8 个月前

🚨 JUST IN: MICROSOFT just open sourced a VOICE AI THAT TRANSCRIBES 60 MINUTES OF AUDIO in a single pass. 100% FREE. It knows who spoke. It knows when they spoke. It knows exactly what they said. All in one shot. No chunking. No context loss. It's called VibeVoice. Not a transcription tool. Not a basic speech to text wrapper. A frontier voice AI family with ASR, TTS, and real time streaming. All open source. All free. Here's what it actually does 👇 VibeVoice ASR - Speech Recognition: → Processes 60 minutes of continuous audio in a single pass → Never slices audio into chunks so global context is never lost → Identifies WHO spoke, WHEN they spoke and WHAT they said simultaneously → Supports customized hotwords for domain specific accuracy → Works in 50+ languages natively → Already adopted by Hugging Face Transformers library → Already being built on by the open source community BY PEOPLE WHO HAD NO IDEA THIS LEVEL OF ACCURACY WAS ALREADY FREE. VibeVoice TTS - Text to Speech: → Generates up to 90 minutes of speech in a single pass → Supports up to 4 distinct speakers in one conversation → Natural turn taking and speaker consistency throughout → Expressive speech that captures emotional nuances → Supports English, Chinese and multiple other languages VibeVoice Realtime - Streaming TTS: → Only 300 millisecond first audible latency → Streams text input in real time → 0.5B parameters so it actually deploys anywhere → Robust long form generation up to 10 minutes → Lightweight enough for production use today The core innovation nobody is talking about: Most voice AI models slice long audio into short chunks. Every time they slice, they lose context. Speaker tracking breaks. Semantic coherence breaks. Accuracy drops. VibeVoice uses continuous speech tokenizers running at an ultra low frame rate of 7.5 Hz. This preserves audio fidelity while dramatically boosting computational efficiency. The entire 60 minutes stays in context. Nothing gets lost. Nobody gets misidentified. The numbers: → VibeVoice ASR 7B - available now on Hugging Face → VibeVoice Realtime 0.5B - try it on Colab right now → 50+ supported languages → 11 distinct English voice styles → 9 multilingual speaker voices → Already integrated into Hugging Face Transformers → Finetuning code now available The wildest part? A voice powered input method called Vibing just built itself on top of VibeVoice ASR. Available on macOS and Windows right now. The open source community is already shipping products on top of this. 100% Open Source. Free to use. Free to fine tune. Free to build on. 🔖 Save this before your competitors find it first. 👇

🚨 JUST IN: MICROSOFT just open sourced a VOICE AI THAT TRANSCRIBES 60 MINUTES OF AUDIO in a single pass. 100% FREE. It knows who spoke. It knows when they spoke. It knows exactly what they said. All in one shot. No chunking. No context loss. It's called VibeVoice. Not a transcription tool. Not a basic speech to text wrapper. A frontier voice AI family with ASR, TTS, and real time streaming. All open source. All free. Here's what it actually does 👇 VibeVoice ASR - Speech Recognition: → Processes 60 minutes of continuous audio in a single pass → Never slices audio into chunks so global context is never lost → Identifies WHO spoke, WHEN they spoke and WHAT they said simultaneously → Supports customized hotwords for domain specific accuracy → Works in 50+ languages natively → Already adopted by Hugging Face Transformers library → Already being built on by the open source community BY PEOPLE WHO HAD NO IDEA THIS LEVEL OF ACCURACY WAS ALREADY FREE. VibeVoice TTS - Text to Speech: → Generates up to 90 minutes of speech in a single pass → Supports up to 4 distinct speakers in one conversation → Natural turn taking and speaker consistency throughout → Expressive speech that captures emotional nuances → Supports English, Chinese and multiple other languages VibeVoice Realtime - Streaming TTS: → Only 300 millisecond first audible latency → Streams text input in real time → 0.5B parameters so it actually deploys anywhere → Robust long form generation up to 10 minutes → Lightweight enough for production use today The core innovation nobody is talking about: Most voice AI models slice long audio into short chunks. Every time they slice, they lose context. Speaker tracking breaks. Semantic coherence breaks. Accuracy drops. VibeVoice uses continuous speech tokenizers running at an ultra low frame rate of 7.5 Hz. This preserves audio fidelity while dramatically boosting computational efficiency. The entire 60 minutes stays in context. Nothing gets lost. Nobody gets misidentified. The numbers: → VibeVoice ASR 7B - available now on Hugging Face → VibeVoice Realtime 0.5B - try it on Colab right now → 50+ supported languages → 11 distinct English voice styles → 9 multilingual speaker voices → Already integrated into Hugging Face Transformers → Finetuning code now available The wildest part? A voice powered input method called Vibing just built itself on top of VibeVoice ASR. Available on macOS and Windows right now. The open source community is already shipping products on top of this. 100% Open Source. Free to use. Free to fine tune. Free to build on. 🔖 Save this before your competitors find it first. 👇

Kanika

221,357 次观看 • 4 个月前

NVIDIA just released a new open source transcription model, Nemotron Speech ASR, designed from the ground up for low-latency use cases like voice agents. Here's a voice agent built with this new model. 24ms transcription finalization and total voice-to-voice inference time under 500ms. This agent actually uses *three* NVIDIA open source models: - Nemotron Speech ASR - Nemotron 3 Nano 30GB in a 4-bit quant (released in December) - A preview checkpoint of the upcoming Magpie text-to-speech model These models are all truly open source: weights, training data, training code, and inference code. This is a big deal! Jensen said in the CES keynote yesterday that he expects open source models to catch up to proprietary models this year in a number of categories. NVIDIA is putting their weight behind making this happen. (As Alan Kay said, the best way to predict the future is to invent it.) The code for this agent is open source too, of course. You can deploy it to production with Modal and Pipecat AI cloud, or run locally on an NVIDIA DGX Spark or RTX 5090.

NVIDIA just released a new open source transcription model, Nemotron Speech ASR, designed from the ground up for low-latency use cases like voice agents. Here's a voice agent built with this new model. 24ms transcription finalization and total voice-to-voice inference time under 500ms. This agent actually uses three NVIDIA open source models: - Nemotron Speech ASR - Nemotron 3 Nano 30GB in a 4-bit quant (released in December) - A preview checkpoint of the upcoming Magpie text-to-speech model These models are all truly open source: weights, training data, training code, and inference code. This is a big deal! Jensen said in the CES keynote yesterday that he expects open source models to catch up to proprietary models this year in a number of categories. NVIDIA is putting their weight behind making this happen. (As Alan Kay said, the best way to predict the future is to invent it.) The code for this agent is open source too, of course. You can deploy it to production with Modal and Pipecat AI cloud, or run locally on an NVIDIA DGX Spark or RTX 5090.

kwindla

274,679 次观看 • 7 个月前

LINGUISTIC IMPERIALISM Linguistic imperialism is the process whereby dominant powers impose their language on those they colonise, suppressing indigenous languages and thus marginalising their speakers and sustaining power inequalities. Indigenous languages in colonial Africa were frowned upon, while colonial languages were made mandatory. In Anglophone Africa, policies were all written in English; media and broadcasting used English; and there were many systemic policies forbidding the use of indigenous languages. For example, students in many Kenyan schools were punished for using any other languages, with most of these punishments involving shaming tactics such as the wearing of bones from dead animals as chains around the neck. This also happened to the indigenous people who were forced to speak Arabic and change their names in places like Sudan. This warped the consciousness of individuals, leading to a loss of appreciation for indigenous languages and cultures - and promoted the adoption of the coloniser’s worldview, values, systems and structures. UNESCO's 1953 report, The Use of Vernacular Languages in Education, indicated that around 40% of the global population received education in an unfamiliar language. Sub-Saharan Africa, which is home to nearly 30% of the world’s languages, still uses the colonisers’ languages as national languages. Consequently, the education and values instilled remain those of the colonisers. Many languages are at risk of extinction, with only a few speakers left. The loss of these languages is equivalent to the loss of African heritage and culture.

LINGUISTIC IMPERIALISM Linguistic imperialism is the process whereby dominant powers impose their language on those they colonise, suppressing indigenous languages and thus marginalising their speakers and sustaining power inequalities. Indigenous languages in colonial Africa were frowned upon, while colonial languages were made mandatory. In Anglophone Africa, policies were all written in English; media and broadcasting used English; and there were many systemic policies forbidding the use of indigenous languages. For example, students in many Kenyan schools were punished for using any other languages, with most of these punishments involving shaming tactics such as the wearing of bones from dead animals as chains around the neck. This also happened to the indigenous people who were forced to speak Arabic and change their names in places like Sudan. This warped the consciousness of individuals, leading to a loss of appreciation for indigenous languages and cultures - and promoted the adoption of the coloniser’s worldview, values, systems and structures. UNESCO's 1953 report, The Use of Vernacular Languages in Education, indicated that around 40% of the global population received education in an unfamiliar language. Sub-Saharan Africa, which is home to nearly 30% of the world’s languages, still uses the colonisers’ languages as national languages. Consequently, the education and values instilled remain those of the colonisers. Many languages are at risk of extinction, with only a few speakers left. The loss of these languages is equivalent to the loss of African heritage and culture.

African Stream

20,321 次观看 • 1 年前

Today we're introducing TRIBE v2 (Trimodal Brain Encoder), a foundation model trained to predict how the human brain responds to almost any sight or sound. Building on our Algonauts 2025 award-winning architecture, TRIBE v2 draws on 500+ hours of fMRI recordings from 700+ people to create a digital twin of neural activity and enable zero-shot predictions for new subjects, languages, and tasks. Try the demo and learn more here:

Today we're introducing TRIBE v2 (Trimodal Brain Encoder), a foundation model trained to predict how the human brain responds to almost any sight or sound. Building on our Algonauts 2025 award-winning architecture, TRIBE v2 draws on 500+ hours of fMRI recordings from 700+ people to create a digital twin of neural activity and enable zero-shot predictions for new subjects, languages, and tasks. Try the demo and learn more here:

AI at Meta

6,969,420 次观看 • 5 个月前

New short course on Serverless LLM apps with Amazon Bedrock, taught by Amazon Web Services' Mike G Chambers! A serverless architecture enables you to quickly deploy your applications without needing to set up and manage compute servers to run your applications on, the maintenance of which can be another full-time job. In this course, you’ll learn how to do this by using an event-driven architecture to build complex AI workflows. Mike illustrate these concepts by building a cool application that automatically detects incoming customer inquiries, transcribes them with ASR (automatic speech recognition), summarizes them with an LLM using Bedrock, and deploys serverless with AWS Lambda. I hope this course makes it much easier for you to build and deploy LLM applications requiring multi-step AI workflows. Please sign up here:

New short course on Serverless LLM apps with Amazon Bedrock, taught by Amazon Web Services' Mike G Chambers! A serverless architecture enables you to quickly deploy your applications without needing to set up and manage compute servers to run your applications on, the maintenance of which can be another full-time job. In this course, you’ll learn how to do this by using an event-driven architecture to build complex AI workflows. Mike illustrate these concepts by building a cool application that automatically detects incoming customer inquiries, transcribes them with ASR (automatic speech recognition), summarizes them with an LLM using Bedrock, and deploys serverless with AWS Lambda. I hope this course makes it much easier for you to build and deploy LLM applications requiring multi-step AI workflows. Please sign up here:

Andrew Ng

108,171 次观看 • 2 年前

The Speaker announced in Lok Sabha today that translations of proceedings is being made available in 22 languages including Sanskrit. Translations, are most welcome for languages that are official state languages. Can you tell me which state has Sanskrit as its official language? Why is taxpayers' money being wasted on a language that is not even communicable? It is not spoken in any state in India, and nobody uses it for communication! As per the 2011 Census, only 24,821 people supposedly speak Sanskrit. When this data is available, why should taxpayers' money be wasted to further the RSS ideologies?

The Speaker announced in Lok Sabha today that translations of proceedings is being made available in 22 languages including Sanskrit. Translations, are most welcome for languages that are official state languages. Can you tell me which state has Sanskrit as its official language? Why is taxpayers' money being wasted on a language that is not even communicable? It is not spoken in any state in India, and nobody uses it for communication! As per the 2011 Census, only 24,821 people supposedly speak Sanskrit. When this data is available, why should taxpayers' money be wasted to further the RSS ideologies?

Dayanidhi Maran தயாநிதி மாறன்

291,190 次观看 • 1 年前

We have officially launched Fun-ASR1.5, a major update to our end-to-end speech recognition model. This release focuses on three core pillars: broader language coverage, language switching, and production-ready text output. Key Features: • Multilingual Support: Supports high-accuracy recognition for 30 languages across Asia, Europe, and the Middle East within a single model. • Language Switching: Handles mixed-language speech (Code-Switching) natively, automatically detecting and transcribing language shifts without the need for manual tagging. • Professional Text Output: Delivers "ready-to-use" text with smart punctuation and automatic formatting for dates, numbers, and currencies. Fun-ASR1.5 bridges the gap between raw audio and professional documentation, providing a reliable engine for global communication.

We have officially launched Fun-ASR1.5, a major update to our end-to-end speech recognition model. This release focuses on three core pillars: broader language coverage, language switching, and production-ready text output. Key Features: • Multilingual Support: Supports high-accuracy recognition for 30 languages across Asia, Europe, and the Middle East within a single model. • Language Switching: Handles mixed-language speech (Code-Switching) natively, automatically detecting and transcribing language shifts without the need for manual tagging. • Professional Text Output: Delivers "ready-to-use" text with smart punctuation and automatic formatting for dates, numbers, and currencies. Fun-ASR1.5 bridges the gap between raw audio and professional documentation, providing a reliable engine for global communication.

Tongyi Lab

3,917,307 次观看 • 4 个月前

India’s Sovereign Move - Sarvam Vision Cracks the Code across 22 Indian languages Sarvam Vision is an AI model from Sarvam AI that excels in optical character recognition (OCR) for 22 Indian languages, outperforming global models like Gemini and GPT-4o on Indic benchmarks. This achievement supports India's push for sovereign AI by digitizing real-world documents and archives in local scripts. Sarvam Vision, a 3-billion-parameter model, handles "messy" scanned paperwork across languages like Hindi (95.91% accuracy), Bengali (92.61%), Tamil (93.42%), Marathi (93.13%), and even low-resource ones like Santali and Dogri (over 80%). It structures data natively without relying on English translation layers, enabling applications in cultural recovery and document intelligence. The model powers India's sovereign AI infrastructure under the IndiaAI Mission, where Sarvam was selected to build a national LLM. This launch fits Sarvam's broader stack, including prior models like Sarvam-Translate (also for 22 languages) and audio models for code-mixed speech. By focusing on Indic challenges ignored by Western AI, it unlocks centuries of knowledge in non-English archives. Recent demos, like digitizing historical texts, highlight its real-world impact beyond benchmarks. Credit : AIM Network.

India’s Sovereign Move - Sarvam Vision Cracks the Code across 22 Indian languages Sarvam Vision is an AI model from Sarvam AI that excels in optical character recognition (OCR) for 22 Indian languages, outperforming global models like Gemini and GPT-4o on Indic benchmarks. This achievement supports India's push for sovereign AI by digitizing real-world documents and archives in local scripts. Sarvam Vision, a 3-billion-parameter model, handles "messy" scanned paperwork across languages like Hindi (95.91% accuracy), Bengali (92.61%), Tamil (93.42%), Marathi (93.13%), and even low-resource ones like Santali and Dogri (over 80%). It structures data natively without relying on English translation layers, enabling applications in cultural recovery and document intelligence. The model powers India's sovereign AI infrastructure under the IndiaAI Mission, where Sarvam was selected to build a national LLM. This launch fits Sarvam's broader stack, including prior models like Sarvam-Translate (also for 22 languages) and audio models for code-mixed speech. By focusing on Indic challenges ignored by Western AI, it unlocks centuries of knowledge in non-English archives. Recent demos, like digitizing historical texts, highlight its real-world impact beyond benchmarks. Credit : AIM Network.

Augadh

20,688 次观看 • 6 个月前

Introducing Indic Parler-TTS: Open-Source Text-to-Speech for Over a Billion Indic Speakers! 🌏 In collaboration with Hugging Face, we are excited to release Indic Parler-TTS, a state-of-the-art open-source text-to-speech system designed to bring accessible and high-quality speech technology to India’s diverse linguistic community. Supporting 20 of the 22 scheduled Indian languages—and English in various accents (US, British, Indian)—it’s built to serve over a billion speakers and empower companies, developers, researchers, and communities. Why Indic Parler-TTS Stands Out: 1. Open and Accessible: Fully open-source with permissive licensing for unrestricted usage. 2. Wide Language Support: Includes a vast range of Indic languages, with rich diversity in voices. 3. High-Quality Audio: Produces natural, clear, and lifelike speech. 4. Adaptable and Fine-Tunable: Customize it to new languages, accents, or specific applications. 5. State-of-the-Art Performance: Proven through rigorous evaluation. 6. Versatile and Inclusive: Indic Parler-TTS offers 69 unique voices across 18 Indian languages, making it a perfect fit for diverse use cases like audiobooks, virtual assistants, and educational tools. Let's democratize speech technology together and make speech technology more inclusive and accessible for everyone. ▶️ Experience it now: Demo: Model page:

Introducing Indic Parler-TTS: Open-Source Text-to-Speech for Over a Billion Indic Speakers! 🌏 In collaboration with Hugging Face, we are excited to release Indic Parler-TTS, a state-of-the-art open-source text-to-speech system designed to bring accessible and high-quality speech technology to India’s diverse linguistic community. Supporting 20 of the 22 scheduled Indian languages—and English in various accents (US, British, Indian)—it’s built to serve over a billion speakers and empower companies, developers, researchers, and communities. Why Indic Parler-TTS Stands Out: 1. Open and Accessible: Fully open-source with permissive licensing for unrestricted usage. 2. Wide Language Support: Includes a vast range of Indic languages, with rich diversity in voices. 3. High-Quality Audio: Produces natural, clear, and lifelike speech. 4. Adaptable and Fine-Tunable: Customize it to new languages, accents, or specific applications. 5. State-of-the-Art Performance: Proven through rigorous evaluation. 6. Versatile and Inclusive: Indic Parler-TTS offers 69 unique voices across 18 Indian languages, making it a perfect fit for diverse use cases like audiobooks, virtual assistants, and educational tools. Let's democratize speech technology together and make speech technology more inclusive and accessible for everyone. ▶️ Experience it now: Demo: Model page:

AI4Bharat

28,681 次观看 • 1 年前

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.

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 次观看 • 3 年前

1,200+ Languages. One Vision for AI Inclusion. 🤝 How do we bridge the gap between global technology and local culture? We are thrilled to share highlights from our recent developer session, co-hosted by Tongyi Lab x YiXi, featuring insights from our partners at AI Singapore. In this video, Jian Gang Ngui from AI Singapore dives into the critical mission of building AI that truly understands the linguistic and cultural nuances of Southeast Asia—a region home to 700+ million people speaking over 1,200 languages. By leveraging Qwen, Gemma, and other state-of-the-art open-source foundation models, AISG is working hand-in-hand with native communities to integrate local languages and cultural contexts to build LLMs that are truly accessible and relevant to everyone. Proud to support AISG in this journey!

1,200+ Languages. One Vision for AI Inclusion. 🤝 How do we bridge the gap between global technology and local culture? We are thrilled to share highlights from our recent developer session, co-hosted by Tongyi Lab x YiXi, featuring insights from our partners at AI Singapore. In this video, Jian Gang Ngui from AI Singapore dives into the critical mission of building AI that truly understands the linguistic and cultural nuances of Southeast Asia—a region home to 700+ million people speaking over 1,200 languages. By leveraging Qwen, Gemma, and other state-of-the-art open-source foundation models, AISG is working hand-in-hand with native communities to integrate local languages and cultural contexts to build LLMs that are truly accessible and relevant to everyone. Proud to support AISG in this journey!

Alibaba Cloud

25,025 次观看 • 3 个月前

Hi XR devs! Big day today. v83 of the Meta XR SDK is officially out and it includes something very special to me 🦙. For the past 6 months we’ve been building AI Inference Building Blocks, which you may have seen teased at Meta Connect 2025. Our goal: keep the simplicity of Building Blocks while giving you the freedom to plug in any AI provider or model you want to power you apps with AI. More details and a full step-by-step guide are coming soon. And stay tuned as two more iterations are already locked in for early next year with new capabilities and major performance and inference improvements like you've never seen on Meta Quest. 🚀 For now, learn more on the Meta Horizon OS Developer Center:

Hi XR devs! Big day today. v83 of the Meta XR SDK is officially out and it includes something very special to me 🦙. For the past 6 months we’ve been building AI Inference Building Blocks, which you may have seen teased at Meta Connect 2025. Our goal: keep the simplicity of Building Blocks while giving you the freedom to plug in any AI provider or model you want to power you apps with AI. More details and a full step-by-step guide are coming soon. And stay tuned as two more iterations are already locked in for early next year with new capabilities and major performance and inference improvements like you've never seen on Meta Quest. 🚀 For now, learn more on the Meta Horizon OS Developer Center:

Robi ᯅ

12,259 次观看 • 8 个月前

Starting today, open source is leading the way. Introducing Llama 3.1: Our most capable models yet. Today we’re releasing a collection of new Llama 3.1 models including our long awaited 405B. These models deliver improved reasoning capabilities, a larger 128K token context window and improved support for 8 languages among other improvements. Llama 3.1 405B rivals leading closed source models on state-of-the-art capabilities across a range of tasks in general knowledge, steerability, math, tool use and multilingual translation. The models are available to download now directly from Meta or Hugging Face. With today’s release the ecosystem is also ready to go with 25+ partners rolling out our latest models — including Amazon Web Services, NVIDIA, Databricks, Groq Inc, Dell Technologies, Microsoft Azure and Google Cloud ready on day one. More details in the full announcement ➡️ Download Llama 3.1 models ➡️ With these releases we’re setting the stage for unprecedented new opportunities and we can’t wait to see the innovation our newest models will unlock across all levels of the AI community.

Starting today, open source is leading the way. Introducing Llama 3.1: Our most capable models yet. Today we’re releasing a collection of new Llama 3.1 models including our long awaited 405B. These models deliver improved reasoning capabilities, a larger 128K token context window and improved support for 8 languages among other improvements. Llama 3.1 405B rivals leading closed source models on state-of-the-art capabilities across a range of tasks in general knowledge, steerability, math, tool use and multilingual translation. The models are available to download now directly from Meta or Hugging Face. With today’s release the ecosystem is also ready to go with 25+ partners rolling out our latest models — including Amazon Web Services, NVIDIA, Databricks, Groq Inc, Dell Technologies, Microsoft Azure and Google Cloud ready on day one. More details in the full announcement ➡️ Download Llama 3.1 models ➡️ With these releases we’re setting the stage for unprecedented new opportunities and we can’t wait to see the innovation our newest models will unlock across all levels of the AI community.

AI at Meta

1,269,410 次观看 • 2 年前

Reports are getting a MAJOR update starting today (buckle up, this is a big one 🤯): 1) You can now create your own report that perfectly fits your needs by specifying the structure, style, tone, and more. 2) We've added the language picker to reports, so you can now create a report in any of our 80+ supported languages. 3) Notebook will now offer dynamic suggestions for topics or themes all based on your sources, from overviews to deep dives. For example, uploading scientific papers might suggest creating a white paper, while news articles could generate explainers. 4) You can now fully customize prompts to any of your reports 5) We're introducing a new stock report— blog posts!

Reports are getting a MAJOR update starting today (buckle up, this is a big one 🤯): 1) You can now create your own report that perfectly fits your needs by specifying the structure, style, tone, and more. 2) We've added the language picker to reports, so you can now create a report in any of our 80+ supported languages. 3) Notebook will now offer dynamic suggestions for topics or themes all based on your sources, from overviews to deep dives. For example, uploading scientific papers might suggest creating a white paper, while news articles could generate explainers. 4) You can now fully customize prompts to any of your reports 5) We're introducing a new stock report— blog posts!

Gemini Notebook

905,084 次观看 • 11 个月前

U.N. official wants to decolonize AI and train new models with third-world "systems of knowledge" Deputy Secretary-General Amina J. Mohammed of Nigeria: "Colonial conquest brought catastrophe and genocide, in many cases on a continental scale. Entire communities were destroyed. Indigenous people were driven far from their lands. The assault was also on memory itself. Languages were suppressed. Knowledge gained through millennia of civilization, including in the Mayan codices, was deliberately put to the flame. Many traditions rooted in land and community and memory were lost." "These are living traditions carrying a wisdom that can help the region shape technological change in its own image towards its own vision of a good life, to pursue development on its own terms. And that starts with ensuring the people of this region can see themselves in the technology. More than 800 indigenous peoples live here, representing 60 million people. Many of their languages and systems of knowledge are sustained through oral traditions when AI systems are trained largely on written material that has been gathered elsewhere. That knowledge can be distorted or made invisible. Right now, Chile's National Center for Artificial Intelligence and 30 partner institutions are developing a LatAm GPT, an open-source model trained on regional data. Its first version is in Spanish and Portuguese, but researchers are also developing tools for other languages -- with the consent of the indigenous communities concerned. And that is what an inclusive reset can look like. Technology built with a fuller account of the people that it is meant to serve."

U.N. official wants to decolonize AI and train new models with third-world "systems of knowledge" Deputy Secretary-General Amina J. Mohammed of Nigeria: "Colonial conquest brought catastrophe and genocide, in many cases on a continental scale. Entire communities were destroyed. Indigenous people were driven far from their lands. The assault was also on memory itself. Languages were suppressed. Knowledge gained through millennia of civilization, including in the Mayan codices, was deliberately put to the flame. Many traditions rooted in land and community and memory were lost." "These are living traditions carrying a wisdom that can help the region shape technological change in its own image towards its own vision of a good life, to pursue development on its own terms. And that starts with ensuring the people of this region can see themselves in the technology. More than 800 indigenous peoples live here, representing 60 million people. Many of their languages and systems of knowledge are sustained through oral traditions when AI systems are trained largely on written material that has been gathered elsewhere. That knowledge can be distorted or made invisible. Right now, Chile's National Center for Artificial Intelligence and 30 partner institutions are developing a LatAm GPT, an open-source model trained on regional data. Its first version is in Spanish and Portuguese, but researchers are also developing tools for other languages -- with the consent of the indigenous communities concerned. And that is what an inclusive reset can look like. Technology built with a fuller account of the people that it is meant to serve."

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45,256 次观看 • 1 个月前