Asif Razzaq’s Post

A breakthrough in AI processing? AI, specifically the major Large Language Models (LLMs) are remarkably effective at responding to conversational queries. However, at the same time they still have issues that relegate them to the “helper” category. A major reason for their current limitations is their massive hardware requirements. There are two general areas where the hardware is an issue: Training and usage. Training LLMs requires mind-boggling volumes of data and computational power. Using them also requires huge computational power, and even with that the responses are still slow and faltering, since they are limited by the number of tokens (language elements) they can process in a second. Anyone who has used an LLM can testify to watching the response get generated painfully slowly, sometimes word-by-word. A small company called Groq has recently been in the news. They have created a new hardware component specifically to address the performance problem of using LLMs. They call it a Language Processing Unit (LPU). Today’s approach is to use Graphics Processing Units (GPUs) as the hardware to drive LLMs. Nvidia is the primary source of these GPUs. The basic architecture of GPUs has remained relatively unchanged since their introduction. By contrast, the LPU introduces a specialized approach focused on optimizing the processing capabilities of LLMs. The LPUs do not currently seem to provide any advantage in the training of the LLMs, so that bottleneck remains, but early benchmarks have shown a performance increase of anywhere from 2x to 10x or more in executing LLMs. If these results hold up under further testing and production deployment, what are some of the implications? Real-time responses to queries will be possible, which means more intelligent and responsive systems that can react and adapt in real-time. Applications such as chat, voice assistants, autonomous vehicles, stock trading, and personalized customer service interactions suddenly become feasible. It could be possible to run LLMs on local devices (servers, not phones or PCs). While services like ChatGPT and Gemini today run through cloud API services, the benefits of local LLM processing for privacy, efficiency, and security are significant for many enterprises. The possible uses of LLMs would be increased, leading to the development of entirely new applications that are currently not feasible. This could revolutionize various fields like scientific research, creative content generation, drug discovery, and complex reasoning agents. Running state-of-the-art LLMs is economically prohibitive for most companies today. Improved performance could lead to lower computational costs, making it more affordable for organizations to leverage LLMs. The ability to execute LLMs with higher performance could enable the deployment of more complex models or the simultaneous running of multiple models, opening up new possibilities.

NVIDIA AI Introduces ChatQA: A Family of Conversational Question Answering (QA) Models that Obtain GPT-4 Level Accuracies Quick read: https://lnkd.in/gwdvqVQV Paper: https://lnkd.in/gYKzcvX6 #artificialintelligence #largelanguagemodels #languagemodels #llms #ai NVIDIA NVIDIA AI

🚀𝐖𝐡𝐲 𝐢𝐬 𝐭𝐡𝐞𝐫𝐞 𝐚 𝐛𝐮𝐳𝐳 𝐚𝐫𝐨𝐮𝐧𝐝 𝐭𝐡𝐞 𝐢𝐧𝐭𝐞𝐫𝐧𝐞𝐭 𝐚𝐛𝐨𝐮𝐭 𝐆𝐏𝐓-𝟒𝐨? OpenAI released GPT-4o, and its launch is creating a buzz online. GPT-4o (“o” for “omni”) is a significant step towards much more natural human-computer interaction. It accepts any combination of text, audio, image, and video as input and generates any combination of text, audio, and image outputs. It can respond to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds, similar to human response time in a conversation. It matches GPT-4 Turbo performance on text in English and code, with significant improvements on text in non-English languages, while also being much faster and 50% cheaper in the API. GPT-4o is especially better at vision and audio understanding compared to existing models. 📌𝐊𝐞𝐲 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲𝐬 𝐟𝐫𝐨𝐦 𝐆𝐏𝐓𝟒𝐨 : 📍𝐔𝐧𝐦𝐚𝐭𝐜𝐡𝐞𝐝 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐚𝐧𝐝 𝐒𝐩𝐞𝐞𝐝: GPT-4o not only delivers the highest quality but also ranks among the fastest large language models. 📍𝐈𝐝𝐞𝐚𝐥 𝐟𝐨𝐫 𝐋𝐚𝐭𝐞𝐧𝐜𝐲-𝐒𝐞𝐧𝐬𝐢𝐭𝐢𝐯𝐞 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬: For use cases where speed and latency are critical—previously dominated by models like Claude 3 Haiku or Mixtral 8x7b—GPT-4o now stands as a strong contender, though it comes with a higher cost. 📍𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐯𝐞 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐨𝐧 𝐍𝐯𝐢𝐝𝐢𝐚 𝐇𝐚𝐫𝐝𝐰𝐚𝐫𝐞: While Groq was previously the only provider to break from the trade-off curve using its proprietary LPU chips, OpenAI has achieved this on Nvidia hardware, sparking curiosity about GPT-4o's potential performance on Groq's systems. #AI #LLM #GPT4o #OpenAI #LanguageModels #NLP #ChatGPT4 #Data

With the advent of large language models (LLMs) such as GPT-3, Megatron-Turing, Chinchilla, PaLM-2, Falcon, and Llama 2, remarkable progress in natural language generation has been made in recent years. However, despite their ability to produce human-like text, foundation LLMs can fail to provide helpful and nuanced responses aligned with user preferences. The current approach to improving LLMs involves supervised fine-tuning (SFT) on human demonstrations followed by reinforcement learning from human feedback (RLHF). While RLHF can enhance performance, it has some limitations, including training complexity and a lack of user control. To overcome these challenges, the NVIDIA Research Team has developed and released SteerLM, a new four-step technique that simplifies LLM customization while enabling dynamic steering of model outputs based on attributes you specify, as part of NVIDIA NeMo. https://lnkd.in/gt-AY6RR

📅 March 19, 2024 AIBuzzWorld Daily Newsletter! Dive into the fascinating world of Artificial Intelligence and be the first to learn about the latest AI news: 1. **STABILITY AI RELEASES STABLE VIDEO 3D** 🎥🚀      • Introduces the generation of 3D models from single images.   • Enhances research and commercial applications, outperforming Stable Zero 123.   • Available for research and commercial use with Stability AI membership.   • Readmore: https://lnkd.in/dWNWUtrp 2. **APPLE RESEARCHERS ACHIEVE BREAKTHROUGH IN MULTIMODAL AI** 🍎🔬      • Sets new benchmarks for training large language models on text and images.   • Leads to superior performance in multimodal AI tasks.   • Could significantly impact AI's understanding and processing of complex data.   • Readmore: https://lnkd.in/gxCTpq88 3. **GOOGLE RESEARCHERS UNVEIL AN AI THAT CAN BRING STILL VIDEOS TO LIFE** 🌍📸      • VLOGGER generates lifelike videos from a single photo.   • Showcases potential for dynamic content creation in various fields.   • Could revolutionize digital marketing, filmmaking, and virtual reality.   • Readmore: https://lnkd.in/gHRZEy_K 4. **MICROSOFT HAS ADDED THE GPT-4 TURBO LLM TO THE FREE VERSION OF COPILOT** 💻💡      • Enhances capabilities of Copilot with advanced GPT-4 Turbo LLM.   • Pro users can access older models and the GPT Builder for custom chatbots.   • Democratizes powerful AI tools for a broader user base.   • Readmore: https://lnkd.in/g8BttAnx 5. **KOREAN RESEARCHERS POWER-SHAME NVIDIA WITH NEW NEURAL AI CHIP** 🇰🇷💥      • C-Transformer chip offers significant energy efficiency improvements.   • Challenges Nvidia's A100 GPU with an alternative neural AI chip technology.   • Could lead to more sustainable and cost-effective AI computing solutions.   • Readmore: https://lnkd.in/gqRAb9qK #AI #MachineLearning #ArtificialIntelligence #StabilityAI #Apple #Google #Microsoft #NVIDIA #AIChip #GPT4 #MultimodalAI #TechNews #Innovation #DeepLearning #Robotics #AIResearch #Technology #Video3D #GenerativeAI #EfficientInference #Sustainability

🌐 Llama 3.1: A New Era of AI Precision and Accessibility 🌐 The AI landscape has just expanded with the release of Llama 3.1, and it’s a game-changer! With its advanced capabilities, Llama 3.1 is poised to revolutionize how we interact with technology. - 8B Model: Perfect for multilingual tasks with a focus on efficiency - 70B Model: Balances performance and resource usage for complex language processing - 405B Model: The powerhouse, ideal for high-stakes, deep learning scenarios 🎯 Accuracy: Llama 3.1 models have been trained on NVIDIA's H100 GPUs, setting new benchmarks in accuracy, and surpassing over 150 benchmarks. 📜 Revolutionary Licensing: Meta has updated the licensing, allowing for model distillation and synthetic data creation, fostering innovation and collaboration Embrace the future with Llama 3.1—where precision meets accessibility. https://lnkd.in/gECF5uQk #Llama3_1 #AI #MachineLearning #Innovation

Nvidia paper claims: Open source AI outperforms ChatGPT in retrieval #AIadvancement 🤝 Follow us on Discord 🔜: https://lnkd.in/gt823Zd3 _ ❇️ Summary: A recent research paper by Nvidia researchers found that an open-source AI system using retrieval augmentation can outperform proprietary chatbot models like OpenAI’s GPT-3.5. The paper compares techniques for handling long context in large language models (LLMs) and concludes that a combination of extending the context window and retrieval is the most efficient approach. The hybrid retrieval-augmented LLaMA model achieved better scores than GPT-3.5-turbo and performed similarly to OpenAI’s Davinci model. The researchers believe that integrating retrieval techniques with long context is a promising direction for building more capable open-source conversational AI. Hashtags: #chatGPT #AIRevolution #InnovationInProgress

📅 March 19, 2024 AIBuzzWorld Daily Newsletter! Dive into the fascinating world of Artificial Intelligence and be the first to learn about the latest AI news: 1. **STABILITY AI RELEASES STABLE VIDEO 3D** 🎥🚀      • Introduces the generation of 3D models from single images.   • Enhances research and commercial applications, outperforming Stable Zero 123.   • Available for research and commercial use with Stability AI membership.   • Readmore: https://lnkd.in/g2eaHVq5 2. **APPLE RESEARCHERS ACHIEVE BREAKTHROUGH IN MULTIMODAL AI** 🍎🔬      • Sets new benchmarks for training large language models on text and images.   • Leads to superior performance in multimodal AI tasks.   • Could significantly impact AI's understanding and processing of complex data.   • Readmore: https://lnkd.in/gnXV-SKf 3. **GOOGLE RESEARCHERS UNVEIL AN AI THAT CAN BRING STILL VIDEOS TO LIFE** 🌍📸      • VLOGGER generates lifelike videos from a single photo.   • Showcases potential for dynamic content creation in various fields.   • Could revolutionize digital marketing, filmmaking, and virtual reality.   • Readmore: https://lnkd.in/g293WveZ 4. **MICROSOFT HAS ADDED THE GPT-4 TURBO LLM TO THE FREE VERSION OF COPILOT** 💻💡      • Enhances capabilities of Copilot with advanced GPT-4 Turbo LLM.   • Pro users can access older models and the GPT Builder for custom chatbots.   • Democratizes powerful AI tools for a broader user base.   • Readmore: https://lnkd.in/gF3B8y_8 5. **KOREAN RESEARCHERS POWER-SHAME NVIDIA WITH NEW NEURAL AI CHIP** 🇰🇷💥      • C-Transformer chip offers significant energy efficiency improvements.   • Challenges Nvidia's A100 GPU with an alternative neural AI chip technology.   • Could lead to more sustainable and cost-effective AI computing solutions.   • Readmore: https://lnkd.in/gZp_vHnT #AI #MachineLearning #ArtificialIntelligence #StabilityAI #Apple #Google #Microsoft #NVIDIA #AIChip #GPT4 #MultimodalAI #TechNews #Innovation #DeepLearning #Robotics #AIResearch #Technology #Video3D #GenerativeAI #EfficientInference #Sustainability

AI Models Overview: LLMs (Large Language Models) and MLLMs (Multimodal Large Language Models) are AI models trained with vast amounts of real-world information to replicate and enhance human intelligence in machines. LLMs focus on language, while MLLMs handle multiple data types, including text, audio, and images. Purpose and Applications: 1. LLMs are used for tasks like speech recognition, translation, and text analysis. 2. MLLMs are employed in more diverse applications, such as generating images from text prompts, processing DNA sequences, and even in autonomous driving and semiconductor design. Training Challenges: 1. Both LLMs and MLLMs require significant data for training, involving extensive preprocessing, iterations, and adjustments. The quality of these models depends heavily on the quality of the training data. 2. Continuous Learning: For many applications, the training of these models is an ongoing process, allowing them to update with new information or improve their functionalities over time. Examples of LLMs and MLLMs: Notable examples include ChatGPT by OpenAI, Bard/Gemini by Google DeepMind, and Llama by Meta. These models serve various functions, from chatbots to designing semiconductors. How Pure Storage Enhances LLM Performance and Accuracy: - 1. Retrieval-Augmented Generation (RAG): Pure Storage, in collaboration with NVIDIA, uses RAG to improve the performance and customization of LLMs. RAG allows LLMs to access external, specific, and proprietary data sources, making them more accurate, timely, and relevant. 2. Vector Search Lookup: The RAG pipeline improves the accuracy and relevance of LLM inference by providing a vector search lookup of the query against cleaned and vectorized data as inputs. This process enhances the model's ability to generate more precise and contextually relevant responses. 3. Ideal Solution for Enterprises: The combination of NVIDIA GPUs, NVIDIA networking, NVIDIA microservices, and Pure Storage FlashBlade//S creates an optimal infrastructure for enterprises looking to integrate LLMs into their production business processes. This setup ensures high performance, scalability, and efficiency in managing and processing large datasets for LLM applications.

Notable AI industry updates from the last week of May 2024: GPT-4 and ChatGPT APIs from OpenAI: A GPT-4 Model Was Released OpenAI introduced GPT-4, a new, state-of-the-art language model and expanded on the ChatGPT APIs. These are the ones who advance the capabilities in natural language understanding and generation. Google DeepMind Integration: DeepMind sold its research labs to Google on August 5, 2021, integrating its research teams with the company. This is a partnership that seeks to jump-start AI research and innovation. Windows 11 Voice Assistant by Microsoft: Microsoft introduced a Windows 11-focused AI assistant. This is to make more intelligent features in order to provide the best user experience and drive user productivity from AI. Market Cap Nvidia Milestone: One of the most prestigious artificial intelligence hardware and software companies: NVIDIA reached a mammoth valuation of $1 Trillion Market Cap. That growth is spurred by the ongoing AI boom. Artificial Life and Bio-Inspired AI for Safer Automotive Applications: The system is around 100 times faster than its counterparts on current automotive cameras through the use of AI in combination with a breakthrough type of camera, called a dynamic vision sensor. This advancement is a crucial development for self-driving cars and road security. These developments underscore the ongoing development and influence of AI in all areas of life – from language models to fields related to safety and efficiency. The industry is still changing and we can look forward to a great future.