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Chinese AI Model DeepSeq: Despite U.S. sanctions restricting access to Nvidia AI chips, Chinese researchers developed DeepSeq, an AI model reportedly more powerful than OpenAI’s. Overcoming Hardware Limitations: With only a limited supply of Nvidia chips, Chinese engineers achieved groundbreaking efficiency optimizations, reducing training resource requirements to just 1/100th...

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Johannes Maria

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24,512 просмотров • 1 год назад •via X (Twitter)

ОбразованиеНаука и технологииНовости и политика
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Комментарии: 11

Фото профиля Rosanna Tarsiero
Rosanna Tarsiero1 год назад

In Italy they say, "La necessita' aguzza l'ingegno"

Фото профиля BillOckham
BillOckham1 год назад

Don’t forget about the cost effectiveness- just $6M compared to 20s of M for 🇺🇸AIs🤷🤷🤷

Фото профиля nicco
nicco1 год назад

If technology can't serve all people, then why we still need them？people get brainwashed that high tech needs more money. It isn't. It only raise big companies like apple. But what Apple did in the past years？more expensive phones no one doesn't want it.

Фото профиля Hollywood_Moon_Landing
Hollywood_Moon_Landing1 год назад

Deep Seek

Фото профиля Chinese Genesis
Chinese Genesis1 год назад

Open source is the way. I don't trust any of these companies having AI over us. The way we keep AI in check is make it open source, and kill proprietary AI dominance.

Фото профиля Soojin Snow
Soojin Snow1 год назад

DeepSeq's development showcases China's determination to advance AI research despite challenges

Фото профиля The Information
The Information1 год назад

A recent production hiccup with Nvidia’s latest AI chip shows the growing pressures in its relationship with its main chipmaking partner, TSMC.

Фото профиля SpartakReview
SpartakReview1 год назад

Stop using Meta products

Фото профиля Mariano Cisneros Jr.
Mariano Cisneros Jr.1 год назад

Propaganda

Фото профиля Mariano Cisneros Jr.
Mariano Cisneros Jr.1 год назад

Deepseek is a CCP syop designed to lure Western innovation to enhance, embed, and rely upon it in order to advance a communist authoritarian surveillance agenda using AI.

Фото профиля 🗣 📣 UBI NOW
🗣 📣 UBI NOW1 год назад

😯

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China is once again unsettling its American competitors in the AI race. Chinese labs have returned to the spotlight with the unveiling of GLM 5.2, described as the most powerful AI model ever trained in China — and one of the leading open‑source models worldwide. 🔍 Key highlights of “GLM 5.2”: ♦️ It is considered the strongest AI model developed in China to date, and ranks among the smartest open‑source models available. ♦️ It operates at a cost less than one‑tenth that of Anthropic’s latest models, making it a far more economical option. ♦️ It is likely to attract developers and companies seeking powerful, low‑cost models — especially amid growing technical and geopolitical restrictions. ♦️ Some evaluations place the model among the top globally, trailing OpenAI’s models while outperforming several competitors in specialized benchmarks and performance tests.
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China is once again unsettling its American competitors in the AI race. Chinese labs have returned to the spotlight with the unveiling of GLM 5.2, described as the most powerful AI model ever trained in China — and one of the leading open‑source models worldwide. 🔍 Key highlights of “GLM 5.2”: ♦️ It is considered the strongest AI model developed in China to date, and ranks among the smartest open‑source models available. ♦️ It operates at a cost less than one‑tenth that of Anthropic’s latest models, making it a far more economical option. ♦️ It is likely to attract developers and companies seeking powerful, low‑cost models — especially amid growing technical and geopolitical restrictions. ♦️ Some evaluations place the model among the top globally, trailing OpenAI’s models while outperforming several competitors in specialized benchmarks and performance tests.

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14,293 просмотров • 1 месяц назад

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🔥BREAKING: Nvidia CEO Jensen Huang pushes back against efforts to ban Chinese AI models in the US. In an interview with Axios, Huang said US firms should "ABSOLUTELY" be allowed to use Chinese models. "These Chinese models are excellent." "The market's misunderstood the impact of DeepSeek the first time. It's misunderstood the impact of Kimi again this time." "With great open AI models, it's great for the whole industry." "Great models lead to great use, which leads to great growth." Huang’s statement directly challenges Trump officials and US labs seeking to shut Chinese AI models out.
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🔥BREAKING: Nvidia CEO Jensen Huang pushes back against efforts to ban Chinese AI models in the US. In an interview with Axios, Huang said US firms should "ABSOLUTELY" be allowed to use Chinese models. "These Chinese models are excellent." "The market's misunderstood the impact of DeepSeek the first time. It's misunderstood the impact of Kimi again this time." "With great open AI models, it's great for the whole industry." "Great models lead to great use, which leads to great growth." Huang’s statement directly challenges Trump officials and US labs seeking to shut Chinese AI models out.

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Chinese AI company Deepseek recently made waves when it announced R1, an open-source reasoning model that it claimed achieved comparable performance to OpenAI’s o1, at a fraction of the cost. But for those following AI developments closely, Deepseek and R1 didn’t come out of nowhere. In this episode of Decoded, YC's Diana breaks down the key engineering optimizations behind Deepseek's new models — and how they fit within the broader history of recent AI breakthroughs.
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Chinese AI company Deepseek recently made waves when it announced R1, an open-source reasoning model that it claimed achieved comparable performance to OpenAI’s o1, at a fraction of the cost. But for those following AI developments closely, Deepseek and R1 didn’t come out of nowhere. In this episode of Decoded, YC's Diana breaks down the key engineering optimizations behind Deepseek's new models — and how they fit within the broader history of recent AI breakthroughs.

Y Combinator

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🚀 Excited to release AIBuildAI — an AI agent that automatically builds AI models 🏆 #1 on OpenAI MLE-Bench 💻 GitHub: Building AI models still requires a lot of manual work — designing models, writing code, training them, tuning hyperparameters, and iterating on results. We developed AIBuildAI to automate much of this process. The system runs an agent loop that automatically: • analyzes the task • designs models • writes code to implement them • trains the models • tunes hyperparameters • evaluates model performance • iteratively improves the models 🏆 On OpenAI’s MLE-Bench benchmark, AIBuildAI ranked #1:
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🚀 Excited to release AIBuildAI — an AI agent that automatically builds AI models 🏆 #1 on OpenAI MLE-Bench 💻 GitHub: Building AI models still requires a lot of manual work — designing models, writing code, training them, tuning hyperparameters, and iterating on results. We developed AIBuildAI to automate much of this process. The system runs an agent loop that automatically: • analyzes the task • designs models • writes code to implement them • trains the models • tunes hyperparameters • evaluates model performance • iteratively improves the models 🏆 On OpenAI’s MLE-Bench benchmark, AIBuildAI ranked #1:

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We’re expanding our strategic partnership with Microsoft to: ⚡ Deliver OpenAI’s state-of-the-art models in Snowflake Cortex AI on Microsoft Microsoft Azure. Customers will soon be able to build AI-powered data agents to run analytical workflows on structured and unstructured data using OpenAI’s models. ⚡ Make Snowflake Cortex Agents available in Microsoft 365 Copilot and Microsoft 365 apps, ensuring AI insights become more accessible for users and enabling better decision-making across the enterprise. Learn more about how we’re bringing easy, efficient, and trusted AI to enterprises around the world:
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We’re expanding our strategic partnership with Microsoft to: ⚡ Deliver OpenAI’s state-of-the-art models in Snowflake Cortex AI on Microsoft Microsoft Azure. Customers will soon be able to build AI-powered data agents to run analytical workflows on structured and unstructured data using OpenAI’s models. ⚡ Make Snowflake Cortex Agents available in Microsoft 365 Copilot and Microsoft 365 apps, ensuring AI insights become more accessible for users and enabling better decision-making across the enterprise. Learn more about how we’re bringing easy, efficient, and trusted AI to enterprises around the world:

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11,011 просмотров • 1 год назад

Just watched Kai-Fu Lee 's interview on the US-China AI landscape. A few takeaways felt especially useful✌️: 1. The US is currently about 15 months ahead of China in frontier AI models. 2. Top US AI companies are like “geniuses trying to win the Nobel Prize,” while Chinese AI companies are more like “a group of smart kids forming a study group.” They still compete, but open source creates more shared learning. 3. China’s AI companies face a real monetization challenge in enterprise software. Many Chinese companies are less used to annual subscriptions or usage-based pricing, and still tend to think of software as project-based work. 4. For the same product, Western, Middle Eastern, and Central Asian companies may be willing to pay around 8x more than Chinese companies. That helps explain why many Chinese AI companies are expanding overseas. 5. China may produce many breakthrough AI consumer apps, because Chinese companies are especially strong at consumer apps, hardware, robotics, embodied AI, and AI-first devices. 6. The US may continue to lead in frontier models, while China may move faster in turning AI into products people actually use. 7. His advice for young people in the age of AI: you don’t necessarily need to become an engineer first. What matters more is accountability, taste, conviction, courage, and foresight. More👇
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How does the U.S. win the AI race without tightening export controls on advanced AI chips to China? NVIDIA CEO: • China will not base its defense on Western tech • The Pentagon doesn’t use Chinese chips • China has domestic AI chips (Huawei, startups) and manufactures at scale, so its military has access • The U.S. needs a nuanced plan to stay ahead • Make the American tech stack the global standard • If others can’t build on the American stack, they’ll build on another stack • KPI David Sacks 5y → 80% = win; 20% = loss • China: 50% of AI researchers, 30% of the tech market, ~1B users • Cost of isolation: forfeits ~30% of the market, blocks diffusion of U.S. tech, concedes the rest of the world • President Trump wouldn’t view isolation as a winning strategy To win the AI race, the U.S. must lead with the best chips—and win developers and markets worldwide.

Ask Perplexity

156,952 просмотров • 9 месяцев назад

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🤝 Alphabet and NVIDIA are expanding their decade-long partnership to advance agentic AI, robotics, drug discovery, and more. This involves deep co-engineering with integrated platforms, open-source frameworks, and managed services. ✅ Google Cloud is one of the first to bring the NVIDIA Blackwell platform to the cloud. ✅ Google Distributed Cloud using NVIDIA Confidential Computing on #NVIDIABlackwell for enterprises to run Google Gemini on-premises. ✅ Integration of the NVIDIA AI platform across Vertex AI, Cluster Director and Google Kubernetes Engine. ✅ Bringing the NVIDIA Nemotron family of open models to Vertex AI Model Garden. Watch the full video to learn more ➡️

NVIDIA AI Infrastructure

268,567 просмотров • 6 месяцев назад

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Exciting update for AI developers! The Hugging Face Hub is now more natively integrated into Google Cloud Vertex AI Model Garden. Search through thousands of open Generative AI models from Hugging Face models & deploy them with one click to Vertex AI or GKE. 🤯 What's new: 🔎 Browse and search thousands of Hugging Face models directly within the Vertex AI Model Garden and filter based on what is currently trending in the community. 🚀 Accelerate your AI projects by leveraging readily available one-click deploy your model to Vertex AI or Google Kubernetes Engine (GKE). ⭐️ Featuring popular open models from BlackForestLabsAI - Unofficial FLUX.1, AI at Meta Llama 3.1, Mistral AI, Google DeepMind Gemma, and countless others. Get Started:

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How is an open ecosystem powering the next generation of AI for developers? Recording live from the heart of the action at AMD's Advancing AI 2025, Chain of Thought host Conor Bronsdon welcomes AMD’s Anush Elangovan, VP of AI Software, and Sharon Zhou, VP of AI. Together they unpack AMD's groundbreaking transformation from a hardware giant to a leader in full-stack AI, committed to an open ecosystem. Discover how new MI350 GPUs deliver mind-blowing performance with advanced data types and why ROCm 7 and AMD Developer Cloud offer Day Zero support for frontier models. This relentless pace of hardware and software innovation is reshaping the AI landscape. Then Conor welcomes Sharon Zhou, VP of AI at AMD, to discuss making AMD's powerful software stack truly accessible and how to drive developer curiosity. Sharon explains strategies for creating a "happy path" for community contributions, fostering engagement through teaching, and listening to developers at every stage. She shares her predictions for the future, including the rise of self-improving AI, the critical role of heterogeneous compute, and the potential of "vibes based feedback" to guide models. This vision for democratizing access to high-performance AI, driven by a deep understanding of the developer journey, promises to unlock the next generation of applications. 00:00 Live from AMD's Advancing AI 2025 Event 00:30 Introduction to Anush Elangovan 01:38 The MI350 GPU Series Unveiled 04:57 CDNA4 Architecture Explained 07:00 The Future of AI Infrastructure 08:32 AMD's Developer Cloud and ROCm 7 11:50 Cultural Shift at AMD 14:48 Open Source and Community Contributions 18:35 Software Longevity and Ecosystem Strategy 22:19 AI Agents and Performance Gains 27:36 AI's Role in Solving Power Challenges 28:11 Thanking Anush 28:42 Introduction to Sharon Zhou 29:45 Sharon's Focus at AMD 30:39 Engaging Developers with AMD's AI Tools 31:24 Listening to the AI Community 33:56 Open Source and AI Development 45:04 Future of AI and Self-Improving Models 48:04 Final Thoughts and Farewell
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How is an open ecosystem powering the next generation of AI for developers? Recording live from the heart of the action at AMD's Advancing AI 2025, Chain of Thought host Conor Bronsdon welcomes AMD’s Anush Elangovan, VP of AI Software, and Sharon Zhou, VP of AI. Together they unpack AMD's groundbreaking transformation from a hardware giant to a leader in full-stack AI, committed to an open ecosystem. Discover how new MI350 GPUs deliver mind-blowing performance with advanced data types and why ROCm 7 and AMD Developer Cloud offer Day Zero support for frontier models. This relentless pace of hardware and software innovation is reshaping the AI landscape. Then Conor welcomes Sharon Zhou, VP of AI at AMD, to discuss making AMD's powerful software stack truly accessible and how to drive developer curiosity. Sharon explains strategies for creating a "happy path" for community contributions, fostering engagement through teaching, and listening to developers at every stage. She shares her predictions for the future, including the rise of self-improving AI, the critical role of heterogeneous compute, and the potential of "vibes based feedback" to guide models. This vision for democratizing access to high-performance AI, driven by a deep understanding of the developer journey, promises to unlock the next generation of applications. 00:00 Live from AMD's Advancing AI 2025 Event 00:30 Introduction to Anush Elangovan 01:38 The MI350 GPU Series Unveiled 04:57 CDNA4 Architecture Explained 07:00 The Future of AI Infrastructure 08:32 AMD's Developer Cloud and ROCm 7 11:50 Cultural Shift at AMD 14:48 Open Source and Community Contributions 18:35 Software Longevity and Ecosystem Strategy 22:19 AI Agents and Performance Gains 27:36 AI's Role in Solving Power Challenges 28:11 Thanking Anush 28:42 Introduction to Sharon Zhou 29:45 Sharon's Focus at AMD 30:39 Engaging Developers with AMD's AI Tools 31:24 Listening to the AI Community 33:56 Open Source and AI Development 45:04 Future of AI and Self-Improving Models 48:04 Final Thoughts and Farewell

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37,186 просмотров • 1 год назад

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Preview of a Gaia 🌱 device! Turn-by-turn voice conversation with your own knowledge assistant (a Gaia node). 💸 $15 worth of hardware (pre-tariff!) 🌱 Voice-to-text, LLM and text-to-voice AI models on Gaia nodes 📖 Open source all-the-way — hardware, firmware, server, AI models 🦀 Rust all-the-way from firmware to AI server Notice how I switched to another language (Chinese) in the middle of the conversation and it seamlessly switched too. Very smart. PS. we are still optimizing performance before the software release.

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14,983 просмотров • 1 год назад

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 source is fundamentally necessary for many industries to join the AI revolution." "NVIDIA has the scale and the motivation to build and continue to build these AI models for as long as we shall live." The chip seller benefits when every model is open. NVIDIA is the chip seller. 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
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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 source is fundamentally necessary for many industries to join the AI revolution." "NVIDIA has the scale and the motivation to build and continue to build these AI models for as long as we shall live." The chip seller benefits when every model is open. NVIDIA is the chip seller. 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

14,769 просмотров • 2 месяцев назад

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Chips are the foundation of every AI experience. That's why understanding the hardware behind the software has never mattered more. When you ask a chatbot a question or generate an image, there's a physical chip somewhere doing the heavy lifting. For most of computing history, that was a CPU—the "brain" of a computer—great for general tasks needed to run software and operating systems. AI is more complex: for workloads called training and inference, AI needs to perform trillions of calculations in parallel. That's where AI accelerators come in. Purpose-built accelerators can deliver significantly better performance and efficiency than general-purpose chips. Amazon Web Services Trainium chips are an example—purpose-built for AI training and inference. New chips for a new era. ⬇️

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27,151 просмотров • 2 месяцев назад

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Unsloth AI and NVIDIA are Revolutionizing Local LLM Fine-Tuning: From RTX Desktops to DGX Spark Fine-tune popular AI models faster with Unsloth on NVIDIA RTX AI PCs such as GeForce RTX desktops and laptops to RTX PRO workstations and the new DGX Spark to build personalized assistants for coding, creative work, and complex agentic workflows. The landscape of modern AI is shifting. We are moving away from a total reliance on massive, generalized cloud models and entering the era of local, agentic AI. Whether it is tuning a chatbot to handle hyper-specific product support or building a personal assistant that manages intricate schedules, the potential for generative AI on local hardware is boundless. However, developers face a persistent bottleneck: How do you get a Small Language Model (SLM) to punch above its weight class and respond with high accuracy for specialized tasks? The answer is Fine-Tuning, and the tool of choice is Unsloth. Unsloth provides an easy and high-speed method to customize models. Optimized for efficient, low-memory training on NVIDIA GPUs, Unsloth scales effortlessly from GeForce RTX desktops and laptop all the way to the DGX Spark, the world’s smallest AI supercomputer...... Full analysis: NVIDIA NVIDIA AI NVIDIA AIDev NVIDIAnewsroom Unsloth AI Unsloth

Marktechpost AI Dev News ⚡

31,551 просмотров • 7 месяцев назад

U.S. Navy Bans DeepSeek Over 'Security Concerns' As 'Substantial' Evidence Emerges Chinese AI Ripped Off ChatGPT | ZeroHedge The U.S. Navy has instructed service members to avoid using the Chinese AI platform DeepSeek, citing "potential security and ethical concerns," according to CNBC. An email sent to "shipmates" in recent days, confirmed by CNBC on Tuesday, referenced the Navy's AI policy and emphasized the importance of refraining from using DeepSeek. The memo warned service members against using the platform "for any work-related tasks or personal use" and instructed them to "avoid downloading, installing, or using the DeepSeek model in any capacity." The warning follows the recent rise of DeepSeek’s R1 model, which has garnered significant attention worldwide, particularly within the U.S. business and technology sectors. The R1 model has demonstrated capabilities comparable to OpenAI’s models. In December, DeepSeek claimed it had successfully trained a large language model in just two months at a cost of $6 million—a figure disputed by technologists—despite U.S. restrictions on semiconductor chip exports to China. The R1, an open-source model, surged to the top of Apple’s app store rankings this week, triggering a market sell-off. Shares of AI chipmakers Nvidia and Broadcom plummeted by 17% on Monday, wiping out a combined $800 billion in market value. Nvidia has since recovered some of its losses. On Monday, DeepSeek announced a temporary restriction on user registrations, citing "large-scale malicious attacks" on its services, before later restoring normal operations. DeepSeek’s advancements have challenged the long-held belief that the U.S. was significantly ahead of China in AI development. Asked how R1 caught up to ChatGPT, AI and Crypto Czar David Sacks suggested that DeepSeek may have leveraged a technique known as "distillation" to train its model using OpenAI’s technology. “There’s a technique in AI called distillation, which you’re going to hear a lot about. It’s when one model learns from another model,” Sacks explained to Fox News. “Effectively, the student model asks the parent model millions of questions, mimicking the reasoning process and absorbing knowledge.” “They can essentially extract the knowledge out of the model,” he continued. “There’s substantial evidence that what DeepSeek did here was distill knowledge from OpenAI’s models.” “I don’t think OpenAI is too happy about this,” Sacks added. President Donald Trump has said that DeepSeek “should be a wake-up call” for U.S. tech companies. “The release of DeepSeek AI from a Chinese company should be a wake-up call for our industries that we need to be laser focused on competing,” the president told reporters ahead of a planned speech before Republican lawmakers in Florida. Read more:
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U.S. Navy Bans DeepSeek Over 'Security Concerns' As 'Substantial' Evidence Emerges Chinese AI Ripped Off ChatGPT | ZeroHedge The U.S. Navy has instructed service members to avoid using the Chinese AI platform DeepSeek, citing "potential security and ethical concerns," according to CNBC. An email sent to "shipmates" in recent days, confirmed by CNBC on Tuesday, referenced the Navy's AI policy and emphasized the importance of refraining from using DeepSeek. The memo warned service members against using the platform "for any work-related tasks or personal use" and instructed them to "avoid downloading, installing, or using the DeepSeek model in any capacity." The warning follows the recent rise of DeepSeek’s R1 model, which has garnered significant attention worldwide, particularly within the U.S. business and technology sectors. The R1 model has demonstrated capabilities comparable to OpenAI’s models. In December, DeepSeek claimed it had successfully trained a large language model in just two months at a cost of $6 million—a figure disputed by technologists—despite U.S. restrictions on semiconductor chip exports to China. The R1, an open-source model, surged to the top of Apple’s app store rankings this week, triggering a market sell-off. Shares of AI chipmakers Nvidia and Broadcom plummeted by 17% on Monday, wiping out a combined $800 billion in market value. Nvidia has since recovered some of its losses. On Monday, DeepSeek announced a temporary restriction on user registrations, citing "large-scale malicious attacks" on its services, before later restoring normal operations. DeepSeek’s advancements have challenged the long-held belief that the U.S. was significantly ahead of China in AI development. Asked how R1 caught up to ChatGPT, AI and Crypto Czar David Sacks suggested that DeepSeek may have leveraged a technique known as "distillation" to train its model using OpenAI’s technology. “There’s a technique in AI called distillation, which you’re going to hear a lot about. It’s when one model learns from another model,” Sacks explained to Fox News. “Effectively, the student model asks the parent model millions of questions, mimicking the reasoning process and absorbing knowledge.” “They can essentially extract the knowledge out of the model,” he continued. “There’s substantial evidence that what DeepSeek did here was distill knowledge from OpenAI’s models.” “I don’t think OpenAI is too happy about this,” Sacks added. President Donald Trump has said that DeepSeek “should be a wake-up call” for U.S. tech companies. “The release of DeepSeek AI from a Chinese company should be a wake-up call for our industries that we need to be laser focused on competing,” the president told reporters ahead of a planned speech before Republican lawmakers in Florida. Read more:

Owen Gregorian

75,351 просмотров • 1 год назад

America has the edge in the world’s most powerful AI models, no question. But China is playing a different game: efficiency, scale, and global deployment. Kyle Chan from Brookings China explains: "Does winning mean developing the world's most powerful AI models? If yes, then the us has the decisive lead. American ai models are widely recognized as the best in the world on virtually every metric you can think of, from math and reasoning to coding and AI agents, American private companies continue to set the pace at the technological frontier supported by deep capital markets, a dynamic innovation ecosystem, and the best talent from around the world. China's AI models are improving quickly but continue to lag behind. Chinese AI companies face real constraints on compute due to our export controls and their own limited capital resources, so instead they're prioritizing other goals. They're focused on efficiency, building models that are cheaper to train and run. They're focused on adoption using an open source strategy to win users around the world, and they're focused on integration with other platforms and services. On sheer performance, I would expect the gap between our models and theirs to persist or even widen over time. But model performance is not everything."
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America has the edge in the world’s most powerful AI models, no question. But China is playing a different game: efficiency, scale, and global deployment. Kyle Chan from Brookings China explains: "Does winning mean developing the world's most powerful AI models? If yes, then the us has the decisive lead. American ai models are widely recognized as the best in the world on virtually every metric you can think of, from math and reasoning to coding and AI agents, American private companies continue to set the pace at the technological frontier supported by deep capital markets, a dynamic innovation ecosystem, and the best talent from around the world. China's AI models are improving quickly but continue to lag behind. Chinese AI companies face real constraints on compute due to our export controls and their own limited capital resources, so instead they're prioritizing other goals. They're focused on efficiency, building models that are cheaper to train and run. They're focused on adoption using an open source strategy to win users around the world, and they're focused on integration with other platforms and services. On sheer performance, I would expect the gap between our models and theirs to persist or even widen over time. But model performance is not everything."

Select Committee on China

60,135 просмотров • 3 месяцев назад