Absolutely mind-blowing news from Google's recent breakthrough in AI research! Their new paper introduces "Infini-attention," a groundbreaking technique that enables large language models (LLMs) to process text of infinite length while maintaining constant memory and compute requirements. This advancement could revolutionize how we approach long-context language understanding and generation. Check out the paper here: https://lnkd.in/gVuz2pih #AI #MachineLearning #GoogleAI #InfiniAttention #LanguageModels
Blueprint Europe’s Post
More Relevant Posts
-
AI Strategist, Product Visionary, Tech Innovator, and Angel Investor. Trusted Advisor to 250+ organizations on AI and X tech.
The landscape of AI is rapidly evolving, and one of the most promising advancements in this space is retrieval-augmented generation (RAG). RAG has been a game-changer for large language models (LLMs), allowing them to deliver accurate, contextually relevant information by tapping into vast databases. While traditional RAG methods have been effective, the emergence of Agentic RAG marks a new frontier, introducing a more dynamic and sophisticated approach to processing and decision-making. Explore our article about Agentic RAG here: https://buff.ly/4ddJw0u #RAG #AI #AIagents #future
-
-
The landscape of AI is rapidly evolving, and one of the most promising advancements in this space is retrieval-augmented generation (RAG). RAG has been a game-changer for large language models (LLMs), allowing them to deliver accurate, contextually relevant information by tapping into vast databases. While traditional RAG methods have been effective, the emergence of Agentic RAG marks a new frontier, introducing a more dynamic and sophisticated approach to processing and decision-making. Explore our article about Agentic RAG here: https://buff.ly/4ddJw0u #RAG #AI #AIagents #future
-
-
The landscape of AI is rapidly evolving, and one of the most promising advancements in this space is retrieval-augmented generation (RAG). RAG has been a game-changer for large language models (LLMs), allowing them to deliver accurate, contextually relevant information by tapping into vast databases. While traditional RAG methods have been effective, the emergence of Agentic RAG marks a new frontier, introducing a more dynamic and sophisticated approach to processing and decision-making. Explore our article about Agentic RAG here: https://buff.ly/4ddJw0u #RAG #AI #AIagents #future
-
-
Powering Executives and Boards with Gen AI Business Roadmaps | ESG & AI Governance | Transformation | Chartered Board Director | Follow for blueprints on AI & Leadership, Business Scale Up and Board Maturity 🇨🇦🇬🇧🇪🇺
🚀 JetMoE-8B: The Future of Language Models! Discover how this groundbreaking model with 2.2 billion parameters is changing the game in text processing. Training in just 2 weeks for $0.08 million. Are you ready for the future of AI? Comment your thoughts below! #AI #LanguageModels #Innovation Original article: https://lnkd.in/euSVQbpg
-
-
Google's PaLM, short for Pathways Language Model, is a groundbreaking large language model (LLM) pushing the boundaries of what's possible with language understanding and generation. This behemoth of AI, with over 540 billion parameters, is trained on a colossal dataset of text and code, empowering it to excel in diverse tasks. Read More:🔗👇 https://lnkd.in/gJ_2frmj #vertexapi #vertexaipalmapi #textcompletion #generativeai #googlevertex
-
-
Sales Specialist | Sales and Marketing Automation | HubSpot | ZOHO | AI Video Generator |
Stability AI brings 12B parameters to Stable LM 2 model update Stability AI is bringing new power to its Stable LM 2 generative AI large language models for text content.Read More https://ift.tt/QKbEyRu https://ift.tt/AZ39sgG
-
Google's PaLM, short for Pathways Language Model, is a groundbreaking large language model (LLM) pushing the boundaries of what's possible with language understanding and generation. This behemoth of AI, with over 540 billion parameters, is trained on a colossal dataset of text and code, empowering it to excel in diverse tasks. Read More:🔗👇 https://lnkd.in/gJ_2frmj #vertexapi #vertexaipalmapi #textcompletion #generativeai #googlevertex
-
-
Higher Education Management | Leadership & Policy | International & Comparative Education | She/Her
Policy and regulations will be necessary as we move forward with better Understanding and Control of AI.
Artificial intelligence regulation is a hot topic. In new policy briefs, MIT researchers look at large language models, “pro-worker AI,” and labels for AI-generated content. https://lnkd.in/e_fqKBh6
-
🌐 𝐍𝐚𝐯𝐢𝐠𝐚𝐭𝐢𝐧𝐠 𝐀𝐈 𝐑𝐞𝐠𝐮𝐥𝐚𝐭𝐢𝐨𝐧: 𝐌𝐈𝐓'𝐬 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐟𝐮𝐥 𝐑𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬! 🤖 Large Language Models 🛠️ Pro-Worker AI Read more from the link below. #AIRegulation #MITInsights #ResponsibleAI #FutureTech 🚀🤖
Artificial intelligence regulation is a hot topic. In new policy briefs, MIT researchers look at large language models, “pro-worker AI,” and labels for AI-generated content. https://lnkd.in/e_fqKBh6
MIT experts recommend policies for safe, effective use of AI | MIT Sloan
-
With AI moving at lightning speed, it can be nearly impossible to keep up with advancements in Large Language Models (LLMs). The Infinitive team explored 10 groundbreaking areas LLMs will redefine, from improved reasoning and creativity to enhanced explainability and multimodal understanding. Click here to see what the future holds for the next generation of LLMs and what could be possible: https://lnkd.in/e8UzwNfa #AI #MachineLearning #LLMs #Innovation #TechTrends
-
Co-Founder of Altrosyn and DIrector at CDTECH | Inventor | Manufacturer
5moYour mention of Google's breakthrough in AI research, specifically the introduction of "Infini-attention," reflects a pivotal moment akin to the advent of multi-layer neural networks, which revolutionized deep learning. Just as those advancements expanded the capabilities of AI systems, enabling them to learn hierarchical representations of data, "Infini-attention" promises to reshape how LLMs process and understand vast amounts of text. However, could this approach inadvertently overlook subtle contextual nuances in favor of processing efficiency, potentially affecting the quality of generated outputs?