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I'm thrilled to share my latest article that dives deep into the 𝙉𝙚𝙢𝙤𝙩𝙧𝙤𝙣-4 340𝘽 family of models. These groundbreaking models, introduced by NVIDIA, are set to redefine the landscape of synthetic data generation and large language models. 🔍 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀 𝗼𝗳 𝘁𝗵𝗲 𝗡𝗲𝗺𝗼𝘁𝗿𝗼𝗻-𝟰 𝟯𝟰𝟬𝗕 𝗙𝗮𝗺𝗶𝗹𝘆: 𝗦𝘁𝗮𝘁𝗲-𝗼𝗳-𝘁𝗵𝗲-𝗔𝗿𝘁 𝗠𝗼𝗱𝗲𝗹𝘀: Discover the advanced capabilities of the Nemotron-4 340B-Base, Instruct, and Reward models. 𝗦𝘆𝗻𝘁𝗵𝗲𝘁𝗶𝗰 𝗗𝗮𝘁𝗮 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 (𝗦𝗗𝗚): Learn how these models can turbo-charge a wide range of synthetic data use cases. 𝗨𝗻𝗺𝗮𝘁𝗰𝗵𝗲𝗱 𝗤𝘂𝗮𝗹𝗶𝘁𝘆: Explore the high-quality, openly available models that come with a permissive license, enabling innovation across industries. 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀: Get a glimpse into the training journey and advanced architecture that make these models stand out. Curious to see how these models can transform your AI projects? Check out the full article to uncover the potential of the Nemotron-4 340B family and how it can elevate your AI initiatives to the next level. 👉 ͟𝙍͟𝙚͟𝙖͟𝙙͟ ͟𝙩͟𝙝͟𝙚͟ ͟𝙛͟𝙪͟𝙡͟𝙡͟ ͟𝙖͟𝙧͟𝙩͟𝙞͟𝙘͟𝙡͟𝙚͟ ͟𝙞͟𝙣͟ ͟𝙩͟𝙝͟𝙚͟ ͟𝙛͟𝙞͟𝙧͟𝙨͟𝙩͟ ͟𝙘͟𝙤͟𝙢͟𝙢͟𝙚͟𝙣͟𝙩͟ ͟𝙗͟𝙚͟𝙡͟𝙤͟𝙬͟!͟ ͟📖✨ #AI #MachineLearning #DataScience #LLM #ArtificialIntelligence

💡 "𝗧𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗔𝗜 𝗶𝘀 𝗮𝗯𝗼𝘂𝘁 𝘀𝗰𝗮𝗹𝗶𝗻𝗴 𝗱𝗮𝘁𝗮 𝗮𝗻𝗱 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝘁𝗼 𝗺𝗲𝗲𝘁 𝘂𝗻𝗽𝗿𝗲𝗰𝗲𝗱𝗲𝗻𝘁𝗲𝗱 𝗱𝗲𝗺𝗮𝗻𝗱𝘀." - 𝗠𝗮𝗿𝗰 𝗛𝗮𝗺𝗶𝗹𝘁𝗼𝗻, 𝗩𝗣 𝗼𝗳 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗮𝗻𝗱 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴, 𝗡𝗩𝗜𝗗𝗜𝗔 In our latest blog, Marc Hamilton shares insights into how AI is reshaping the data landscape and why addressing AI as a data center-scale challenge is critical. Together with NVIDIA, DDN is enabling organizations to harness the power of data intelligence and accelerate AI innovation at scale. 🌐 Don’t miss this exclusive interview and discover how we’re driving the next wave of AI breakthroughs: https://lnkd.in/giCxbnY3 👀 Video interview: https://lnkd.in/gaQsDGDn #AI #ArtificialIntelligence #ML #MachineLearning #LLMs #tech #technology #data #DataStorage #DataCenters #DataAnalytics #innovation #supercomputer #supercomputing

[News] This Week in AI: March 17–23 A brief recap of NVIDIA's GTC 2024; Inflection AI and Stability AI lost essential team members ; Stability AI launched SV3D and introduced Image Services on its Developer Platform; Runway announced strategic partnerships; Google was charged with a $270M fine by France's Autorité de la Concurrence https://buff.ly/3PBOWbV ↓ Are you interested in AI? Check out Data Phoenix (https://buff.ly/3vshw92) - the global AI and Data community of 8000+ Engineers, Executives, and Founders. #DataPhoenix #AI #ML #MachineLearning #DataScience #ArtificialIntelligence #News

Great discussion on the pace, foundational tech, and future of AI with NVIDA’s CEO. It is worth your hour.

AI is driving a huge surge in Data Centers, and training these models is power intensive. It takes 1000s of hours of GPU time to do unsupervised pre-training. ⚡ But once trained, the use of these models does not require huge amounts of power (relatively speaking) when they are in inference (thinking). In fact, it's quite possible, on most PCs to run some pretty powerful models on your computer locally. Additionally, open-source models are being 'quantized'. Essentially, the vectors utilized in parameters and weights are resolved to 32 bits in the big, deployed models. Quantization reduces this down to 16, 8, 4 and even 2. This reduces the precision of the model but has a staggeringly low loss rate at 8 bits vs. the full model. What does this mean in practice? Well, it means you don't have to lean on cloud-based AI to have a very capable AI capability powering apps within your business. You can have a very capable Llama-3, fine-tuned on your own data - which will outperform GPT-4o and Gemini in your specific use case, and you can do the fine tuning for pennies on the dollar (vs. the API costs of these players) on huggingface.co. I'd suggest everyone with a GPU in their PC downloads a copy of LLMStudio, and a base model Llama-3 Instruct model just to see how capable these 'little' 7 billion parameter models are. What does this mean? Well Microsoft, and Apple, are both releasing PC's with powerful GPUs to run LLMs locally. As technology advances (and we've already seen this), more and more training and running of AI applications will be local. So, while demand for AI will cause a surge in data center and energy demand, I think the trend will increasingly be for de-centralization and privacy, with chip manufacturers and applications running to catch up. I'd bet on a huge increase in power for Data Centers for AI for the next 5 - 10 years, but I wouldn't bank on it for the next 15. And yes - fine-tuned open-source models, really can take on the big boys: https://lnkd.in/e9Kdn5CW #AI #Energytransition #DataCenters #Power #ChatGPT #llama3 #Gemini

🌟 Inspiring Workshop Experience Neo4j X Qualcomm : GenAI and Graphs on Edge (Special Edition) 🌟 I had the incredible opportunity to attend the Neo4j X Qualcomm: GenAI and Graphs on Edge (Special Edition) workshop. It was an enriching hands-on experience that delved into the intersection of GenAI and graph databases on edge devices. Key Highlights: Qualcomm AI Hub: Nick Debeurre from Qualcomm shared insights on the benefits, challenges, and solutions of edge AI. The Qualcomm AI Hub offers a comprehensive suite of tools, including the AI Engine Direct SDK (QNN), Neural Processing SDK (SNPE), GenAI Interface Extensions (GenIE), and AI Model Efficiency Toolkits (AIMET)  Snapdragon X Elite : We explored the capabilities of the Snapdragon X Elite processor, which promises to revolutionize AI on edge devices with its powerful performance and efficiency. RB3 Gen 2: The RB3 Gen 2 platform: AI integration for industrial automation. Neo4j Graph Technology: Siddhant Agarwal from Neo4j provided an in-depth look at graph databases and their applications in AI, emphasizing the power of connected data. Phi-3 by Vinayak Hegde from Microsoft: Vinayak Hegde introduced Phi-3, optimize small language models, enhancing their efficiency and performance on edge devices. This innovation aims to make advanced AI capabilities more accessible and practical for various applications. A special thanks to Prashant Sharma for organizing this wonderful #DevelopersConference workshop. Your efforts made this event a huge success! #AI #GraphDatabases #EdgeComputing #Neo4j #Qualcomm #DevelopersConference

Nvidia's Vision on AGI: Nvidia CEO Jensen Huang predicts Artificial General Intelligence (AGI) could be realized within 5 years, transforming AI from task-specific applications to systems capable of human-level cognitive tasks. Solving AI Hallucinations: Huang addresses the challenge of AI hallucinations, where AI produces plausible but incorrect information, proposing a retrieval-augmented generation approach. This involves AI systems conducting thorough research to ensure accuracy before delivering answers, mimicking media literacy practices. The Human Role in an AGI Future: The advent of AGI raises significant questions about humanity's place in a future where machines may surpass human intelligence in every aspect. Huang's perspective encourages a dialogue on aligning AGI's capabilities with human values and ethics. A Call for Engagement: The insights from Huang at GTC 2024 serve as a call to action for the AI community to engage in discussions around the ethical development, deployment, and governance of AGI. It's crucial to consider how we can ensure AGI benefits society while mitigating risks. Looking Forward: As we stand on the brink of potential AGI breakthroughs, it's vital for professionals, researchers, and policymakers to envision and prepare for the integration of AGI into our lives. Discussing, planning, and setting the course for a future where AGI enhances human capabilities without compromising our values is imperative. How do you see AGI impacting your field or daily life in the next 5 years, and what steps should we take now to prepare for its integration? #AGI #ArtificialIntelligence #FutureTech

NVIDIA and Apple: Pioneering the Future of AI with ReDrafter 🌟 The AI landscape just got a major boost as NVIDIA and Apple join forces to accelerate large language model (LLM) performance. Through the innovative Recurrent Drafter (ReDrafter) technique, these tech giants are redefining text generation efficiency—making AI smarter, faster, and greener. 🌐✨ 🛠️ Key Highlights: ⚡ 2.7x speed-up in token generation with beam search and dynamic tree attention. 🌍 Open-source availability for global collaboration. 🌱 Reduced GPU usage, lowering costs and environmental impact. This historic partnership not only enhances user experiences but also sets a new benchmark in sustainable AI innovation. 🌟 🔍 Dive deeper into how ReDrafter is revolutionizing LLMs and shaping the future of AI in our latest blog post: https://lnkd.in/dBqHaPJ9 Follow us to stay ahead with cutting-edge insights from Dr. Shahid Masood and the expert team at 1950.ai! 🚀 #AI #LLM #ReDrafter #TechInnovation #MachineLearning #SustainableAI #NVIDIA #Apple #ShahidMasood #DrShahidMasood #1950ai

“At the end of the day, we care about building products that developers love and change the trajectory of the enterprises they work for. NVIDIA helps us with both.” - Chet Kapoor, CEO Check out DataStax CEO Chet Kapoor's interview on Quartz (https://lnkd.in/eH-E-bQ7) - at the 5 minute mark, he talks about DataStax partnership with NVIDIA and how important NeMo, Guardrails and Blueprints are to accelerating DataStax’s AI plans. There is no AI without data. There is no AI without unstructured data. There is no AI without unstructured data at scale. Datastax is helping customers modernize their data estate — empowering them with the right tools to build generative AI applications. NVIDIA helps them do this. Thank you Chet for your continued partnership and integration of NVIDIA NeMo (Retriever, Guardrails, Blueprints) to address DataStax’s three goals: agility for developers, cost-effectiveness, and scale.

𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝘀𝗰𝗶𝗲𝗻𝗰𝗲 𝗶𝘀 𝗱𝗲𝗮𝗱. Do this instead. In a recent talk, Jensen Huang, CEO of Nvidia, said that kids shouldn't learn programming anymore. He said that until now, most of us thought that everyone should learn to program at some point. But the actual opposite is the truth. With the rise of AI, nobody should have or need to learn to program anymore. He highlights that with AI tools, the technology divide between non-programmers and engineers is closing. . 𝗔𝘀 𝗮𝗻 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿, 𝗺𝘆 𝗲𝗴𝗼 𝗶𝘀 𝗵𝘂𝗿𝘁; 𝗺𝘆 𝗳𝗶𝗿𝘀𝘁 𝗿𝗲𝗮𝗰𝘁𝗶𝗼𝗻 𝗶𝘀 𝘁𝗼 𝘀𝗮𝘆 𝗶𝘁 𝗶𝘀 𝘀𝘁𝘂𝗽𝗶𝗱. But after thinking about it more thoroughly, I tend to agree with him. After all, even now, almost anybody can work with AI. This probably won't happen in the next 10 years, but at some point, 100% will do. At some point, we will ask our AI companion to write a program that does X for us or whatever. But, I think this is a great thing, as it will give us more time & energy to focus on what matters, such as: - solving real-world problems (not just tech problems) - moving to the next level of technology (Bioengineering, interplanetary colonization, etc.) - think about the grand scheme of things - be more creative - more time to connect with our family - more time to take care of our I personally think it is a significant step for humanity. . What do you think? As an engineer, do you see your job still present in the next 10+ years? Here is the full talk ↓↓↓ 🔗 A Conversation with the Founder of NVIDIA: Who Will Shape the Future of AI? https://lnkd.in/duxK_t2C #machinelearning #mlops #datascience