Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

He says current models burn capacity memorizing things nobody needs, like SHA hashes, because the training data isn't curated. Strip that out and what he calls the cognitive core could run on a billion parameters. The mechanism is distillation. A very large model, or a huge amount of compute,...

hyperbolic.ai's profile picture

Hyperbolic AI

20,500 subscribers

90,200 Aufrufe • vor 6 Tagen •via TikTok

Wissenschaft & Technologie#andrejkarpathy#ai#llm#technology#openai
LIVE

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

Alexandr Wang splits AI into three parts. Compute, which Nvidia builds. Algorithms, which OpenAI, Google and Anthropic build. And data, which almost nobody was working on when he started Scale in 2016. He bet the company on data being the bottleneck. That bet got a price in June 2025, when Meta paid $14.3 billion for 49% of Scale and hired him to run its superintelligence lab. What happened next proves his point better than the deal does. Within weeks, OpenAI dropped Scale as a data provider. Google paused its work. xAI pulled back. The logic was simple. If data is what separates the next model from the last one, nobody wants their pipeline half-owned by a rival. Scale has won some of them back. Jason Droege, now running the company, says most returned but not all. Meta committed to at least $450 million a year for five years, close to half of Scale's revenue. He was right that data is gold. Being right is what cost Scale its biggest customers. 🔎 We fact-checked 10 expert AI predictions so you know whose calls are worth listening to. It's free in bio. Follow @hyperbolic.ai for more content on AI and technology. Media: Cisco #ai #alexandrwang #scaleai #data #technology
0:37

Alexandr Wang splits AI into three parts. Compute, which Nvidia builds. Algorithms, which OpenAI, Google and Anthropic build. And data, which almost nobody was working on when he started Scale in 2016. He bet the company on data being the bottleneck. That bet got a price in June 2025, when Meta paid $14.3 billion for 49% of Scale and hired him to run its superintelligence lab. What happened next proves his point better than the deal does. Within weeks, OpenAI dropped Scale as a data provider. Google paused its work. xAI pulled back. The logic was simple. If data is what separates the next model from the last one, nobody wants their pipeline half-owned by a rival. Scale has won some of them back. Jason Droege, now running the company, says most returned but not all. Meta committed to at least $450 million a year for five years, close to half of Scale's revenue. He was right that data is gold. Being right is what cost Scale its biggest customers. 🔎 We fact-checked 10 expert AI predictions so you know whose calls are worth listening to. It's free in bio. Follow @hyperbolic.ai for more content on AI and technology. Media: Cisco #ai #alexandrwang #scaleai #data #technology

Hyperbolic AI

224,600 Aufrufe • vor 23 Tagen

Jensen Huang's prop is the point. He's holding a machine Nvidia built with Microsoft over four years. A Blackwell GPU with 6,144 CUDA cores wired to a 20-core Grace CPU, up to one petaflop of AI compute and 128GB of unified memory, in laptops as thin as 14mm. Then the comparison. In 2016 he hand-delivered those same numbers to OpenAI, in a box that took two people to carry upstairs. The real version is better than his version. The DGX-1 he walked into OpenAI's office on August 15, 2016 ran 170 teraflops, not a petaflop. Eight P100 GPUs, 128GB of memory, $129,000 list price. The petaflop DGX-1 arrived a year later with V100 chips. So the thing in his hand isn't equal to what he carried in. It's roughly six times it. Nine years. 170 teraflops to a petaflop. A two-man lift to one hand. $129,000 to something you can order with a laptop. He undersold it. 🔎 We fact-checked 10 expert AI predictions so you know whose calls are worth listening to. It's free in bio. Follow @hyperbolic.ai for more content on AI and technology. #jensenhuang #nvidia #ai #hardware #technology
0:53

Jensen Huang's prop is the point. He's holding a machine Nvidia built with Microsoft over four years. A Blackwell GPU with 6,144 CUDA cores wired to a 20-core Grace CPU, up to one petaflop of AI compute and 128GB of unified memory, in laptops as thin as 14mm. Then the comparison. In 2016 he hand-delivered those same numbers to OpenAI, in a box that took two people to carry upstairs. The real version is better than his version. The DGX-1 he walked into OpenAI's office on August 15, 2016 ran 170 teraflops, not a petaflop. Eight P100 GPUs, 128GB of memory, $129,000 list price. The petaflop DGX-1 arrived a year later with V100 chips. So the thing in his hand isn't equal to what he carried in. It's roughly six times it. Nine years. 170 teraflops to a petaflop. A two-man lift to one hand. $129,000 to something you can order with a laptop. He undersold it. 🔎 We fact-checked 10 expert AI predictions so you know whose calls are worth listening to. It's free in bio. Follow @hyperbolic.ai for more content on AI and technology. #jensenhuang #nvidia #ai #hardware #technology

Hyperbolic AI

695,100 Aufrufe • vor 20 Tagen

Math is cooked, says Harvard physics PhD Alex Wissner-Gross. By one measure, 81% of the most important math results of the last three years landed in a single day. That day was October 6. OpenAI pushed 722 AI-written manuscripts to GitHub at once, including solutions to 90 of the top 500 open problems in math. He says math only went first because it's the easiest to verify, and that science, engineering and medicine are next. 🔎 We fact-checked 10 expert AI predictions. Tap the link in bio to see where AI is really going. Follow @hyperbolic.ai for more content on AI and technology. Media: Moonshots #math #openai #ai #science #technology
0:40

Math is cooked, says Harvard physics PhD Alex Wissner-Gross. By one measure, 81% of the most important math results of the last three years landed in a single day. That day was October 6. OpenAI pushed 722 AI-written manuscripts to GitHub at once, including solutions to 90 of the top 500 open problems in math. He says math only went first because it's the easiest to verify, and that science, engineering and medicine are next. 🔎 We fact-checked 10 expert AI predictions. Tap the link in bio to see where AI is really going. Follow @hyperbolic.ai for more content on AI and technology. Media: Moonshots #math #openai #ai #science #technology

Hyperbolic AI

1,100,000 Aufrufe • vor 3 Tagen

The easy money is in agents, says the man who ran Google for ten years and now runs a rocket company. He means building an agent that does a job, not a company that designs agents. In Y Combinator's latest batch, that's the biggest single category. 56 of 199 companies where the AI does the work end to end and the customer just approves it. He says the best agent wins and you could own the company yourself. Then he puts a clock on it, one or two years. 🔎 We fact-checked 10 expert AI predictions. Tap the link in bio to see where AI is really going. Follow @hyperbolic.ai for more content on AI and technology. Media: Forbes #ericschmidt #aiagents #ai #startups #technology
0:24

The easy money is in agents, says the man who ran Google for ten years and now runs a rocket company. He means building an agent that does a job, not a company that designs agents. In Y Combinator's latest batch, that's the biggest single category. 56 of 199 companies where the AI does the work end to end and the customer just approves it. He says the best agent wins and you could own the company yourself. Then he puts a clock on it, one or two years. 🔎 We fact-checked 10 expert AI predictions. Tap the link in bio to see where AI is really going. Follow @hyperbolic.ai for more content on AI and technology. Media: Forbes #ericschmidt #aiagents #ai #startups #technology

Hyperbolic AI

337,600 Aufrufe • vor 3 Tagen

He created Claude Code and runs it at Anthropic. He says he hasn't written a line of code by hand since November. The agent count swings. He's said it's a few hundred some mornings and thousands, or tens of thousands, on other days. Claude Code now writes all of Anthropic's code, which he ties to an 8x jump in output since the start of the year. It runs its own security review and picks what to build next by reading GitHub and X. 🔎 We fact-checked 10 expert AI predictions. Tap the link in bio to see where AI is really going. Follow @hyperbolic.ai for more content on AI and technology. Media: Sequoia Capital #claudecode #anthropic #ai #coding #technology
0:52

He created Claude Code and runs it at Anthropic. He says he hasn't written a line of code by hand since November. The agent count swings. He's said it's a few hundred some mornings and thousands, or tens of thousands, on other days. Claude Code now writes all of Anthropic's code, which he ties to an 8x jump in output since the start of the year. It runs its own security review and picks what to build next by reading GitHub and X. 🔎 We fact-checked 10 expert AI predictions. Tap the link in bio to see where AI is really going. Follow @hyperbolic.ai for more content on AI and technology. Media: Sequoia Capital #claudecode #anthropic #ai #coding #technology

Hyperbolic AI

135,200 Aufrufe • vor 5 Tagen

NVIDIA CEO Jensen Huang: Elon Musk is in a phenomenal position to lead the future of AI, autonomous vehicles, and humanoid robotics. “Collecting world data is very expensive, and Elon has a great advantage because, number one, his AI factory for his cars is fantastic, has a lot of NVIDIA gear in it, and he has a very large fleet of cars on the road that allows him to collect a lot of data. So I think he has just a phenomenal position, and he's been working on this for a long time, and so he's going to be in a great position to take advantage of it. I know Elon's attitude towards AI, and he's very optimistic about its future, and obviously he's working on some of the most important AI areas. xAI is working on foundation, cognitive intelligence AI. Tesla's working on autonomous vehicles, and Optimus is for humanoid robotics. These three areas of AI are the three most important areas.”
1:04

NVIDIA CEO Jensen Huang: Elon Musk is in a phenomenal position to lead the future of AI, autonomous vehicles, and humanoid robotics. “Collecting world data is very expensive, and Elon has a great advantage because, number one, his AI factory for his cars is fantastic, has a lot of NVIDIA gear in it, and he has a very large fleet of cars on the road that allows him to collect a lot of data. So I think he has just a phenomenal position, and he's been working on this for a long time, and so he's going to be in a great position to take advantage of it. I know Elon's attitude towards AI, and he's very optimistic about its future, and obviously he's working on some of the most important AI areas. xAI is working on foundation, cognitive intelligence AI. Tesla's working on autonomous vehicles, and Optimus is for humanoid robotics. These three areas of AI are the three most important areas.”

DogeDesigner

142,982 Aufrufe • vor 3 Monaten

Those extremes are childish, Jensen Huang told Dwarkesh Patel, who had argued that Nvidia chips in China enable a weapon of a kind. You might as well say it about electricity. Patel was pointing at Anthropic's Mythos, which surfaced thousands of zero-day flaws in major operating systems and browsers. Huang's answer was that China can brute-force it on Huawei clusters anyway. His bet is that selling now keeps the world on the American stack. Nobody knows yet which read is right. 🔎 We fact-checked 10 expert AI predictions. Tap the link in bio to see where AI is really going. Follow @hyperbolic.ai for more content on AI and technology. Media: Dwarkesh Patel #jensenhuang #nvidia #china #chips #aisafety
2:26

Those extremes are childish, Jensen Huang told Dwarkesh Patel, who had argued that Nvidia chips in China enable a weapon of a kind. You might as well say it about electricity. Patel was pointing at Anthropic's Mythos, which surfaced thousands of zero-day flaws in major operating systems and browsers. Huang's answer was that China can brute-force it on Huawei clusters anyway. His bet is that selling now keeps the world on the American stack. Nobody knows yet which read is right. 🔎 We fact-checked 10 expert AI predictions. Tap the link in bio to see where AI is really going. Follow @hyperbolic.ai for more content on AI and technology. Media: Dwarkesh Patel #jensenhuang #nvidia #china #chips #aisafety

Hyperbolic AI

367,600 Aufrufe • vor 1 Tag

The founder of Oracle got on stage and said something that made half the AI industry wince: "it no longer matters which model you use, Claude or ChatGPT. the models have become an interchangeable commodity, and all the value has shifted somewhere else." Larry Ellison knows what he's talking about. he took Oracle from $1,200 in his pocket to becoming the second richest man on the planet, and he's now building the world's largest AI cluster for OpenAI. every model trains on the same open internet data, so they quickly become identical. the edge doesn't come from the model, it comes from the closed data nobody else has but you. in 50 minutes he shows why models become interchangeable, why the only moat is private data, how Oracle built a 1.2 billion watt cluster for OpenAI, and why AI robots will operate more precisely than surgeons. while some argue over whose chatbot is smarter, others are quietly building defensive walls out of their private data. bookmark it and watch ↓
49:52

The founder of Oracle got on stage and said something that made half the AI industry wince: "it no longer matters which model you use, Claude or ChatGPT. the models have become an interchangeable commodity, and all the value has shifted somewhere else." Larry Ellison knows what he's talking about. he took Oracle from $1,200 in his pocket to becoming the second richest man on the planet, and he's now building the world's largest AI cluster for OpenAI. every model trains on the same open internet data, so they quickly become identical. the edge doesn't come from the model, it comes from the closed data nobody else has but you. in 50 minutes he shows why models become interchangeable, why the only moat is private data, how Oracle built a 1.2 billion watt cluster for OpenAI, and why AI robots will operate more precisely than surgeons. while some argue over whose chatbot is smarter, others are quietly building defensive walls out of their private data. bookmark it and watch ↓

Damir Akaza

164,950 Aufrufe • vor 5 Monaten

Nvidia $NVDA CEO Jensen Huang was asked about a recent story that said he warned that China will beat the US in the AI race “That’s not what I said. What I said was China has very good AI technology. They have many AI researchers, in fact 50% of the world’s AI researchers are in China. And they develop very good AI technology. In fact, the most popular AI models in the world today, open-source models, are from China. So, they are moving very, very fast. The United States has to continue to move incredibly fast. And otherwise, otherwise – the world is very competitive, so we have to run fast.” (H/T Dan Nystedt)
0:42

Nvidia $NVDA CEO Jensen Huang was asked about a recent story that said he warned that China will beat the US in the AI race “That’s not what I said. What I said was China has very good AI technology. They have many AI researchers, in fact 50% of the world’s AI researchers are in China. And they develop very good AI technology. In fact, the most popular AI models in the world today, open-source models, are from China. So, they are moving very, very fast. The United States has to continue to move incredibly fast. And otherwise, otherwise – the world is very competitive, so we have to run fast.” (H/T Dan Nystedt)

Evan

1,947,050 Aufrufe • vor 1 Jahr

Mamdani Says He Remains ‘Very Concerned’ About AI, Calls for More Regulations “I'm concerned by the fact that we are finding out about this technology, almost exclusively from those who are creating this technology — as opposed to from any federal government regulation of the technology.”
2:18

Mamdani Says He Remains ‘Very Concerned’ About AI, Calls for More Regulations “I'm concerned by the fact that we are finding out about this technology, almost exclusively from those who are creating this technology — as opposed to from any federal government regulation of the technology.”

Chief Nerd

27,332 Aufrufe • vor 1 Monat

In Ackman's latest interview he divides the AI market, stating that it is not a bubble at the foundational layer, but highly risky at the software/model layer. He sees "near infinite demand" for compute. He believes infrastructure providers such as Microsoft, Amazon, Meta, and even SpaceX will earn massive returns on their data center investments. He is highly skeptical of pure model layer businesses like OpenAI and Anthropic. He notes they face massive capital burn rates and an existential threat from open-source models such as Chinese model and SpaceX that are driving the cost of AI access effectively to zero. A business model reliant on charging for proprietary models will collapse if open-source alternatives are essentially free. $AMZN $META $MSFT $SPCX
3:09

In Ackman's latest interview he divides the AI market, stating that it is not a bubble at the foundational layer, but highly risky at the software/model layer. He sees "near infinite demand" for compute. He believes infrastructure providers such as Microsoft, Amazon, Meta, and even SpaceX will earn massive returns on their data center investments. He is highly skeptical of pure model layer businesses like OpenAI and Anthropic. He notes they face massive capital burn rates and an existential threat from open-source models such as Chinese model and SpaceX that are driving the cost of AI access effectively to zero. A business model reliant on charging for proprietary models will collapse if open-source alternatives are essentially free. $AMZN $META $MSFT $SPCX

CapexAndChill

138,165 Aufrufe • vor 4 Monaten

THAT MOMENT WHEN NVIDIA CEO JENSEN HUANG WAS ASKED ABOUT HIS COMMENTS ON CHINA BEATING THE U.S. IN THE AI RACE: “THAT’S NOT WHAT I SAID. WHAT I SAID WAS THAT CHINA HAS VERY GOOD AI TECHNOLOGY. THEY HAVE MANY AI RESEARCHERS. IN FACT, 50% OF THE WORLD’S AI RESEARCHERS ARE IN CHINA. THEY DEVELOP VERY GOOD AI TECHNOLOGY. THE MOST POPULAR OPEN-SOURCE AI MODELS IN THE WORLD TODAY ARE FROM CHINA. SO THEY ARE MOVING VERY, VERY FAST. THE UNITED STATES HAS TO CONTINUE TO MOVE INCREDIBLY FAST. OTHERWISE, THE WORLD IS VERY COMPETITIVE, SO WE HAVE TO RUN FAST.”
0:40

THAT MOMENT WHEN NVIDIA CEO JENSEN HUANG WAS ASKED ABOUT HIS COMMENTS ON CHINA BEATING THE U.S. IN THE AI RACE: “THAT’S NOT WHAT I SAID. WHAT I SAID WAS THAT CHINA HAS VERY GOOD AI TECHNOLOGY. THEY HAVE MANY AI RESEARCHERS. IN FACT, 50% OF THE WORLD’S AI RESEARCHERS ARE IN CHINA. THEY DEVELOP VERY GOOD AI TECHNOLOGY. THE MOST POPULAR OPEN-SOURCE AI MODELS IN THE WORLD TODAY ARE FROM CHINA. SO THEY ARE MOVING VERY, VERY FAST. THE UNITED STATES HAS TO CONTINUE TO MOVE INCREDIBLY FAST. OTHERWISE, THE WORLD IS VERY COMPETITIVE, SO WE HAVE TO RUN FAST.”

Vivek Sen

155,599 Aufrufe • vor 5 Monaten

🚨 FRANCOIS CHOLLET SAYS THE CURRENT LLM STACK IS THE WRONG PATH TO AGI Interviewer: “When will we accomplish the first definition of AGI?” Chollet confirms we are already on that exact trajectory, pointing out that: "Current technology can fully automate at human level or beyond any domain where you have verifiable rewards, right? And code... code being the first one." He then brings up the problem with LLMs, stating that: "It's possible in principle to build something that looks a lot like AGI on top of the LLM stack... I do believe, however, this would be the wrong thing to do because it would be very inefficient." Looking ahead to what the final architecture will actually require, he explains that: "AI research will have to trend towards not just efficiency, but in fact optimality over time. And for this reason, future AI in a few decades... it's not going to be this harness on top of a reasoning model on top of a base LLM."
1:41

🚨 FRANCOIS CHOLLET SAYS THE CURRENT LLM STACK IS THE WRONG PATH TO AGI Interviewer: “When will we accomplish the first definition of AGI?” Chollet confirms we are already on that exact trajectory, pointing out that: "Current technology can fully automate at human level or beyond any domain where you have verifiable rewards, right? And code... code being the first one." He then brings up the problem with LLMs, stating that: "It's possible in principle to build something that looks a lot like AGI on top of the LLM stack... I do believe, however, this would be the wrong thing to do because it would be very inefficient." Looking ahead to what the final architecture will actually require, he explains that: "AI research will have to trend towards not just efficiency, but in fact optimality over time. And for this reason, future AI in a few decades... it's not going to be this harness on top of a reasoning model on top of a base LLM."

Chris

15,578 Aufrufe • vor 7 Monaten

Andrej Karpathy just made one of the most interesting arguments about AI model design that most people are completely missing. His take is that frontier AI models are not too big because the technology is complex and too big because the training data is garbage. When you or I think of the internet, we picture Wall Street Journal articles, Wikipedia entries, serious writing. That is not what a pretraining dataset looks like. When researchers at frontier labs look at random documents from the actual training corpus, it is stock ticker symbols, broken HTML, spam, gibberish. One estimate puts Llama 3's information compression at just 0.07 bits per token meaning the model has only a hazy recollection of most of what it trained on. So we build trillion parameter models not because we need a trillion parameter brain but because we need a trillion-parameter compression engine to squeeze some intelligence out of a firehose of noise. Most of those parameters are doing memory work, not cognitive work. Karpathy's prediction is separate the two entirely. Build a cognitive core, a model that contains only the algorithms for reasoning and problem-solving, stripped of encyclopedic memorization and pair it with external memory that it can query when it needs facts. He thinks a cognitive core trained on high-quality data could hit genuine intelligence at around one billion parameters. For reference, today's flagship models run between 200 billion and 1.8 trillion parameters with most of that weight dedicated to remembering the internet's slop. The trend is already moving his direction. GPT-4o operates at roughly 200 billion parameters and outperforms the original 1.8 trillion-parameter GPT-4. Inference costs for GPT-3.5-level performance dropped 280-fold between 2022 and 2024 driven almost entirely by smaller, cleaner, better-architected models. The real bottleneck in AI right now is not compute but rather data quality.
2:00

Andrej Karpathy just made one of the most interesting arguments about AI model design that most people are completely missing. His take is that frontier AI models are not too big because the technology is complex and too big because the training data is garbage. When you or I think of the internet, we picture Wall Street Journal articles, Wikipedia entries, serious writing. That is not what a pretraining dataset looks like. When researchers at frontier labs look at random documents from the actual training corpus, it is stock ticker symbols, broken HTML, spam, gibberish. One estimate puts Llama 3's information compression at just 0.07 bits per token meaning the model has only a hazy recollection of most of what it trained on. So we build trillion parameter models not because we need a trillion parameter brain but because we need a trillion-parameter compression engine to squeeze some intelligence out of a firehose of noise. Most of those parameters are doing memory work, not cognitive work. Karpathy's prediction is separate the two entirely. Build a cognitive core, a model that contains only the algorithms for reasoning and problem-solving, stripped of encyclopedic memorization and pair it with external memory that it can query when it needs facts. He thinks a cognitive core trained on high-quality data could hit genuine intelligence at around one billion parameters. For reference, today's flagship models run between 200 billion and 1.8 trillion parameters with most of that weight dedicated to remembering the internet's slop. The trend is already moving his direction. GPT-4o operates at roughly 200 billion parameters and outperforms the original 1.8 trillion-parameter GPT-4. Inference costs for GPT-3.5-level performance dropped 280-fold between 2022 and 2024 driven almost entirely by smaller, cleaner, better-architected models. The real bottleneck in AI right now is not compute but rather data quality.

Milk Road AI

200,781 Aufrufe • vor 6 Monaten

🇺🇸 🇨🇳 Jensen Huang agreed to buy Hugging Face for $12.9 billion in September... and that one move tells you what he believed and why he had to go. Philip Pilkington says Hugging Face was America's answer to DeepSeek, open source with no one controlling the pipeline, and Huang bought it because his whole model depends on open source AI: make the best chips, let the software be free, and beat China on hardware. Closed source is the opposite model, with Anthropic and OpenAI charging $20 a month forever in what Philip calls rent extraction. That is the model the gray suits are now protecting, and the man who threatened it most just resigned. Philip Pilkington
3:13

🇺🇸 🇨🇳 Jensen Huang agreed to buy Hugging Face for $12.9 billion in September... and that one move tells you what he believed and why he had to go. Philip Pilkington says Hugging Face was America's answer to DeepSeek, open source with no one controlling the pipeline, and Huang bought it because his whole model depends on open source AI: make the best chips, let the software be free, and beat China on hardware. Closed source is the opposite model, with Anthropic and OpenAI charging $20 a month forever in what Philip calls rent extraction. That is the model the gray suits are now protecting, and the man who threatened it most just resigned. Philip Pilkington

Mario Nawfal

134,521 Aufrufe • vor 14 Stunden

Nvidia $NVDA catches and passes China's open models by the end of 2027. That's Jason Calacanis @jason on All-In, and the mechanism is pricing. Jensen sells a company a full rack of compute, soup to nuts, total sovereignty, at 80 to 90% below what that company pays for tokens from OpenAI and Anthropic. He doesn't need margin on the tokens because he already made it on the hardware. Calacanis's read: starting now Nvidia is the number one competitor to OpenAI and Anthropic, not only for tokens but for enterprise compute, and the leading open source provider in America. What the rack-versus-tokens shift does to AI margins: Source: All-In Podcast -
1:16

Nvidia $NVDA catches and passes China's open models by the end of 2027. That's Jason Calacanis @jason on All-In, and the mechanism is pricing. Jensen sells a company a full rack of compute, soup to nuts, total sovereignty, at 80 to 90% below what that company pays for tokens from OpenAI and Anthropic. He doesn't need margin on the tokens because he already made it on the hardware. Calacanis's read: starting now Nvidia is the number one competitor to OpenAI and Anthropic, not only for tokens but for enterprise compute, and the leading open source provider in America. What the rack-versus-tokens shift does to AI margins: Source: All-In Podcast -

Podcast Alpha

26,746 Aufrufe • vor 2 Monaten

Jensen Huang on "distillation" On his new interview with axios, he was asked this question "Should open source model companies be allowed to distill closed models" "Distillation—learning from AI, learning from other people, and learning from other sources of knowledge, is fundamental to intelligence. We are constantly learning from other people. I am learning from you through the questions you are asking, and you are learning from me. All day long, we are learning from one another. AI also has to learn from something. The original AI models, whether they were open or closed, were trained on previously created knowledge from the internet. Now, AI is generating more content than humans. In a few more years, the internet could be 99% AI-generated content, and that content will have been created by some form of AI. As a result, AI systems will constantly be distilling knowledge and intelligence from other AI systems. The fact that AI can learn is a good thing. We want AI systems to be intelligent because a smarter AI can also be a safer AI." ---- From "Axios" YouTube channel, (full video link in comment)
1:07

Jensen Huang on "distillation" On his new interview with axios, he was asked this question "Should open source model companies be allowed to distill closed models" "Distillation—learning from AI, learning from other people, and learning from other sources of knowledge, is fundamental to intelligence. We are constantly learning from other people. I am learning from you through the questions you are asking, and you are learning from me. All day long, we are learning from one another. AI also has to learn from something. The original AI models, whether they were open or closed, were trained on previously created knowledge from the internet. Now, AI is generating more content than humans. In a few more years, the internet could be 99% AI-generated content, and that content will have been created by some form of AI. As a result, AI systems will constantly be distilling knowledge and intelligence from other AI systems. The fact that AI can learn is a good thing. We want AI systems to be intelligent because a smarter AI can also be a safer AI." ---- From "Axios" YouTube channel, (full video link in comment)

Rohan Paul

444,801 Aufrufe • vor 3 Monaten

$WDC CEO says storage demand and requirement compound and grow whether AI runs on US frontier models or cheaper open-source ones. "Whether it's the traditional frontier models that you've seen in the U.S. that are premium-based, or some of the new open-source, more economical models, the underlying requirement is still that they're going to require a lot of data to support the training of these models." "These models are going to generate a lot more data that requires storage for us." "So we view it as very positive, because even as compute maybe drives greater efficiency in terms of compute and memory resources, the requirement for storage is just going to compound and grow." "So we view it very positively for demand going forward."
0:35

$WDC CEO says storage demand and requirement compound and grow whether AI runs on US frontier models or cheaper open-source ones. "Whether it's the traditional frontier models that you've seen in the U.S. that are premium-based, or some of the new open-source, more economical models, the underlying requirement is still that they're going to require a lot of data to support the training of these models." "These models are going to generate a lot more data that requires storage for us." "So we view it as very positive, because even as compute maybe drives greater efficiency in terms of compute and memory resources, the requirement for storage is just going to compound and grow." "So we view it very positively for demand going forward."

Fireside Alpha

14,114 Aufrufe • vor 3 Monaten

" I know Elon's attitude towards AI, and he's very optimistic about its future. And obviously he's working on some of the most important AI areas. xAI is working on foundation cognitive intelligence AI, Tesla's working on autonomous vehicles, and Optimus is for humanoid robotics. These three areas of AI are the three most important areas of AI. And so I think he's working on exactly the right things," says Nvidia CEO Jensen Huang on Elon Musk and AI Ed Ludlow Bloomberg Technology $TSLA $NVDA
4:23

" I know Elon's attitude towards AI, and he's very optimistic about its future. And obviously he's working on some of the most important AI areas. xAI is working on foundation cognitive intelligence AI, Tesla's working on autonomous vehicles, and Optimus is for humanoid robotics. These three areas of AI are the three most important areas of AI. And so I think he's working on exactly the right things," says Nvidia CEO Jensen Huang on Elon Musk and AI Ed Ludlow Bloomberg Technology $TSLA $NVDA

Herbert Ong

1,979,843 Aufrufe • vor 1 Jahr

#WATCH | Delhi: On Norway's perspective and governance approach toward AI partnerships, Deputy Chief of Mission at the Royal Norwegian Embassy in New Delhi Arvinn E. Gadgil says, "We certainly can't bring scale, because we are a small country of five and a half million people. But what we can perhaps bring is an approach. We have a governance system and model which enjoys a lot of trust. We have a regulation of our technology sectors which is relatively robust..." On India's global AI summit, he adds, "...My key takeaway is that this is difficult. And it's probably more difficult than we can fully appreciate at the moment. But there is no way of solving these problems without international cooperation. And my Prime Minister is very clear on that. He's in fact just taken an initiative together with the Finnish President on finding ways to come together and find common solutions to regulating the dangerous parts of the technology while amplifying the good parts of the technology..."
1:47

#WATCH | Delhi: On Norway's perspective and governance approach toward AI partnerships, Deputy Chief of Mission at the Royal Norwegian Embassy in New Delhi Arvinn E. Gadgil says, "We certainly can't bring scale, because we are a small country of five and a half million people. But what we can perhaps bring is an approach. We have a governance system and model which enjoys a lot of trust. We have a regulation of our technology sectors which is relatively robust..." On India's global AI summit, he adds, "...My key takeaway is that this is difficult. And it's probably more difficult than we can fully appreciate at the moment. But there is no way of solving these problems without international cooperation. And my Prime Minister is very clear on that. He's in fact just taken an initiative together with the Finnish President on finding ways to come together and find common solutions to regulating the dangerous parts of the technology while amplifying the good parts of the technology..."

ANI

16,655 Aufrufe • vor 1 Monat