NVIDIA partners with Perplexity and European AI innovators to boost local AI development

“Google DeepMind, Meta, and Nvidia are among the companies attempting to gain ground in the AI race by developing systems that aim to navigate the physical world by learning from videos and robotic data rather than just language.”

Alibaba Group Unveils AI Model with NVIDIA Chinese technology giant Alibaba has announced a significant expansion of its artificial intelligence (AI) capabilities through a strategic partnership with chipmaker Nvidia. For more... https://lnkd.in/d97C8y9z #Alibaba #Nvidia #AI

Gimme Credit cited in The Globe and Mail (10/1/2025): "AI is being employed by virtually every major company. And firms are finding more and more uses for AI in their operations, primarily because of the massive cost savings attained," Dave Novosel of Gimme Credit. Read Full Article Here: https://hubs.la/Q03LMVdm0 #gimmecredit #corporatebondresearch #inthenews

Japan is strategically positioning itself in the global AI race, not by sheer volume, but through a focused pursuit of "Sovereign AI"—developing models deeply attuned to its unique language and culture—and an ambitious national effort to reclaim its leadership in cutting-edge semiconductor manufacturing. While not as numerous as some other nations, Japan's domestic LLMs are proving highly performant, and its GPU strategy is a long-game play for future self-sufficiency. Japanese LLMs: Crafting Cultural Nuance and Efficiency The emphasis in Japan's LLM development is clear: create models that excel in the intricacies of the Japanese language and can operate with remarkable efficiency, often tailored for specific, sensitive applications. Shisa AI's Shisa V2 405B stands out as Japan's current top-performing LLM. Built upon the robust Llama 3.1 base, it has been meticulously fine-tuned with custom Japanese datasets, allowing it to compete with global flagships like GPT-4o and DeepSeek-V3 on Japanese benchmarks. This project showcases how focused, specialized labs can achieve world-class results. NTT's tsuzumi 2 represents a significant step towards a truly domestic and efficient AI. This model achieves world-class performance in Japanese language tasks while being remarkably lightweight, capable of running efficiently on a single GPU. Its design prioritizes robust specialized knowledge across sectors like finance, healthcare, and government, making it ideal for on-premise deployment where confidential data security is paramount. Rakuten Group's Rakuten AI 2.0 utilizes the efficient Mixture-of-Experts (MoE) architecture. This allows it to deliver performance comparable to much larger models at a significantly lower computational cost. Rakuten's focus is on optimizing AI for its vast ecosystem, even releasing a compact Rakuten AI 2.0 mini for edge computing applications. The Fugaku-LLM, a collaboration between RIKEN, Fujitsu, and Tohoku University, is unique. This 13-billion-parameter model was trained entirely from scratch on the Fugaku supercomputer, which uniquely uses custom Fujitsu CPUs instead of traditional GPUs. This project demonstrates Japan's capacity to leverage its distinctive hardware infrastructure for large-scale AI training, showing particular strength in humanities and social sciences tasks. These initiatives underscore Japan's commitment to building AI that is culturally aware, highly efficient, and strategically independent, serving both commercial and national interests.

South Korea just doubled down on sovereign AI — the government pledged ₩530 billion (~$390M) to five homegrown teams to build large language models tailored to Korean language and culture. This is a strategic push to reduce dependence on foreign AI, strengthen national security, and keep tighter control over data. Who’s in the running: - LG AI Research — Exaone 4.0: a hybrid reasoning model blending broad language understanding with advanced logical reasoning. - SK Telecom — A.X and personal AI agents, supported by SKT’s Titan supercomputer (powered by Nvidia GPUs) which is a core training infrastructure for the program. - Naver Cloud — HyperCLOVA and HyperCLOVA X, powering CLOVA X chatbots and the Cue generative search experience. - NC AI — a consortium that includes top Korean universities and industry partners aiming to build scalable multimodal generative models for industrial transformation. - Upstage — Solar Pro 2, the startup’s frontier-recognized model competing with global players. How the program supports winners: the Ministry of Science and ICT pairs funding with shared assets — jointly purchased datasets (valued at ~10 billion won), GPU resources, and help attracting global talent — while reviewing progress every six months and retaining only the best performers until two lead the sovereign AI drive. Why it matters: this initiative shows a different model for national AI strategy — investing in localized models that respect language, culture and data sovereignty while building competitive capability against global AI superpowers. Questions: - Should more countries pursue sovereign AI programs to protect data and boost local innovation? - What trade-offs should policymakers consider between openness to global models and national control? (Reporting referenced: Ministry of Science and ICT announcements, August 2025; industry reporting on infrastructure and consortiums, August 2025.) #AI #SovereignAI #SouthKorea #LargeLanguageModels #DataSovereignty #AIGovernance #TechPolicy #ML

Nvidia CEO Jensen Huang may be a major player in the artificial intelligence arms race — but his day-to-day chatbot interactions aren’t always complex. Huang, whose company makes many of the high-powered computer chips that power AI large language models, uses a variety of different chatbots like Google’s Gemini and OpenAI’s ChatGPT to help him write first drafts, he said at a Wired event. “I give it a basic outline, give it some PDFs of my previous talks, and I get it to write my first draft,” he said. “It’s really fantastic.” He isn’t the only tech leader to use AI chatbots for simple daily tasks.

🤖 Daily AI News Updates Based on recent developments in the AI industry, here are three significant updates that would interest AI professionals: **🚀 OpenAI DevDay 2025 Unveils Major Platform Expansion** OpenAI announced transformative updates at DevDay 2025, introducing AgentKit—a comprehensive toolkit for building and scaling AI agents—alongside a new ChatGPT app ecosystem that now serves 800 million weekly users[3]. The company also launched the Sora API and enhanced Codex capabilities that can handle day-long coding tasks[1]. CEO Sam Altman discussed the emergence of "zero-person companies" potentially worth billions, run entirely by AI agents, signaling a fundamental shift in how businesses might operate in the near future[1]. **💰 Major AI Infrastructure Investments Reshape the Landscape** Two significant funding announcements are accelerating AI compute capabilities. Cerebras secured $1.1 billion at an $8.1 billion valuation to expand its AI inference infrastructure, claiming 20x faster speeds than NVIDIA GPUs while serving trillions of tokens monthly[1]. Meanwhile, OpenAI struck a landmark partnership with AMD for 6GW of AI compute starting in late 2026, with the deal linking OpenAI to nearly 10% of AMD equity and triggering record stock performance[3]. These moves represent critical infrastructure investments as demand for AI processing power continues to surge. **🔬 Google DeepMind's Dreamer 4 Revolutionizes AI Learning Efficiency** Google DeepMind introduced Dreamer 4, an AI agent that learns tasks entirely within its own world model, achieving breakthrough efficiency by beating OpenAI's VPT using 100x less data[1]. This advancement in model-based reinforcement learning represents a significant step toward more sample-efficient AI systems that can learn complex tasks without requiring massive datasets, potentially democratizing access to advanced AI capabilities for organizations with limited data resources. #AI #ArtificialIntelligence #TechNews #Innovation #MachineLearning #AI #TechNews #Innovation #MachineLearning #ArtificialIntelligence

NVIDIA just made a $2 billion bet that could reshape the entire AI landscape. Reflection AI - a startup founded just 18 months ago by ex-Google DeepMind researchers - just closed one of the largest AI funding rounds in history. Here's why this matters more than the headline number: ✓ 15x valuation jump: From $545M to $8B in just 7 months ✓ Founded by the minds behind Gemini and AlphaGo ✓ Positioning as America's answer to Chinese AI dominance (DeepSeek, Qwen) ✓ Fully open-source approach vs. closed models like OpenAI The strategic context is fascinating. While OpenAI and Anthropic keep their models locked down, Reflection AI is betting that enterprises want ownership, control, and customization. Their CEO put it perfectly: "Once you get into enterprise territory, you want an open model. You want ownership, infrastructure control, and cost optimization." With backing from NVIDIA, Sequoia, Eric Schmidt, and others, they're not just building another AI company - they're building America's open-source AI champion. First frontier model drops early 2026. The race is officially on. What's your take - will open-source AI models eventually dominate the enterprise market, or do closed models have lasting advantages? #AI #OpenSource #TechFunding #Innovation #Enterprise

China’s AI Power Surge in LLMs (Alibaba Group's Qwen Moonshot AI's Kimi AI K2 DeepSeek AI #ZhipuAI's GLM-4.5 Baidu, Inc.’s #Ernie) is supported by Huawei #BaiduKunlun #BirenTechnology and #Cambricon on the chips side. China's technology sector has now achieved self-sufficiency in artificial intelligence, motivated by domestic competition and the need to circumvent international chip export restrictions. The Chinese LLM Landscape: An Open-Source Buffet Chinese companies are not just competing on model size, but on efficiency, context length and open-source availability, often utilising the highly efficient Mixture-of-Experts (MoE) architecture. Alibaba's Qwen Series is as a versatile and powerful all-rounder. Distinguished by its strong open-source commitment, Qwen excels in multilingual tasks (supporting over 100 languages) and features strong multimodal capabilities, handling text, images, and audio. Moonshot AI's Kimi K2 is gaining attention for its exceptional ability to handle extremely long context windows (e.g., 128K tokens). Kimi is a leader in agentic AI, meaning it can plan and execute complex, multi-step tasks autonomously. DeepSeek AI has carved out a niche by demonstrating coding and mathematical reasoning performance that rivals top global models, but at a reported fraction of the traditional training cost. Zhipu AI's GLM-4.5 is highly regarded for its robust tool-use and reliable function calling, which are critical for developers building sophisticated AI agents. Baidu’s Ernie Series, one of China’s original foundation models, continues to be a major player, using its extensive knowledge graph for enhanced reasoning in its latest multimodal versions. Ultimately, the Chinese LLM ecosystem is driving forward on efficiency and accessibility, with many top models being released as open-source alternatives to proprietary Western systems. The Hardware Race: Bypassing Restrictions with Domestic GPUs Necessity is the mother of invention thanks to US controls on NVIDIA's H100 forcing Chinese tech giants to heavily invest in domestic alternatives. Huawei Ascend is the most crucial domestic line of AI accelerators, with chips like the Ascend 910/920 being the go-to alternative. DeepSeek are specifically optimizing their LLMs to run efficiently on Ascend hardware. Baidu Kunlun develops its own chips primarily to power its proprietary LLMs and cloud infrastructure. Biren Technology and Cambricon are two other domestic startups that are quickly advancing their general-purpose computing GPUs to fill the void left by international sanctions. The primary strategy being employed is software optimization. By finding smarter, more efficient ways to train and run their LLMs, China is proving that they can extract performance comparable to top-tier models with less advanced or export-restricted hardware. The hardware race is therefore a dual challenge of both chip manufacturing and software ingenuity.

Did you know that by late 2025, Small Language Model agents could outperform massive LLMs, revolutionizing AI efficiency? NVIDIA's latest framework is making waves, and it's just one of the hottest trends buzzing on X today! Key insights from the latest AI buzz: - NVIDIA's paper outlines how compact SLM agents can tackle complex tasks faster and cheaper than giants like GPT models—perfect for scalable real-world apps. - Former OpenAI and DeepMind leaders just raised $300M to build "AI scientists" for breakthroughs in materials discovery, accelerating innovation in fields like energy and healthcare. - Agentic AI is transforming workflows, from providing conversation insights to enabling direct task automation, as investments shift toward foundational infrastructure like data pipelines. This ties into broader trends where AI is moving from hype to hyper-efficiency—think how synthetic data generation (via GANs) is fueling models without real-world limits, as highlighted in recent reports. For instance, imagine deploying SLM agents in your business to automate customer service without the massive compute costs—it's not sci-fi anymore! What's your take? Have you experimented with agentic AI in your projects, or how do you see SLMs changing the game? Share your thoughts below, and tag a colleague who needs to see this! As we kick off October, it's inspiring to see AI democratizing access to powerful tools—reminds me of how early web tech leveled the playing field. Let's build the future together! #AITrends #ArtificialIntelligence #NVIDIAAI #AgenticAI @NVIDIA @OpenAI @DeepMind