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JUST IN: Google releases Gemini 1.5, a powerful MoE model. It's a huge breakthrough. The model has the longest context window ever seen: 1 million tokens. It can process 1 hour of video, 11 hours of audio, 30,000 lines of code, or 700,000 words in a single prompt. When...

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Lior Alexander

115,537 subscribers

83,409 просмотров • 2 лет назад •via X (Twitter)

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Комментарии: 10

Фото профиля Lior⚡
Lior⚡2 лет назад

Technical report here:

Фото профиля Lior⚡
Lior⚡2 лет назад

Amazing work by @OriolVinyals, @JeffDean, and @GoogleAI team

Фото профиля Elad Gil
Elad Gil2 лет назад

I think has had 5MM context window for a while?

Фото профиля Lior⚡
Lior⚡2 лет назад

Might've got tricked by their copy 🤔 'We’ve been able to significantly increase the amount of information our models can process — running up to 1 million tokens consistently, achieving the longest context window of any large-scale foundation model yet."

Фото профиля Google
Google2 лет назад

The Gemini fun has just gotten started.

Фото профиля Uri Eliabayev
Uri Eliabayev2 лет назад

עוד פרטים וזה נראה שהם ב10 מיליון 🤔

Фото профиля SaaS Growth Strategies
SaaS Growth Strategies2 лет назад

Really interesting to see the innovation coming from Google. Longer context length will enable new LLM applications in document processing. Not to mention AI tutors will become better.

Фото профиля Evgeny Matohin
Evgeny Matohin2 лет назад

I wish it had a proper API!

Фото профиля RohiniAI
RohiniAI2 лет назад

Raising the level

Фото профиля ASIF AGHA
ASIF AGHA2 лет назад

three.js demo was interesting to see!

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In December, we launched Gemini 1.0 Pro. Today, we're introducing Gemini 1.5 Pro! 🚀 This next-gen model uses a Mixture-of-Experts (MoE) approach for more efficient training & higher-quality responses. Gemini 1.5 Pro, our mid-sized model, will soon come standard with a 128K-token context window, but starting today, developers + customers can sign up for the limited Private Preview to try out 1.5 Pro with a groundbreaking and experimental 1 million token context window! The 1M tokens feature unlocks huge possibilities for devs - upload hundreds of pages of text, entire code repos, and long videos and let Gemini reason across them. It's still experimental and early and we’d love your feedback - learn more here.
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In December, we launched Gemini 1.0 Pro. Today, we're introducing Gemini 1.5 Pro! 🚀 This next-gen model uses a Mixture-of-Experts (MoE) approach for more efficient training & higher-quality responses. Gemini 1.5 Pro, our mid-sized model, will soon come standard with a 128K-token context window, but starting today, developers + customers can sign up for the limited Private Preview to try out 1.5 Pro with a groundbreaking and experimental 1 million token context window! The 1M tokens feature unlocks huge possibilities for devs - upload hundreds of pages of text, entire code repos, and long videos and let Gemini reason across them. It's still experimental and early and we’d love your feedback - learn more here.

Sundar Pichai

852,640 просмотров • 2 лет назад

Google announces Gemini 1.5 blog: Gemini 1.5 Pro, mid-sized model, will soon come standard with a 128K-token context window, but starting today, developers + customers can sign up for the limited Private Preview to try out 1.5 Pro with a groundbreaking and experimental 1 million token context window
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Google announces Gemini 1.5 blog: Gemini 1.5 Pro, mid-sized model, will soon come standard with a 128K-token context window, but starting today, developers + customers can sign up for the limited Private Preview to try out 1.5 Pro with a groundbreaking and experimental 1 million token context window

AK

13,850 просмотров • 2 лет назад

🚨 Google just dropped Gemini Pro 1.5, the next version of its AI model with a 1,000,000+ token context length. The model can now understand entire books, full movies, and podcast series' all in one go. This surpasses all other competitor Chatbot context windows by a longshot. Gemini Pro 1.5 now uses a Mixture-of-Experts (MoE) approach for more efficient training and higher-quality responses. Impressive to see Google ship Pro 1.5 mid-February just after the launch of Pro 1.0 in December, and Advanced last week.
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🚨 Google just dropped Gemini Pro 1.5, the next version of its AI model with a 1,000,000+ token context length. The model can now understand entire books, full movies, and podcast series' all in one go. This surpasses all other competitor Chatbot context windows by a longshot. Gemini Pro 1.5 now uses a Mixture-of-Experts (MoE) approach for more efficient training and higher-quality responses. Impressive to see Google ship Pro 1.5 mid-February just after the launch of Pro 1.0 in December, and Advanced last week.

Rowan Cheung

792,363 просмотров • 2 лет назад

New ways for developers to build with Gemini → 😎 Gemini 1.5 Flash joins 1.5 Pro in public preview via the Gemini API in Google AI Studio 🥳 A new Context Caching feature in the Gemini API 🤗 A preview of our 2 million context window #GoogleIO
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New ways for developers to build with Gemini → 😎 Gemini 1.5 Flash joins 1.5 Pro in public preview via the Gemini API in Google AI Studio 🥳 A new Context Caching feature in the Gemini API 🤗 A preview of our 2 million context window #GoogleIO

Google for Developers

17,467 просмотров • 2 лет назад

We asked Logan Kilpatrick what stood out most from Google I/O. His answer: Gemini Omni, Google's new multimodal AI model. "The model can take in any input and produce any output. Text, audio, video, image." "You get a bunch of really interesting capability transfer when you bring it all into a single model." Right now the killer use case is video editing. "It's like having a VFX studio on demand."
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We asked Logan Kilpatrick what stood out most from Google I/O. His answer: Gemini Omni, Google's new multimodal AI model. "The model can take in any input and produce any output. Text, audio, video, image." "You get a bunch of really interesting capability transfer when you bring it all into a single model." Right now the killer use case is video editing. "It's like having a VFX studio on demand."

MTS

18,884 просмотров • 2 месяцев назад

Audio Transcription with Google Gemini 1.5 Flash In this video, Gemini-Flash was able to transcribe 13 minutes of audio in 50-60 seconds. I have tested this with multiple audio files, and the transcription accuracy is close to 99%. If the audio is clear, you get 100% correct transcription. Even if the audio is really bad with a lot of noise, you still get around 95-96% accuracy. The code for this is available on GitHub. If you're interested, you can download and run it. You just need a Google API key.
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Audio Transcription with Google Gemini 1.5 Flash In this video, Gemini-Flash was able to transcribe 13 minutes of audio in 50-60 seconds. I have tested this with multiple audio files, and the transcription accuracy is close to 99%. If the audio is clear, you get 100% correct transcription. Even if the audio is really bad with a lot of noise, you still get around 95-96% accuracy. The code for this is available on GitHub. If you're interested, you can download and run it. You just need a Google API key.

AshutoshShrivastava

122,990 просмотров • 1 год назад

Introducing Gemini 2.5 Pro Experimental! 🎉 Our newest Gemini model has stellar performance across math and science benchmarks. It’s an incredible model for coding and complex reasoning, and it’s #1 on the Arena leaderboard by a drastic 40 ELO margin. Only a handful of model releases have leaped ahead so strongly in ELO. 📈 ELO score differences map directly to win rate: e.g. a 400 ELO difference yields a ~91% win rate. Incredible that since 1.5, just a year ago, we jumped 200 ELO (300 since 1.0). Here’s a fun example where Gemini 2.5 Pro writes code to create an animated swarm of colorful boids swimming in a rotating hexagon. 💫 Try the model for free today in AI Studio. It’s also available to Gemini Advanced users in Google Gemini. Blog:
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Introducing Gemini 2.5 Pro Experimental! 🎉 Our newest Gemini model has stellar performance across math and science benchmarks. It’s an incredible model for coding and complex reasoning, and it’s #1 on the Arena leaderboard by a drastic 40 ELO margin. Only a handful of model releases have leaped ahead so strongly in ELO. 📈 ELO score differences map directly to win rate: e.g. a 400 ELO difference yields a ~91% win rate. Incredible that since 1.5, just a year ago, we jumped 200 ELO (300 since 1.0). Here’s a fun example where Gemini 2.5 Pro writes code to create an animated swarm of colorful boids swimming in a rotating hexagon. 💫 Try the model for free today in AI Studio. It’s also available to Gemini Advanced users in Google Gemini. Blog:

Oriol Vinyals

212,414 просмотров • 1 год назад

Introducing Gemini 3 ✨ It’s the best model in the world for multimodal understanding, and our most powerful agentic + vibe coding model yet. Gemini 3 can bring any idea to life, quickly grasping context and intent so you can get what you need with less prompting. Find Gemini 3 Pro rolling out today in the Google Gemini and AI Mode in Search. For developers, build with it now in Google AI Studio and Vertex AI. Excited for you to try it!
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Introducing Gemini 3 ✨ It’s the best model in the world for multimodal understanding, and our most powerful agentic + vibe coding model yet. Gemini 3 can bring any idea to life, quickly grasping context and intent so you can get what you need with less prompting. Find Gemini 3 Pro rolling out today in the Google Gemini and AI Mode in Search. For developers, build with it now in Google AI Studio and Vertex AI. Excited for you to try it!

Sundar Pichai

2,950,722 просмотров • 8 месяцев назад

This is a big day. Meta is open-sourcing AudioCraft. You can now generate incredible music and sounds with a single prompt. It includes the most performant Generative AI Model (audio) on the market, the "Llama" of Audio. The research framework contains the weights and code of these models: ▸ MusicGen: controllable text-to-music model. ▸ AudioGen: text-to-sound model. ▸ EnCodec: high fidelity neural audio codec. ▸ Multi Band Diffusion: An EnCodec compatible decoder using diffusion. This is going to tremendously speed up audio research 👏
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This is a big day. Meta is open-sourcing AudioCraft. You can now generate incredible music and sounds with a single prompt. It includes the most performant Generative AI Model (audio) on the market, the "Llama" of Audio. The research framework contains the weights and code of these models: ▸ MusicGen: controllable text-to-music model. ▸ AudioGen: text-to-sound model. ▸ EnCodec: high fidelity neural audio codec. ▸ Multi Band Diffusion: An EnCodec compatible decoder using diffusion. This is going to tremendously speed up audio research 👏

Lior Alexander

231,608 просмотров • 3 лет назад

Along with text, images, video and code, Gemini is able to process raw audio signal end-to-end. 🔊 It can listen to and understand speech, making it not only useful for transcription but a model that has a much more nuanced perception of its environment. ↓
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Along with text, images, video and code, Gemini is able to process raw audio signal end-to-end. 🔊 It can listen to and understand speech, making it not only useful for transcription but a model that has a much more nuanced perception of its environment. ↓

Google DeepMind

140,169 просмотров • 2 лет назад

Having fun playing with new native audio capabilities in Gemini 1.5 Pro! ♊ Here’s a demo using audio from the #GoogleIO keynote with examples you can try: transcription, word-level timecodes, and searching audio by drawing. (🔊Video has sound)
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Having fun playing with new native audio capabilities in Gemini 1.5 Pro! ♊ Here’s a demo using audio from the #GoogleIO keynote with examples you can try: transcription, word-level timecodes, and searching audio by drawing. (🔊Video has sound)

Alexander Chen

77,097 просмотров • 2 лет назад

That's pretty powerful You can turn any YT video into an infographic using Nano Banana Pro in Gemini. - Copy any YT link - Paste in Gemini and ask it to analyze it (Gemini can access the video just using the URL) - Then ask for an infographic Prompt: "Generate an image of an infographic explaining the concept presented in the video."
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That's pretty powerful You can turn any YT video into an infographic using Nano Banana Pro in Gemini. - Copy any YT link - Paste in Gemini and ask it to analyze it (Gemini can access the video just using the URL) - Then ask for an infographic Prompt: "Generate an image of an infographic explaining the concept presented in the video."

Paul Couvert

168,337 просмотров • 8 месяцев назад

Droid made me a little ghost. I can hotkey anywhere, it pops up, it hears my audio, turns it into text, passes it to any model to do tool calls, or manage my screen, then responds in audio. Unfortunately the audio of the TTS isn't in the video, but it's using chatterbox
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Droid made me a little ghost. I can hotkey anywhere, it pops up, it hears my audio, turns it into text, passes it to any model to do tool calls, or manage my screen, then responds in audio. Unfortunately the audio of the TTS isn't in the video, but it's using chatterbox

0xSero

10,122 просмотров • 2 месяцев назад

WHY IS NO ONE TALKING ABOUT THIS?? Gemma 3n model was one of the best surprises for me. The fact that you can run it on edge devices even with just 2GB of RAM is impressive. A few weeks back, I was on holiday and used the Gemini Live feature a lot. But I kept running into issues whenever I was in a place where the network wasn’t reliable. Gemma 3n : > Multimodal: Supports text and image inputs; video/audio in development. > Context Window: Up to 128K tokens (32K for 1B model). > Multilingual: Trained on 140+ languages. > Privacy: Offline, on-device processing for data security. > Model Sizes: E2B (5B parameters, 2GB RAM) and E4B (10–12B parameters, 3GB RAM). Video credit: Google YT
1:34

WHY IS NO ONE TALKING ABOUT THIS?? Gemma 3n model was one of the best surprises for me. The fact that you can run it on edge devices even with just 2GB of RAM is impressive. A few weeks back, I was on holiday and used the Gemini Live feature a lot. But I kept running into issues whenever I was in a place where the network wasn’t reliable. Gemma 3n : > Multimodal: Supports text and image inputs; video/audio in development. > Context Window: Up to 128K tokens (32K for 1B model). > Multilingual: Trained on 140+ languages. > Privacy: Offline, on-device processing for data security. > Model Sizes: E2B (5B parameters, 2GB RAM) and E4B (10–12B parameters, 3GB RAM). Video credit: Google YT

AshutoshShrivastava

210,490 просмотров • 1 год назад

(1/5) Gemini 3, our most intelligent model, is landing in Google Search today – starting with AI Mode. Excited that this is the first time we’re shipping a new Gemini model in Search on day one! 🚀 In Search, Gemini 3 with generative layouts will make it easy to get a rich understanding of anything on your mind. It has state-of-the-art reasoning, deep multimodal understanding and advanced agentic capabilities. That allows the model to shine when you ask it to explain advanced concepts or ideas – it reasons and can code interactive visuals in real-time. It can tackle your toughest questions like advanced science.
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(1/5) Gemini 3, our most intelligent model, is landing in Google Search today – starting with AI Mode. Excited that this is the first time we’re shipping a new Gemini model in Search on day one! 🚀 In Search, Gemini 3 with generative layouts will make it easy to get a rich understanding of anything on your mind. It has state-of-the-art reasoning, deep multimodal understanding and advanced agentic capabilities. That allows the model to shine when you ask it to explain advanced concepts or ideas – it reasons and can code interactive visuals in real-time. It can tackle your toughest questions like advanced science.

Robby Stein

94,877 просмотров • 8 месяцев назад

Better/faster/cheaper voice AI turn detection with Gemini 2.0 The code that determines when the agent should respond to the user is some of the most important code in your voice AI agent. The technical terms for this job are "turn detection" or "phrase endpointing." If the voice AI responds before the user has finished their thought, the conversation is choppy and unproductive. If the AI waits too long, the conversation is slow and frustrating. There are a number of ways to approach this. You can: 1. Use a fast "voice activity detection" model to detect pauses in speech. Respond when the user pauses. 2. Use a specialized phrase endpointing model that operates on transcribed text, pattern-matching on text semantics. 3. Train a specialized phrase endpointing model that operates directly on audio. 4. Leverage the native audio capabilities of a SOTA LLM like Gemini 2.0. We've benchmarked all four of these, and Gemini 2.0 currently beats other approaches. Using Gemini is also cheaper than transcribing the audio separately using a transcription service or model. Here's a short video showing Gemini phrase endpointing in two scenarios. First, correctly handling pauses in natural conversation. Second, requesting a phone number (which is a common activity in a use case like customer support). You can see the Completeness check lines in the terminal output, printed each time Gemini processes a chunk of audio.
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Better/faster/cheaper voice AI turn detection with Gemini 2.0 The code that determines when the agent should respond to the user is some of the most important code in your voice AI agent. The technical terms for this job are "turn detection" or "phrase endpointing." If the voice AI responds before the user has finished their thought, the conversation is choppy and unproductive. If the AI waits too long, the conversation is slow and frustrating. There are a number of ways to approach this. You can: 1. Use a fast "voice activity detection" model to detect pauses in speech. Respond when the user pauses. 2. Use a specialized phrase endpointing model that operates on transcribed text, pattern-matching on text semantics. 3. Train a specialized phrase endpointing model that operates directly on audio. 4. Leverage the native audio capabilities of a SOTA LLM like Gemini 2.0. We've benchmarked all four of these, and Gemini 2.0 currently beats other approaches. Using Gemini is also cheaper than transcribing the audio separately using a transcription service or model. Here's a short video showing Gemini phrase endpointing in two scenarios. First, correctly handling pauses in natural conversation. Second, requesting a phone number (which is a common activity in a use case like customer support). You can see the Completeness check lines in the terminal output, printed each time Gemini processes a chunk of audio.

kwindla

64,184 просмотров • 1 год назад

Really happy to see the interest around our “Hands-on with Gemini” video. In our developer blog yesterday, we broke down how Gemini was used to create it. We gave Gemini sequences of different modalities — image and text in this case — and had it respond by predicting what might come next. Devs can try similar things when access to Pro opens on 12/13 🚀. The knitting demo used Ultra⚡ All the user prompts and outputs in the video are real, shortened for brevity. The video illustrates what the multimodal user experiences built with Gemini could look like. We made it to inspire developers. When you’re building an app, you can get similar results (there’s always some variability with LLMs) by prompting Gemini with an instruction that allows the user to "configure" the behavior of the model, like inputting “you are an expert in science …” before a user can engage in the same kind of back and forth dialogue. Here’s a clip of what this looks like in AI Studio with Gemini Pro. We’ve come a long way since Flamingo 🦩 & PALI, looking forward to seeing what people build with it!
2:24

Really happy to see the interest around our “Hands-on with Gemini” video. In our developer blog yesterday, we broke down how Gemini was used to create it. We gave Gemini sequences of different modalities — image and text in this case — and had it respond by predicting what might come next. Devs can try similar things when access to Pro opens on 12/13 🚀. The knitting demo used Ultra⚡ All the user prompts and outputs in the video are real, shortened for brevity. The video illustrates what the multimodal user experiences built with Gemini could look like. We made it to inspire developers. When you’re building an app, you can get similar results (there’s always some variability with LLMs) by prompting Gemini with an instruction that allows the user to "configure" the behavior of the model, like inputting “you are an expert in science …” before a user can engage in the same kind of back and forth dialogue. Here’s a clip of what this looks like in AI Studio with Gemini Pro. We’ve come a long way since Flamingo 🦩 & PALI, looking forward to seeing what people build with it!

Oriol Vinyals

180,966 просмотров • 2 лет назад

Introducing Omma Studio, a full suite of AI tools to generate 3d, video/image, and audio with SOTA models. Everything made in Studio can integrate directly with your Omma's chat code projects to deploy/publish to the web. Content can be generated in parallel alongside your code generation in Omma, too. Studio -> Code -> Deploy
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Introducing Omma Studio, a full suite of AI tools to generate 3d, video/image, and audio with SOTA models. Everything made in Studio can integrate directly with your Omma's chat code projects to deploy/publish to the web. Content can be generated in parallel alongside your code generation in Omma, too. Studio -> Code -> Deploy

Omma AI

22,584 просмотров • 1 месяц назад

🎥 Today we’re premiering Meta Movie Gen: the most advanced media foundation models to-date. Developed by AI research teams at Meta, Movie Gen delivers state-of-the-art results across a range of capabilities. We’re excited for the potential of this line of research to usher in entirely new possibilities for casual creators and creative professionals alike. More details and examples of what Movie Gen can do ➡️ 🛠️ Movie Gen models and capabilities Movie Gen Video: 30B parameter transformer model that can generate high-quality and high-definition images and videos from a single text prompt. Movie Gen Audio: A 13B parameter transformer model that can take a video input along with optional text prompts for controllability to generate high-fidelity audio synced to the video. It can generate ambient sound, instrumental background music and foley sound — delivering state-of-the-art results in audio quality, video-to-audio alignment and text-to-audio alignment. Precise video editing: Using a generated or existing video and accompanying text instructions as an input it can perform localized edits such as adding, removing or replacing elements — or global changes like background or style changes. Personalized videos: Using an image of a person and a text prompt, the model can generate a video with state-of-the-art results on character preservation and natural movement in video. We’re continuing to work closely with creative professionals from across the field to integrate their feedback as we work towards a potential release. We look forward to sharing more on this work and the creative possibilities it will enable in the future.
0:30

🎥 Today we’re premiering Meta Movie Gen: the most advanced media foundation models to-date. Developed by AI research teams at Meta, Movie Gen delivers state-of-the-art results across a range of capabilities. We’re excited for the potential of this line of research to usher in entirely new possibilities for casual creators and creative professionals alike. More details and examples of what Movie Gen can do ➡️ 🛠️ Movie Gen models and capabilities Movie Gen Video: 30B parameter transformer model that can generate high-quality and high-definition images and videos from a single text prompt. Movie Gen Audio: A 13B parameter transformer model that can take a video input along with optional text prompts for controllability to generate high-fidelity audio synced to the video. It can generate ambient sound, instrumental background music and foley sound — delivering state-of-the-art results in audio quality, video-to-audio alignment and text-to-audio alignment. Precise video editing: Using a generated or existing video and accompanying text instructions as an input it can perform localized edits such as adding, removing or replacing elements — or global changes like background or style changes. Personalized videos: Using an image of a person and a text prompt, the model can generate a video with state-of-the-art results on character preservation and natural movement in video. We’re continuing to work closely with creative professionals from across the field to integrate their feedback as we work towards a potential release. We look forward to sharing more on this work and the creative possibilities it will enable in the future.

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2,265,027 просмотров • 1 год назад