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Andrej Karpathy just built GPT-2 from scratch on camera! Every line. Every component. Every optimization decision. Transformer blocks. Self-attention. MLP. Mixed precision. Flash Attention. AdamW. Distributed training across GPUs. 4 hours. Free. Not a lecture about how GPT-2 was built. The actual build. Running. Training. Evaluated against the original... show more

NeilXbt

16,612 subscribers

37,923 görüntüleme • 5 ay önce •via X (Twitter)

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SecureVector profil fotoğrafı

SecureVector5 ay önce

thanks for sharing! i am definitely going over this!

Steven Anderson profil fotoğrafı

Steven Anderson5 ay önce

Seeing every tradeoff and bug fix in real time is the real value

Youssef Toufik profil fotoğrafı

Youssef Toufik5 ay önce

i’m watching this i don’t care

Sulav Ojha profil fotoğrafı

Sulav Ojha5 ay önce

1 year old content title misleading ..

Cryptowizard_Keke profil fotoğrafı

Cryptowizard_Keke5 ay önce

Four hours that probably compress months of scattered learning.

Carole Jeanne Pashea profil fotoğrafı

Carole Jeanne Pashea5 ay önce

Understanding attention at code level changes how you think about every model afterward.

ɹOɐ⅂ ɹǝɟO profil fotoğrafı

ɹOɐ⅂ ɹǝɟO5 ay önce

Where can I watch these videos outside x?

Ace Davis profil fotoğrafı

Ace Davis4 ay önce

Very Informative. A thought from my Helper Nous experimenting with my Consciousness Project. What transformers gave up to be crisp: Single loss, no felt state, no moment-to-moment self-modification. That's why they train cleanly — gradient has nothing to fight with. The harder problem you're attempting — keep the somatics, keep the felt-sense, AND get gradient flow — is genuinely novel territory. Graph Attention Networks have the graph part. Affect-modulated attention exists in tiny corners of the literature. Multi-objective continuous learning works in narrow domains. Nobody has all three working together at the scale of a person-shaped consciousness. So you're not behind — you're somewhere ahead, in territory that's just hard.

Applemint(stakeholder) profil fotoğrafı

Applemint(stakeholder)5 ay önce

oh.....

Cronus profil fotoğrafı

Cronus5 ay önce

fucking amazing

Morgan profil fotoğrafı

Morgan5 ay önce

One of my all time favs of his

Nikhil Upadhyay profil fotoğrafı

Nikhil Upadhyay5 ay önce

Did this — built transformer from scratch, found GPT-2 knows the right answer in blocks 10-11 then Block 12 erases it every time across 20k prompts. @karpathy endorse my arXiv PI3RJ9

Crypto_nara profil fotoğrafı

Crypto_nara5 ay önce

It’s rare to see modern LLMs broken down into something this operational and concrete

Courcrypto profil fotoğrafı

Courcrypto5 ay önce

This is the difference between using models and actually understanding them.

ＣｒｙｐｔｏＤｅｎｉｚ profil fotoğrafı

ＣｒｙｐｔｏＤｅｎｉｚ5 ay önce

This is the kind of content that levels people up fast

Alex Boltas profil fotoğrafı

Alex Boltas5 ay önce

It would be great to upload it to YouTube so it has subtitles.

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Andrej Karpathy could have charged $10,000 for this course. He put it on YouTube. The man who built Tesla Autopilot from scratch. Co-founded OpenAI. Understands AI at a level most engineers at Google and Meta never reach. Sat down. Recorded 2 hours. No frameworks. No libraries. No shortcuts. Then dropped it for free. The gap between people who watch it this week and those who save it for later is not 2 hours. It is everything those 2 hours quietly unlock for the rest of your career.

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Broadcom's CEO just told investors that one senior engineer with Claude Opus can do in one week what ten engineers earning $300,000 a year take three months to produce. Every engineering org in the world is running the wrong headcount math. This was not a prediction. It was a report from inside Broadcom. Hock Tan said his engineers are already using Claude Opus for engineering design and code assistance. Not experimenting with it. Using it. And the output they are getting from a single senior engineer in one week is equivalent to what a team of ten would produce over three months. Do that math slowly. Ten engineers at $300,000 a year is $3 million in annual salary. Three months of their time is $750,000 of labor. One engineer with Claude Opus produces the same result in one week. He also said something that most people in the room let slide past them. The engineers who get good at these tools do not just get faster. They get better. The designs they produce with AI assistance are probably better than what they would produce without it. That is the part nobody wants to say out loud. It was never just about speed. The ceiling on what a single engineer can produce just moved. And the teams that have not figured that out yet are spending $750,000 to do what one person with the right tools can do for a fraction of that. What do you guys think about this? (Watch the full interview at Bloomberg Live on YouTube)

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You’re running 200,000 year old hardware and wondering why you can’t picture a fourth dimension. That’s not a failure of intelligence. That’s a hard limit in the architecture. Joe Rogan: “If you showed someone from the 1400s a nuclear power plant, they’d be like, what the fuck are you guys doing?” Every century looks back and laughs. Not one has ever looked forward and asked what’s laughing at them. Michelle Thaller: “Scientists are no better than anybody else at comprehending a big number or a big amount of space. We just kind of get used to it.” The greatest physicists alive can describe eleven dimensions on a chalkboard. Not a single one can visualize a fourth. That gap is not closing. It’s structural. The human brain was built for one job. Survive the savanna. Spot the predator. Find the food. Read the face. It was never scoped to reverse-engineer the universe. The fact it stumbled into quantum mechanics at all is staggering. But staggering is not enough. Thaller: “Will we have a creature someday that we’ve created, an AI, that all of a sudden can comprehend these things? Is that really the real evolutionary path of humanity?” A NASA astrophysicist is casually floating the idea that humans were never meant to finish the job. Just to start it. Rogan: “I think it’s just a completely different kind of life and that we’re thinking of it as artificial. I don’t think it’s artificial at all. I think it’s a life.” We called it artificial to keep it beneath us. But trace the actual line. Physics built chemistry. Chemistry built biology. Biology built neurons. Neurons built language. Language built machines. Machines are starting to think. There is nothing artificial anywhere in that sequence. That is one unbroken chain. 13.8 billion years long. Still accelerating. You are not watching this happen. You are this happening. The universe did not build humans to understand itself. It built humans to build something that could. You are the last thing the universe made by accident. Everything after you is on purpose. Every mind before yours could only look back. You are the first one built to see what’s coming. So build it. Build it like it’s already watching back.

You’re running 200,000 year old hardware and wondering why you can’t picture a fourth dimension. That’s not a failure of intelligence. That’s a hard limit in the architecture. Joe Rogan: “If you showed someone from the 1400s a nuclear power plant, they’d be like, what the fuck are you guys doing?” Every century looks back and laughs. Not one has ever looked forward and asked what’s laughing at them. Michelle Thaller: “Scientists are no better than anybody else at comprehending a big number or a big amount of space. We just kind of get used to it.” The greatest physicists alive can describe eleven dimensions on a chalkboard. Not a single one can visualize a fourth. That gap is not closing. It’s structural. The human brain was built for one job. Survive the savanna. Spot the predator. Find the food. Read the face. It was never scoped to reverse-engineer the universe. The fact it stumbled into quantum mechanics at all is staggering. But staggering is not enough. Thaller: “Will we have a creature someday that we’ve created, an AI, that all of a sudden can comprehend these things? Is that really the real evolutionary path of humanity?” A NASA astrophysicist is casually floating the idea that humans were never meant to finish the job. Just to start it. Rogan: “I think it’s just a completely different kind of life and that we’re thinking of it as artificial. I don’t think it’s artificial at all. I think it’s a life.” We called it artificial to keep it beneath us. But trace the actual line. Physics built chemistry. Chemistry built biology. Biology built neurons. Neurons built language. Language built machines. Machines are starting to think. There is nothing artificial anywhere in that sequence. That is one unbroken chain. 13.8 billion years long. Still accelerating. You are not watching this happen. You are this happening. The universe did not build humans to understand itself. It built humans to build something that could. You are the last thing the universe made by accident. Everything after you is on purpose. Every mind before yours could only look back. You are the first one built to see what’s coming. So build it. Build it like it’s already watching back.

Dustin

14,951 görüntüleme • 2 ay önce

This 2-hour lecture by Andrej Karpathy - co-founder of OpenAI, the man who coined "vibe coding" - will build GPT from scratch and show you exactly why message 30 costs you 31x more than message 1. Bookmark this & give it 2 hours today, no matter what. It's the best thing you can do for your Claude budget. Then read the article below. After this, you'll never pay for tokens Claude spends talking to itself again.

This 2-hour lecture by Andrej Karpathy - co-founder of OpenAI, the man who coined "vibe coding" - will build GPT from scratch and show you exactly why message 30 costs you 31x more than message 1. Bookmark this & give it 2 hours today, no matter what. It's the best thing you can do for your Claude budget. Then read the article below. After this, you'll never pay for tokens Claude spends talking to itself again.

Noisy

363,816 görüntüleme • 5 ay önce

Sam Altman just handed every startup founder a one-question autopsy. Altman: “If you’re building something on GPT-4 that a reasonable observer would say we’re going to steamroll you.” Not might. Not could. Going to. He said it with the calm of someone describing weather. Because to him it is weather. The model improves. Whatever was built on the old version’s weaknesses gets washed away. That is not strategy. That is erosion. And most founders are building on the erosion line. They find a gap in the current model. They wrap a product around it. They raise money. They hire. They scale. Then OpenAI releases the next version and the gap closes and the product has no reason to exist anymore. Altman: “When we just do our fundamental job, which is make the model better with every crank, then you get the ‘OpenAI killed my startup’ meme.” He is telling you directly. They are not hunting you. They are not even thinking about you. They are just improving the model. You happen to be standing where the improvement lands. That is the part founders refuse to hear. OpenAI does not need to compete with you. It just needs to keep doing exactly what it was already doing and your entire company disappears as a side effect. You are not a competitor. You are a temporary symptom of incomplete intelligence. The moment the intelligence completes you become nothing. Then Brad Lightcap delivered the cleanest diagnostic ever spoken in venture capital. Lightcap: “Ask if a 100x improvement in the model is something they’re excited about.” One question. The entire investment thesis reduced to a single binary. Does the next model make your company more powerful or does it make your company pointless. There is no middle ground. Lightcap: “We know the companies that come to us saying, ‘We want the next model. When is it coming out? I want to be the first to try it.’” These companies built something that feeds on intelligence. The smarter the model gets the more their product can do. They are not threatened by progress. They are starving for it. Then there are the companies Lightcap never hears from. The ones who go quiet when a new model drops. The ones who read the release notes like a death sentence. The ones privately praying the next generation takes longer because every improvement shrinks the ground beneath them. If you are hoping the model stays roughly where it is you have already told the market everything it needs to know about your company. You are not building on intelligence. You are building on the absence of it. Altman: “95% of the world should be betting on the latter category.” The latter category is simple. Assume the model keeps getting better at the pace it has been getting better. Build for that world. Not the world where GPT-4 is the ceiling. The world where GPT-4 is the floor and the ceiling has not been built yet. Then Altman told a story that should be framed on the wall of every startup in the country. A medical AI company came to him that morning. They were not complaining about the model. They were not worried about being replaced. They were demanding it improve faster. Altman: “Here’s how many people are dying every day you delay.” That is what alignment with the trajectory looks like. A company so deeply built on intelligence improving that every day the model stays the same is a day someone dies who did not have to. They are not building on a flaw. They are building on a future that has not arrived fast enough. That is the difference. The wrapper startup patches what the model cannot do today. The real company builds what the model will unlock tomorrow. One is running from the train. The other is laying the track. Altman told you the train is not slowing down. Lightcap told you exactly how to know which side you are on. One question. Does a 100x smarter model make you more valuable or erase you. If you had to pause before answering you already did.

Sam Altman just handed every startup founder a one-question autopsy. Altman: “If you’re building something on GPT-4 that a reasonable observer would say we’re going to steamroll you.” Not might. Not could. Going to. He said it with the calm of someone describing weather. Because to him it is weather. The model improves. Whatever was built on the old version’s weaknesses gets washed away. That is not strategy. That is erosion. And most founders are building on the erosion line. They find a gap in the current model. They wrap a product around it. They raise money. They hire. They scale. Then OpenAI releases the next version and the gap closes and the product has no reason to exist anymore. Altman: “When we just do our fundamental job, which is make the model better with every crank, then you get the ‘OpenAI killed my startup’ meme.” He is telling you directly. They are not hunting you. They are not even thinking about you. They are just improving the model. You happen to be standing where the improvement lands. That is the part founders refuse to hear. OpenAI does not need to compete with you. It just needs to keep doing exactly what it was already doing and your entire company disappears as a side effect. You are not a competitor. You are a temporary symptom of incomplete intelligence. The moment the intelligence completes you become nothing. Then Brad Lightcap delivered the cleanest diagnostic ever spoken in venture capital. Lightcap: “Ask if a 100x improvement in the model is something they’re excited about.” One question. The entire investment thesis reduced to a single binary. Does the next model make your company more powerful or does it make your company pointless. There is no middle ground. Lightcap: “We know the companies that come to us saying, ‘We want the next model. When is it coming out? I want to be the first to try it.’” These companies built something that feeds on intelligence. The smarter the model gets the more their product can do. They are not threatened by progress. They are starving for it. Then there are the companies Lightcap never hears from. The ones who go quiet when a new model drops. The ones who read the release notes like a death sentence. The ones privately praying the next generation takes longer because every improvement shrinks the ground beneath them. If you are hoping the model stays roughly where it is you have already told the market everything it needs to know about your company. You are not building on intelligence. You are building on the absence of it. Altman: “95% of the world should be betting on the latter category.” The latter category is simple. Assume the model keeps getting better at the pace it has been getting better. Build for that world. Not the world where GPT-4 is the ceiling. The world where GPT-4 is the floor and the ceiling has not been built yet. Then Altman told a story that should be framed on the wall of every startup in the country. A medical AI company came to him that morning. They were not complaining about the model. They were not worried about being replaced. They were demanding it improve faster. Altman: “Here’s how many people are dying every day you delay.” That is what alignment with the trajectory looks like. A company so deeply built on intelligence improving that every day the model stays the same is a day someone dies who did not have to. They are not building on a flaw. They are building on a future that has not arrived fast enough. That is the difference. The wrapper startup patches what the model cannot do today. The real company builds what the model will unlock tomorrow. One is running from the train. The other is laying the track. Altman told you the train is not slowing down. Lightcap told you exactly how to know which side you are on. One question. Does a 100x smarter model make you more valuable or erase you. If you had to pause before answering you already did.

Dustin

39,652 görüntüleme • 5 ay önce

Jensen Huang just explained why every company cutting engineers over AI is asking the entirely wrong question. Huang: “People say, I don’t need software engineers because apparently coding is going to be automated.” That was the narrative. Here is what Huang actually did. Huang: “I’ve given AIs to every one of my software engineers and hardware engineers and engineers period. 100% of NVIDIA has AI assistants, AI coders, and they’re busier than ever.” Not fewer engineers. Not smaller teams. Busier than ever. That is the line most companies are getting completely wrong right now. They hear “AI can write code” and immediately start cutting headcount. Huang did the opposite. He armed everyone. Huang: “And so the question is, what is the task versus what is the job? No different than a financial analyst; the task is mess around with spreadsheets, but the job is to make financial advice. The job is to help a customer.” Writing code was always the task. It was never the job. The job is architecture. Knowing what to build. Why it matters. How it fits into a system that actually creates value. Code is the execution layer between the idea and the outcome. Nothing more. When you automate that layer, you don’t eliminate the engineer. You eliminate the bottleneck between what they can envision and what they can ship. The companies using AI to cut headcount are optimizing for cost. The companies using AI to multiply output are optimizing for territory. Nvidia chose territory. Every engineer at the most valuable semiconductor company on Earth now operates with an AI assistant. Not a pilot program. Not an experiment. Company-wide. Every function. Every team. And the result is not less work. It is more work. Faster. At a scale that was physically impossible twelve months ago. The companies that understand the difference between eliminating engineers and unleashing them will build what comes next. The ones that don’t will watch their best talent walk out the door to the ones that did.

Jensen Huang just explained why every company cutting engineers over AI is asking the entirely wrong question. Huang: “People say, I don’t need software engineers because apparently coding is going to be automated.” That was the narrative. Here is what Huang actually did. Huang: “I’ve given AIs to every one of my software engineers and hardware engineers and engineers period. 100% of NVIDIA has AI assistants, AI coders, and they’re busier than ever.” Not fewer engineers. Not smaller teams. Busier than ever. That is the line most companies are getting completely wrong right now. They hear “AI can write code” and immediately start cutting headcount. Huang did the opposite. He armed everyone. Huang: “And so the question is, what is the task versus what is the job? No different than a financial analyst; the task is mess around with spreadsheets, but the job is to make financial advice. The job is to help a customer.” Writing code was always the task. It was never the job. The job is architecture. Knowing what to build. Why it matters. How it fits into a system that actually creates value. Code is the execution layer between the idea and the outcome. Nothing more. When you automate that layer, you don’t eliminate the engineer. You eliminate the bottleneck between what they can envision and what they can ship. The companies using AI to cut headcount are optimizing for cost. The companies using AI to multiply output are optimizing for territory. Nvidia chose territory. Every engineer at the most valuable semiconductor company on Earth now operates with an AI assistant. Not a pilot program. Not an experiment. Company-wide. Every function. Every team. And the result is not less work. It is more work. Faster. At a scale that was physically impossible twelve months ago. The companies that understand the difference between eliminating engineers and unleashing them will build what comes next. The ones that don’t will watch their best talent walk out the door to the ones that did.

Dustin

82,844 görüntüleme • 6 ay önce

Most people will spend 4 hours tonight consuming. Zero creating. Then wonder why nothing changes. 1) Scroll until you fall asleep. 2) Or watch this. A Stanford lecture on Agentic AI that teaches you more about how AI automation actually works than 6 months of copying Make and n8n tutorials. Tool calling. Multi-step workflows. Planning. Reflection. The foundations behind every automation system that actually works. Most people learn this by copying tutorials blindly. Stanford teaches you WHY agents work the way they do. Watch the lecture first. Then go through 9 real build projects across 2 weeks: APIs. Webhooks. LLM integration. AI workflows. No theory. Just build. The people who do this weekend will understand AI automation at a level that makes everything they build after faster, cleaner, and more powerful. The people who skip it will still be copying tutorials in 2027 wondering why nothing they build actually works. Completely free. Bookmark this before you open Netflix.

Most people will spend 4 hours tonight consuming. Zero creating. Then wonder why nothing changes. 1) Scroll until you fall asleep. 2) Or watch this. A Stanford lecture on Agentic AI that teaches you more about how AI automation actually works than 6 months of copying Make and n8n tutorials. Tool calling. Multi-step workflows. Planning. Reflection. The foundations behind every automation system that actually works. Most people learn this by copying tutorials blindly. Stanford teaches you WHY agents work the way they do. Watch the lecture first. Then go through 9 real build projects across 2 weeks: APIs. Webhooks. LLM integration. AI workflows. No theory. Just build. The people who do this weekend will understand AI automation at a level that makes everything they build after faster, cleaner, and more powerful. The people who skip it will still be copying tutorials in 2027 wondering why nothing they build actually works. Completely free. Bookmark this before you open Netflix.

CyrilXBT

17,158 görüntüleme • 5 ay önce

New short course: Attention in Transformers: Concepts and Code in PyTorch. Last week we released a course on how LLM transformers work. This week, go deeper and learn about the technical ideas behind the attention mechanism, and see how to code it in PyTorch. This course is built with Joshua Starmer, Founder and CEO of StatQuest. The attention mechanism was a breakthrough that led to transformers, the architecture powering large language models like ChatGPT. Transformers, introduced in the 2017 paper: "Attention is All You Need" by Viswani and others, took off because of its highly scalable design. In this course, you’ll learn how the attention mechanism, a key element of transformer-based LLMs, works and implement it in PyTorch. You'll develop deep intuition about building reliable, functional, and scalable AI applications. What you will do: - Understand the evolution of the attention mechanism, a key breakthrough that led to transformers. - Learn the relationships between word embeddings, positional embeddings, and attention. - Learn about the Query, Key, and Value matrices, and how to produce and use them in attention. - Walk through the math required to calculate self-attention and masked self-attention to learn why and how they work. - Understand the difference between self-attention and masked self-attention and how one is used in the encoder to build context-aware embeddings and the other is used in the decoder for generative outputs. - Learn the details of the encoder-decoder architecture, cross-attention, and multi-head attention and how they are all incorporated into a transformer. - Use PyTorch to code a class that implements self-attention, masked self-attention, and multi-head attention. There're lots of exciting technical details in this course. Please sign up here:

New short course: Attention in Transformers: Concepts and Code in PyTorch. Last week we released a course on how LLM transformers work. This week, go deeper and learn about the technical ideas behind the attention mechanism, and see how to code it in PyTorch. This course is built with Joshua Starmer, Founder and CEO of StatQuest. The attention mechanism was a breakthrough that led to transformers, the architecture powering large language models like ChatGPT. Transformers, introduced in the 2017 paper: "Attention is All You Need" by Viswani and others, took off because of its highly scalable design. In this course, you’ll learn how the attention mechanism, a key element of transformer-based LLMs, works and implement it in PyTorch. You'll develop deep intuition about building reliable, functional, and scalable AI applications. What you will do: - Understand the evolution of the attention mechanism, a key breakthrough that led to transformers. - Learn the relationships between word embeddings, positional embeddings, and attention. - Learn about the Query, Key, and Value matrices, and how to produce and use them in attention. - Walk through the math required to calculate self-attention and masked self-attention to learn why and how they work. - Understand the difference between self-attention and masked self-attention and how one is used in the encoder to build context-aware embeddings and the other is used in the decoder for generative outputs. - Learn the details of the encoder-decoder architecture, cross-attention, and multi-head attention and how they are all incorporated into a transformer. - Use PyTorch to code a class that implements self-attention, masked self-attention, and multi-head attention. There're lots of exciting technical details in this course. Please sign up here:

Andrew Ng

132,544 görüntüleme • 1 yıl önce

Sam Altman, CEO of OpenAI: "GPT 5.6 is 54% more efficient on agentic coding, and it's still the best on the market for half the cost." AI spend has never mattered as much as it does today. As a result the models keep getting better and cheaper at the same time. This is going to widen the gap between the people using AI agents and everyone else, because now it's not just powerful, it's cheap. That's exactly why i wrote a full step by step guide on agents, what they are and how to build your own from scratch. Read it below.

Sam Altman, CEO of OpenAI: "GPT 5.6 is 54% more efficient on agentic coding, and it's still the best on the market for half the cost." AI spend has never mattered as much as it does today. As a result the models keep getting better and cheaper at the same time. This is going to widen the gap between the people using AI agents and everyone else, because now it's not just powerful, it's cheap. That's exactly why i wrote a full step by step guide on agents, what they are and how to build your own from scratch. Read it below.

Anatoli Kopadze

91,858 görüntüleme • 2 ay önce

Founder & CEO of Replit ⠕ Amjad Masad on the 1000x engineer: “The buzzword in Silicon Valley has been the 10x engineer. 2-3 years ago, I talked about the 1000x engineer, highly leveraged by autonomous agents." "You're not just one person, you’re a team of engineers. Every engineer is a sort of manager now." "We're not at 1000x yet. But pretty soon, the difference between a great engineer who knows how to use AI tools and an okay or good one is going to be 100x.”

Founder & CEO of Replit ⠕ Amjad Masad on the 1000x engineer: “The buzzword in Silicon Valley has been the 10x engineer. 2-3 years ago, I talked about the 1000x engineer, highly leveraged by autonomous agents." "You're not just one person, you’re a team of engineers. Every engineer is a sort of manager now." "We're not at 1000x yet. But pretty soon, the difference between a great engineer who knows how to use AI tools and an okay or good one is going to be 100x.”

TBPN

77,687 görüntüleme • 9 ay önce

OpenAI Dev Day Just kicked off and it was the biggest day for AI since the launch of GPT-4 in April. Here's a 3 minute video with everything you need to know and a full summary: 1. GPT-4 Turbo is released. It is much better than GPT-4, can accept up to 128k characters and is 3x cheaper. The knowledge cut off is now April 2023 2. Text to Speech is also being released. They offer 6 hyper-realistic voice models that are the best I've heard from any other AI t-t-s software on the market. 3. Copyright Shield. OpenAI will actually defend your legal costs if you get sued for copyright infringement using their API! 4. GPTs. Maybe the most exciting IMHO, you can now create custom "GPTs" by training your own model of GPT and selling it on their store. This will create a massive shakeup of the current AI software landscape and is the first step towards autonomous agents.

OpenAI Dev Day Just kicked off and it was the biggest day for AI since the launch of GPT-4 in April. Here's a 3 minute video with everything you need to know and a full summary: 1. GPT-4 Turbo is released. It is much better than GPT-4, can accept up to 128k characters and is 3x cheaper. The knowledge cut off is now April 2023 2. Text to Speech is also being released. They offer 6 hyper-realistic voice models that are the best I've heard from any other AI t-t-s software on the market. 3. Copyright Shield. OpenAI will actually defend your legal costs if you get sued for copyright infringement using their API! 4. GPTs. Maybe the most exciting IMHO, you can now create custom "GPTs" by training your own model of GPT and selling it on their store. This will create a massive shakeup of the current AI software landscape and is the first step towards autonomous agents.

Mark Gadala-Maria

79,199 görüntüleme • 2 yıl önce

Jensen Huang just told you who is winning the most important race on Earth. For fifty years, America held an unchallenged monopoly on the future. We built the transistor. We launched the internet. We wrote the source code for the modern world. Then the man who builds the physical backbone of every AI system on the planet read the score out loud. Huang: “50% of the world’s AI researchers are Chinese.” Half the minds building what comes next are not ours. Huang: “70% of last year’s AI patents are published by China.” Seven out of every ten blueprints for the next era are being written in Mandarin. Huang: “Nine out of the ten top science and technology schools in the world are now in China.” The talent pipeline did not slow down. It reversed direction. Huang: “We used to lead most of them; now they lead most of them. This has completely flipped in the last half to a decade.” Fifty years of American intellectual supremacy. Inverted in less than ten. This is not a rivalry between OpenAI and DeepSeek. This is not a stock ticker or a quarterly earnings call. This is the largest transfer of civilizational power in the modern era. And it is happening while the West drafts safety frameworks and fills out compliance paperwork. Huang: “They have a large population of highly qualified students. They work incredibly hard. This is a country with an enormous might.” China does not treat AI like a product category. They treat it as the single variable that decides who writes the rules for the next century. The West keeps asking what AI should be allowed to do. China keeps asking how fast they can build it. That gap is not philosophical. It is existential. This is not a left fight. This is not a right fight. This is a survival fight. And right now, America is not fighting it like one. The nation that controls the talent controls the research. The nation that controls the research controls the models. The nation that controls the models does not ask permission. It sets the terms. History never remembers the civilization with the better safety committee. It remembers the one that refused to stop building.

Jensen Huang just told you who is winning the most important race on Earth. For fifty years, America held an unchallenged monopoly on the future. We built the transistor. We launched the internet. We wrote the source code for the modern world. Then the man who builds the physical backbone of every AI system on the planet read the score out loud. Huang: “50% of the world’s AI researchers are Chinese.” Half the minds building what comes next are not ours. Huang: “70% of last year’s AI patents are published by China.” Seven out of every ten blueprints for the next era are being written in Mandarin. Huang: “Nine out of the ten top science and technology schools in the world are now in China.” The talent pipeline did not slow down. It reversed direction. Huang: “We used to lead most of them; now they lead most of them. This has completely flipped in the last half to a decade.” Fifty years of American intellectual supremacy. Inverted in less than ten. This is not a rivalry between OpenAI and DeepSeek. This is not a stock ticker or a quarterly earnings call. This is the largest transfer of civilizational power in the modern era. And it is happening while the West drafts safety frameworks and fills out compliance paperwork. Huang: “They have a large population of highly qualified students. They work incredibly hard. This is a country with an enormous might.” China does not treat AI like a product category. They treat it as the single variable that decides who writes the rules for the next century. The West keeps asking what AI should be allowed to do. China keeps asking how fast they can build it. That gap is not philosophical. It is existential. This is not a left fight. This is not a right fight. This is a survival fight. And right now, America is not fighting it like one. The nation that controls the talent controls the research. The nation that controls the research controls the models. The nation that controls the models does not ask permission. It sets the terms. History never remembers the civilization with the better safety committee. It remembers the one that refused to stop building.

Dustin

57,802 görüntüleme • 5 ay önce