Загрузка видео...

Не удалось загрузить видео

На главную

Jensen Huang open-sourced NVIDIA's flagship AI model, its weights, its data, AND how they created it. "We open sourced the models," Huang says. "We open sourced the weights." "We open sourced the data." "We open sourced how we created it." Four layers of openness in one model release. "Open...

GeniusGTX's profile picture

GeniusThinking

278,425 subscribers

14,769 просмотров • 2 месяцев назад •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

Jensen Huang says NVIDIA doesn't actually build computers — and that's why every investor can't wait for them. "We don't build computers," Huang says. "We actually don't build clouds." "As it turns out, we're a computing platform company. And nobody can buy anything from us." "We vertically design, vertically integrate to design and optimize. But then we open up the entire platform at every single layer to be integrated into other companies' products." "I can't do what I do without having convinced them first." "Most of GTC is about manifesting a future that by the time my product is ready, they're going, 'What took you so long?'" Three layers, three owners: - NVIDIA owns the design - The supply chain owns the manufacturing - The clouds own the customers Huang owns the future they're all building together. P.S. I made a playbook breaking down 100+ most powerful decision making mental models used by history's greatest thinkers. 5,000+ downloads. 113 five-star reviews. Grab a free copy here: If you're new here, follow GeniusThinking for content on the greatest minds in economics, psychology, and history. — Jensen Huang ( NVIDIA ), NVIDIA CEO, on Lex Fridman's ( Lex Fridman ) podcast
0:46

Jensen Huang says NVIDIA doesn't actually build computers — and that's why every investor can't wait for them. "We don't build computers," Huang says. "We actually don't build clouds." "As it turns out, we're a computing platform company. And nobody can buy anything from us." "We vertically design, vertically integrate to design and optimize. But then we open up the entire platform at every single layer to be integrated into other companies' products." "I can't do what I do without having convinced them first." "Most of GTC is about manifesting a future that by the time my product is ready, they're going, 'What took you so long?'" Three layers, three owners: - NVIDIA owns the design - The supply chain owns the manufacturing - The clouds own the customers Huang owns the future they're all building together. P.S. I made a playbook breaking down 100+ most powerful decision making mental models used by history's greatest thinkers. 5,000+ downloads. 113 five-star reviews. Grab a free copy here: If you're new here, follow GeniusThinking for content on the greatest minds in economics, psychology, and history. — Jensen Huang ( NVIDIA ), NVIDIA CEO, on Lex Fridman's ( Lex Fridman ) podcast

GeniusThinking

37,751 просмотров • 2 месяцев назад

Jensen Huang says the gamers calling DLSS 5 'AI slop' got it backward — the artist still owns every frame. Lex asked Huang about the gamers calling DLSS 5 "AI slop." Huang didn't dodge. "I don't love AI slop myself," Huang says. "All of the AI generated content increasingly looks similar, and they're all beautiful." "I'm empathetic towards what they're thinking." "That's just not what DLSS 5 is trying to do." Then he named what DLSS 5 actually is: "DLSS 5 is 3D conditioned, 3D guided. It's ground truth structure data guided." "The artist determined the geometry. We are completely truthful to the geometry." "It's conditioned by the textures, the artistry of the artist." "Every single frame, it enhances but it doesn't change anything." AI slop generates without permission. DLSS 5 generates with the artist's blueprint. Same technology. Opposite intent. P.S. I made a playbook breaking down 100+ most powerful decision making mental models used by history's greatest thinkers. 5,000+ downloads. 113 five-star reviews. Grab a free copy here: If you're new here, follow GeniusThinking for content on the greatest minds in economics, psychology, and history. — Jensen Huang ( NVIDIA ), NVIDIA CEO, on Lex Fridman's ( Lex Fridman ) podcast
2:12

Jensen Huang says the gamers calling DLSS 5 'AI slop' got it backward — the artist still owns every frame. Lex asked Huang about the gamers calling DLSS 5 "AI slop." Huang didn't dodge. "I don't love AI slop myself," Huang says. "All of the AI generated content increasingly looks similar, and they're all beautiful." "I'm empathetic towards what they're thinking." "That's just not what DLSS 5 is trying to do." Then he named what DLSS 5 actually is: "DLSS 5 is 3D conditioned, 3D guided. It's ground truth structure data guided." "The artist determined the geometry. We are completely truthful to the geometry." "It's conditioned by the textures, the artistry of the artist." "Every single frame, it enhances but it doesn't change anything." AI slop generates without permission. DLSS 5 generates with the artist's blueprint. Same technology. Opposite intent. P.S. I made a playbook breaking down 100+ most powerful decision making mental models used by history's greatest thinkers. 5,000+ downloads. 113 five-star reviews. Grab a free copy here: If you're new here, follow GeniusThinking for content on the greatest minds in economics, psychology, and history. — Jensen Huang ( NVIDIA ), NVIDIA CEO, on Lex Fridman's ( Lex Fridman ) podcast

GeniusThinking

13,519 просмотров • 2 месяцев назад

Open models are accelerating development and deployment across agentic AI, physical AI, and a growing range of industries. ✨ “We have billions of dollars of supercomputers in operation, so we can develop our open models,” said NVIDIA CEO Jensen Huang in a recent keynote. “That work is starting to attract attention across industries worldwide, as we build frontier AI models in many different domains.” This includes leading open models from NVIDIA, including: ⚙️ NVIDIA Nemotron for agentic AI ⚙️ NVIDIA Cosmos and GR00T for physical AI ⚙️ NVIDIA Alpamayo for autonomous vehicles ⚙️ NVIDIA Clara for biomedical AI Learn more about open source and AI ➡️
0:20

Open models are accelerating development and deployment across agentic AI, physical AI, and a growing range of industries. ✨ “We have billions of dollars of supercomputers in operation, so we can develop our open models,” said NVIDIA CEO Jensen Huang in a recent keynote. “That work is starting to attract attention across industries worldwide, as we build frontier AI models in many different domains.” This includes leading open models from NVIDIA, including: ⚙️ NVIDIA Nemotron for agentic AI ⚙️ NVIDIA Cosmos and GR00T for physical AI ⚙️ NVIDIA Alpamayo for autonomous vehicles ⚙️ NVIDIA Clara for biomedical AI Learn more about open source and AI ➡️

NVIDIA AI

19,296 просмотров • 7 месяцев назад

🚨 BREAKING: NVIDIA just removed the biggest friction point in Voice AI They open-sourced PersonaPlex 7B, a real-time conversational model. It listens and speaks simultaneously to handle natural interruptions and overlaps. 100% Open Source.
0:43

🚨 BREAKING: NVIDIA just removed the biggest friction point in Voice AI They open-sourced PersonaPlex 7B, a real-time conversational model. It listens and speaks simultaneously to handle natural interruptions and overlaps. 100% Open Source.

How To Prompt

704,175 просмотров • 4 месяцев назад

World Foundation Models (WFM) are the future of Physical AI, and we believe this future should be open-sourced. We are partnering with Valeo Group to build one of the largest open-source, multi-camera WFMs — redefining how Physical AI interacts with the real world.
0:33

World Foundation Models (WFM) are the future of Physical AI, and we believe this future should be open-sourced. We are partnering with Valeo Group to build one of the largest open-source, multi-camera WFMs — redefining how Physical AI interacts with the real world.

NATIX Network

261,597 просмотров • 7 месяцев назад

Announcing the Open Model Series: a new series on open source AI models, with merve of Hugging Face. Hugging Face is the open model hub: it hosts model weights, datasets, evals, & demo apps, and maintains the open-source libraries (like transformers) used to run and train them. I think of it like GitHub, but for AI models. You can download open models and run them on your own machines. In this series we'll cover what open source and open weight models are, how they work, how to run them yourself, and why they're important. By the way - this video is edited entirely with Claude! I can make a tutorial on how I did it. If there's a question you want answered about open models, leave it in the comments. We will read them all. Episode 1 is out soon.
0:26

Announcing the Open Model Series: a new series on open source AI models, with merve of Hugging Face. Hugging Face is the open model hub: it hosts model weights, datasets, evals, & demo apps, and maintains the open-source libraries (like transformers) used to run and train them. I think of it like GitHub, but for AI models. You can download open models and run them on your own machines. In this series we'll cover what open source and open weight models are, how they work, how to run them yourself, and why they're important. By the way - this video is edited entirely with Claude! I can make a tutorial on how I did it. If there's a question you want answered about open models, leave it in the comments. We will read them all. Episode 1 is out soon.

Harper Carroll

30,392 просмотров • 26 дней назад

Earlier this week at GTC, we announced our partnership with Nvidia. We will work with Nvidia to build strong, American open-source models that are at the frontier of scientific reasoning. These models will be essential for the US to compete with China on science in the coming decades. Jensen is committing to spend tens of billions of dollars developing open-source models, and we are excited to be a partner with them in figuring out how to benchmark, train and use those agents to accelerate scientific research. We have already open-sourced some of the work we have done with them, and are looking forward to open-sourcing more. There are few things today that are more important. See our blog post below, and watch the video to learn more, narrated by the man himself.
1:44

Earlier this week at GTC, we announced our partnership with Nvidia. We will work with Nvidia to build strong, American open-source models that are at the frontier of scientific reasoning. These models will be essential for the US to compete with China on science in the coming decades. Jensen is committing to spend tens of billions of dollars developing open-source models, and we are excited to be a partner with them in figuring out how to benchmark, train and use those agents to accelerate scientific research. We have already open-sourced some of the work we have done with them, and are looking forward to open-sourcing more. There are few things today that are more important. See our blog post below, and watch the video to learn more, narrated by the man himself.

Sam Rodriques

23,763 просмотров • 5 месяцев назад

Nvidia has just announced Alpamayo 2 Super, an open 34 billion parameter reasoning vision-language-action model designed to accelerate the development of autonomous vehicles. This new model combines the NVIDIA Cosmos 3 Super reasoning model with a 2 billion parameter diffusion-based action expert model, and is post trained with reinforcement learning. The model can return multiple outputs: future trajectory plans, reasoning traces, grounded answers to questions about the scenes, and auto label generation. The model weights are now available for anyone to download on Hugging Face, and the inference code has been posted to GitHub. Distilled models can be deployed commercially without any further permission from Nvidia, and model outputs carry no license conditions. Automakers can distill down a compact version of this model that can run on the Nvidia computer in the car. Major kudos to Nvidia and Jensen Huang for advancing the state of the industry by releasing this as an open model with permissive licensing. Jensen isn't just paying lip service to the idea of open models, Nvidia is actually contributing to the ecosystem — and it's great for their business, because it helps sell more Thor computers that go in the car. Anyone can go download the model and play with it. If you do, let me know what you think. Personally I think it's so cool that we have open weights models that are this advanced, for anyone to download.
2:30

Nvidia has just announced Alpamayo 2 Super, an open 34 billion parameter reasoning vision-language-action model designed to accelerate the development of autonomous vehicles. This new model combines the NVIDIA Cosmos 3 Super reasoning model with a 2 billion parameter diffusion-based action expert model, and is post trained with reinforcement learning. The model can return multiple outputs: future trajectory plans, reasoning traces, grounded answers to questions about the scenes, and auto label generation. The model weights are now available for anyone to download on Hugging Face, and the inference code has been posted to GitHub. Distilled models can be deployed commercially without any further permission from Nvidia, and model outputs carry no license conditions. Automakers can distill down a compact version of this model that can run on the Nvidia computer in the car. Major kudos to Nvidia and Jensen Huang for advancing the state of the industry by releasing this as an open model with permissive licensing. Jensen isn't just paying lip service to the idea of open models, Nvidia is actually contributing to the ecosystem — and it's great for their business, because it helps sell more Thor computers that go in the car. Anyone can go download the model and play with it. If you do, let me know what you think. Personally I think it's so cool that we have open weights models that are this advanced, for anyone to download.

Whole Mars Catalog

45,595 просмотров • 25 дней назад

Last week at #CES2026, our CEO Jensen Huang unveiled #NVIDIARubin, our new extreme-codesigned, six chip AI platform, alongside major advancements in open models and physical AI. On the show floor, we showcased some of the AI infrastructure and #acceleratedcomputing technologies that's powering the next era of AI. Thank you to everyone who stopped by the NVIDIA Showcase and we’ll see you next time. 👋
0:25

Last week at #CES2026, our CEO Jensen Huang unveiled #NVIDIARubin, our new extreme-codesigned, six chip AI platform, alongside major advancements in open models and physical AI. On the show floor, we showcased some of the AI infrastructure and #acceleratedcomputing technologies that's powering the next era of AI. Thank you to everyone who stopped by the NVIDIA Showcase and we’ll see you next time. 👋

NVIDIA AI Infrastructure

30,115 просмотров • 7 месяцев назад

🚨 BREAKING: On Lex Fridman's podcast Jensen Huang declared the exact moment when we achieved AGI. You can stop guessing when AGI gets here. CEO of Nvidia Jensen Huang gave Lex Fridman a four-word answer. "I think it's now," "I think we've achieved AGI. We've achieved AGI when an AI can build a billion-dollar app then immediately die." "You said a billion, and you didn't say forever." "It is not out of the question that a Claw was able to create a web service, some interesting little app that all of a sudden, a few billion people used for 50 cents, and then it went out of business again shortly after." "We saw a whole bunch of those type of companies during the internet era, and most of those websites were not anything more sophisticated than what OpenClaw could generate today." "It's happening right now," Huang says. "When you go to China you're gonna see a whole bunch of people teaching their Claws to go out and look for jobs and do work, make money." The AGI you've been waiting for is already getting hired. It's just not hiring you. If you're new here, follow @AiEvolutio for the latest on ChatGPT, Claude, and the AI tools shaping how we work and create. — Jensen Huang ( NVIDIA ), NVIDIA CEO, on Lex Fridman's ( Lex Fridman ) podcast
1:39

🚨 BREAKING: On Lex Fridman's podcast Jensen Huang declared the exact moment when we achieved AGI. You can stop guessing when AGI gets here. CEO of Nvidia Jensen Huang gave Lex Fridman a four-word answer. "I think it's now," "I think we've achieved AGI. We've achieved AGI when an AI can build a billion-dollar app then immediately die." "You said a billion, and you didn't say forever." "It is not out of the question that a Claw was able to create a web service, some interesting little app that all of a sudden, a few billion people used for 50 cents, and then it went out of business again shortly after." "We saw a whole bunch of those type of companies during the internet era, and most of those websites were not anything more sophisticated than what OpenClaw could generate today." "It's happening right now," Huang says. "When you go to China you're gonna see a whole bunch of people teaching their Claws to go out and look for jobs and do work, make money." The AGI you've been waiting for is already getting hired. It's just not hiring you. If you're new here, follow @AiEvolutio for the latest on ChatGPT, Claude, and the AI tools shaping how we work and create. — Jensen Huang ( NVIDIA ), NVIDIA CEO, on Lex Fridman's ( Lex Fridman ) podcast

AI Evolution

105,765 просмотров • 3 месяцев назад

NVIDIA just removed the biggest friction point in Voice AI 🤯 They've open-sourced PersonaPlex-7B, a full-duplex conversational model that can listen and speak at the same time. Instead of waiting for you to finish talking, it uses a dual-stream architecture to process and respond in real-time. 100% Open-Source and Free.
0:42

NVIDIA just removed the biggest friction point in Voice AI 🤯 They've open-sourced PersonaPlex-7B, a full-duplex conversational model that can listen and speak at the same time. Instead of waiting for you to finish talking, it uses a dual-stream architecture to process and respond in real-time. 100% Open-Source and Free.

Simplifying AI

278,970 просмотров • 7 месяцев назад

NVIDIA CEO Jensen Huang says one scaling law multiplies AI faster than NVIDIA can hire engineers. Most people know three AI scaling laws. Pre-training. Post-training. Test-time. Each one multiplies intelligence by throwing more compute at a different stage. Jensen Huang says there's a fourth and it's the one that will dominate... Agentic scaling law. "During test time, that agentic system goes off and does research, bangs on databases, uses tools," Huang says. "And one of the most important things it does is spawn off a whole bunch of sub-agents." That's the multiplier. One AI worker can become a team. Then a department. Then a company. "It's so much easier to scale NVIDIA by hiring more employees than it is to scale myself," Huang says. Now imagine scaling without a payroll constraint. "The agentic scaling law — it's kind of like multiplying AI," Huang says. "We could spin off agents as fast as you want to spin off agents." Each agent spins off sub-agents. Each sub-agent spins off more. The compute requirement compounds inside a single query. And every agent generates new data, new experiences, new edge cases. "Wow, this is really good. We ought to memorize this," Huang says. "That data set comes back to pre-training." The four scaling laws don't compete. They feed each other. Agentic systems produce data, which feeds pre-training, which smartens the base model, which enables better agents, which produce more data. A flywheel that compounds forever. The companies pricing in three scaling laws are mispricing the fourth. The fourth eats the other three for lunch. P.S. Pull the thread on any story like this and you'll find the hidden incentive at the other end. As Munger said: "Show me the incentive and I'll show you the outcome." So I wrote a short book on how to spot them and design your own. Comment "INCENTIVES" and I'll send you the details. If you're new here, follow GeniusThinking for content on the greatest minds in economics, psychology, and history. — Jensen Huang ( NVIDIA ), NVIDIA CEO, on Lex Fridman's ( Lex Fridman ) podcast
0:55

NVIDIA CEO Jensen Huang says one scaling law multiplies AI faster than NVIDIA can hire engineers. Most people know three AI scaling laws. Pre-training. Post-training. Test-time. Each one multiplies intelligence by throwing more compute at a different stage. Jensen Huang says there's a fourth and it's the one that will dominate... Agentic scaling law. "During test time, that agentic system goes off and does research, bangs on databases, uses tools," Huang says. "And one of the most important things it does is spawn off a whole bunch of sub-agents." That's the multiplier. One AI worker can become a team. Then a department. Then a company. "It's so much easier to scale NVIDIA by hiring more employees than it is to scale myself," Huang says. Now imagine scaling without a payroll constraint. "The agentic scaling law — it's kind of like multiplying AI," Huang says. "We could spin off agents as fast as you want to spin off agents." Each agent spins off sub-agents. Each sub-agent spins off more. The compute requirement compounds inside a single query. And every agent generates new data, new experiences, new edge cases. "Wow, this is really good. We ought to memorize this," Huang says. "That data set comes back to pre-training." The four scaling laws don't compete. They feed each other. Agentic systems produce data, which feeds pre-training, which smartens the base model, which enables better agents, which produce more data. A flywheel that compounds forever. The companies pricing in three scaling laws are mispricing the fourth. The fourth eats the other three for lunch. P.S. Pull the thread on any story like this and you'll find the hidden incentive at the other end. As Munger said: "Show me the incentive and I'll show you the outcome." So I wrote a short book on how to spot them and design your own. Comment "INCENTIVES" and I'll send you the details. If you're new here, follow GeniusThinking for content on the greatest minds in economics, psychology, and history. — Jensen Huang ( NVIDIA ), NVIDIA CEO, on Lex Fridman's ( Lex Fridman ) podcast

GeniusThinking

93,264 просмотров • 3 месяцев назад

ELON: CHINA HAS THE BEST OPEN SOURCE AI MODELS RIGHT NOW “The best open source models are generally from China, which is bizarre. I think the second best one, or maybe it’s better than second best, is Grok 2.5. The open source model is actually very good, and we will continue to open source our models.” Source: The All-In Podcast
0:47

ELON: CHINA HAS THE BEST OPEN SOURCE AI MODELS RIGHT NOW “The best open source models are generally from China, which is bizarre. I think the second best one, or maybe it’s better than second best, is Grok 2.5. The open source model is actually very good, and we will continue to open source our models.” Source: The All-In Podcast

Mario Nawfal

1,163,582 просмотров • 10 месяцев назад

NVIDIA CEO Jensen Huang explains the architecture of the AI revolution AI is fundamentally about thinking—and that requires supercomputers. We’ve created a new industry: AI infrastructure. It will be as foundational as the electricity and internet infrastructures—permanent for every society. This is a transformative time; how we deliver and produce intelligence is changing. Each layer of the AI stack is growing: 1. Energy 2. Chips 3. Infrastructure 4. AI models 5. AI applications We’re witnessing a complete revolution across the stack, with opportunities at every layer. NVIDIA intends to participate across the entire stack.
1:34

NVIDIA CEO Jensen Huang explains the architecture of the AI revolution AI is fundamentally about thinking—and that requires supercomputers. We’ve created a new industry: AI infrastructure. It will be as foundational as the electricity and internet infrastructures—permanent for every society. This is a transformative time; how we deliver and produce intelligence is changing. Each layer of the AI stack is growing: 1. Energy 2. Chips 3. Infrastructure 4. AI models 5. AI applications We’re witnessing a complete revolution across the stack, with opportunities at every layer. NVIDIA intends to participate across the entire stack.

Ask Perplexity

64,013 просмотров • 10 месяцев назад

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.
1:24

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 просмотров • 7 месяцев назад

Elon Musk reveals money itself may vanish as a concept once AI and robots satisfy every human need. He says wealth was never the point. Labor allocation was. "Money is really an information system for labor allocation." "If there's no labor to allocate, it's meaningless." He even jokes a trillion dollars on a desert island is worthless because there's nothing to trade for. Once AI and robots do the work, what does money even track anymore? P.S. I made a playbook breaking down 100+ most powerful decision making mental models used by history's greatest thinkers. 5,000+ downloads. 113 five-star reviews. Grab a free copy here: If you're new here, follow GeniusThinking for content on the greatest minds in economics, psychology, and history. — Elon Musk, founder of SpaceX, Tesla and X, on Nikhil Kamath's People by WTF.
0:30

Elon Musk reveals money itself may vanish as a concept once AI and robots satisfy every human need. He says wealth was never the point. Labor allocation was. "Money is really an information system for labor allocation." "If there's no labor to allocate, it's meaningless." He even jokes a trillion dollars on a desert island is worthless because there's nothing to trade for. Once AI and robots do the work, what does money even track anymore? P.S. I made a playbook breaking down 100+ most powerful decision making mental models used by history's greatest thinkers. 5,000+ downloads. 113 five-star reviews. Grab a free copy here: If you're new here, follow GeniusThinking for content on the greatest minds in economics, psychology, and history. — Elon Musk, founder of SpaceX, Tesla and X, on Nikhil Kamath's People by WTF.

GeniusThinking

14,867 просмотров • 1 месяц назад

How did a tiny, scrappy team build one of the most powerful AI voice models? In a deep dive with Sesame CTO Ankit Kumar and a16z's Anjney Midha, we explore how Sesame is pushing the boundaries of AI conversation, why it open-sourced its speech generation model, and the power of small teams to outdo much larger AI labs on product focus. A part of their secret: a relentless focus on real-time, natural conversations over raw intelligence, and a deep commitment to voice, personality, and user experience. By opening up its speech generation model, Sesame is paving the way for even more breakthroughs in AI-native conversation 👇
1:04

How did a tiny, scrappy team build one of the most powerful AI voice models? In a deep dive with Sesame CTO Ankit Kumar and a16z's Anjney Midha, we explore how Sesame is pushing the boundaries of AI conversation, why it open-sourced its speech generation model, and the power of small teams to outdo much larger AI labs on product focus. A part of their secret: a relentless focus on real-time, natural conversations over raw intelligence, and a deep commitment to voice, personality, and user experience. By opening up its speech generation model, Sesame is paving the way for even more breakthroughs in AI-native conversation 👇

a16z

29,930 просмотров • 1 год назад

We are excited to join Nvidia's Nemotron Coalition of leading AI labs working together to advance open frontier foundation models. To celebrate we have partnered with NVIDIA and Nebius Token Factory to provide 2 free weeks of the new Nemotron 3 Ultra model on the Nous Portal!
0:29

We are excited to join Nvidia's Nemotron Coalition of leading AI labs working together to advance open frontier foundation models. To celebrate we have partnered with NVIDIA and Nebius Token Factory to provide 2 free weeks of the new Nemotron 3 Ultra model on the Nous Portal!

Nous Research

1,500,890 просмотров • 2 месяцев назад

🔥NVIDIA CEO Jensen Huang: "The AI bubble will come SOMEDAY. It’s just not today.” “It’s very unlikely in the next five years.” “It depends on how fast we can build.” "The challenge is that the rate at which we can build the infrastructure is limited by physical things." "The industry could build faster, but we don't have enough chips, memory, and data centers."
0:58

🔥NVIDIA CEO Jensen Huang: "The AI bubble will come SOMEDAY. It’s just not today.” “It’s very unlikely in the next five years.” “It depends on how fast we can build.” "The challenge is that the rate at which we can build the infrastructure is limited by physical things." "The industry could build faster, but we don't have enough chips, memory, and data centers."

Coin Bureau

47,200 просмотров • 1 месяц назад