AmarelUS’ Post

🚀 Exciting breakthrough in LLM reliability! 🧠NexusRaven-V2, our cutting-edge function-calling LLM, has set a new standard in minimizing AI hallucinations, surpassing GPT-4's performance in a recent third-party independent research benchmark. Dive into our latest blog post to explore how we're pioneering reliable agents with minimal hallucinations: [https://lnkd.in/egUU9wpz] Key Highlights: 🏆 Zero Hallucinations: NexusRaven-V2 showcased remarkable accuracy with zero hallucinations in 840 tests, focusing on tool selection and usage – a significant leap over GPT-4 with 23 hallucinations. 📈 Enhanced Success Rates: It boasts a 9% higher success rate than GPT-4 in information-seeking applications requiring meticulous attention to detail and a 4% increase in adversarial scenarios that demand a deep understanding of tool documentation, even with vague tool and API argument names. Try NexusRaven-V2 on Huggingface: [https://lnkd.in/eF6r9qgt] Check out the original third-party benchmark: [https://lnkd.in/ehAM5UAi] #GenAI #LLM #NexusRavenV2 #Technology #Innovation

🗣 Through many customer conversations, we discovered the top adoption criterion of GenAI agents to be the reliability to accomplish tasks with minimal hallucinations. 📢 Checkout how Nexusflow leverage a new agent building paradigm, with extractive reasoning as the first-class citizen for reliable tool-use agents.

There's been a series of interesting releases of domain/application specific LLMs. This one promises to be extremely good at API/Tool use. The big question is whether integrating with multiple smaller LLMs will end up being worthwhile for the incremental benefit when the major LLMs are continuously improving.

🌟 The Future Automation - LLM Agents 🌟 LLM-based agents or LLM agents integrate large language models (LLMs) with advanced modules like planning, memory and tool usage. They represent a paradigm shift in how we solve complex problems by combining LLMs with external resources and dynamic reasoning.  🎯 Core Components 🔸 User Request: Input or question from the user. 🔸 Agent/Brain: The LLM acts as the core controller. 🔸 Planning: Breaks down tasks into manageable subtasks. 🔸 Memory: Stores and recalls past actions and data. 🔸 Tools: Interfaces with external resources (e.g., APIs, databases). 💡 LLM agents are setting the stage for the future of intelligent automation. Here's why: 1. Enhanced Complexity Handling : LLM agents integrate large language models (LLMs) with advanced modules like planning, memory, and tool usage. This allows them to tackle complex, multi-step tasks beyond the reach of standalone LLMs. 2. Dynamic Problem-Solving : Combining LLMs with external tools and databases enables agents to address intricate queries, such as analyzing decade-long trends and visualizing data, by breaking down tasks and leveraging real-time information. 3. Adaptive Learning and Memory : With both short-term and long-term memory modules, LLM agents can recall past interactions and refine their strategies, leading to more accurate and contextually aware responses. 4. Versatile Applications : From drug discovery (ChemCrow) to coding assistance (ChatDev), LLM agents are revolutionizing fields by automating complex processes and enhancing efficiency. 5. Robust Frameworks : Tools like LangChain, AutoGPT, and frameworks such as ReAct and Reflexion are empowering LLM agents to operate effectively in dynamic environments and handle diverse tasks. Agents represent the next leap in AI, offering sophisticated capabilities for planning, adaptation and execution. #AI #MachineLearning #Automation #TechInnovation #LLMAgents #FutureOfWork 

Unlike traditional prompting, ReAct prompts instruct LLMs to generate two key things: Reasoning Traces: These are step-by-step explanations of the LLM's thought process as it tackles a task. Task-Specific Actions: These are the actual actions the LLM takes within the environment to complete the task. This two-pronged approach offers several advantages: Reduced Fact Hallucination: Traditional prompting can lead to LLMs making up facts to support their answers.ReAct's reasoning traces help mitigate this issue. Dynamic Reasoning: The LLM can continuously assess the situation, adjust its reasoning, and take new actions as needed. Integration with External Knowledge: ReAct allows the LLM to interact with external sources like knowledge bases, potentially leading to more accurate and informed responses. Benefits of ReAct Prompting: Improved Accuracy: ReAct can lead to more reliable and trustworthy outputs from LLMs. Enhanced Adaptability: Agents trained with ReAct prompting can handle unexpected situations and adapt their strategies on the fly. Deeper Understanding: Reasoning traces provide valuable insights into the LLM's thought process, aiding researchers in debugging and improvement. Real-world Applications: ReAct prompting is still under development, but it holds promise for various real-world applications, including: Chatbots: ReAct-trained chatbots could provide more helpful and informative responses by dynamically reasoning through user queries. Question Answering Systems: These systems could generate more comprehensive and accurate answers,explaining their reasoning process. Intelligent Automation: Agents trained with ReAct prompting could handle complex tasks in dynamic environments, making them more versatile and efficient. Overall, ReAct prompting represents a significant step forward in LLM development. By combining reasoning and acting,it paves the way for more powerful and adaptable AI agents. #ReActPrompting #LLMReasoning #ActingReasoning #AIExplainability #TrustworthyAI

🌟 LLM Agents are truly redefining the future of intelligent automation! 🤖 The combination of LLMs with advanced modules like planning, memory, and tool usage is a breakthrough in problem-solving. These agents go beyond the capabilities of standalone LLMs by integrating external resources and employing dynamic reasoning, making them incredibly powerful. 1️⃣ Enhanced Complexity Handling: The ability of LLM agents to break down complex tasks into manageable subtasks and interface with external tools allows them to handle multi-step, intricate problems. This makes them ideal for tasks requiring real-time data, trend analysis, or even highly technical fields like drug discovery. 2️⃣ Dynamic Problem-Solving: By leveraging external resources like APIs and databases, LLM agents can go beyond static responses, addressing sophisticated queries, such as data visualization or long-term trend analysis. This adaptability allows for real-time processing and more comprehensive solutions. 3️⃣ Adaptive Learning & Memory: The incorporation of short-term and long-term memory is a game-changer. Agents can store past interactions and refine their problem-solving strategies over time, resulting in more contextually accurate and user-tailored responses. 4️⃣ Versatile Applications: From fields like drug discovery (ChemCrow) to coding assistance (ChatDev), LLM agents are transforming industries by automating complex workflows, improving efficiency, and enabling quicker decision-making. 5️⃣ Robust Frameworks: Tools like LangChain, AutoGPT, and frameworks like ReAct and Reflexion are equipping LLM agents with the structure they need to operate in dynamic, ever-changing environments, allowing them to perform a wide range of tasks with precision. LLM agents represent the next leap in AI by offering advanced capabilities for planning, adaptation, and execution. As they continue to evolve, they will be at the heart of intelligent automation, driving innovation and transforming industries across the board. Thanks for sharing this—LLM agents are definitely the future of automation and AI! #AI #LLMAgents #Automation #MachineLearning #TechInnovation #DeepLearning #FutureOfWork #LangChain #AutoGPT #AIResearch

Applied #multiagent solution in production use-case - and it didn’t work well. The goal is to have “Copilot” for a specialized domain. Depending on sub-domain, copilot should be able to perform different actions: answer questions based on the provided documents, reason about data model, generate code, etc. One more challenge is that some tasks involve multiple sub-domains (e.g., code generation and a data model). Not to overload one agent with too many tools, I wanted to try multi-agent solution, with each agent specializing in one sub-domain. Chose #crewAI - it has nice clean interface, ability to delegate tasks between agents, and the overview from deeplearning.ai course was promising. Alas… In practice, it struggled - agents continuously tried to apply non-existing tools (e.g., sentence “Review and revise provided draft” was treated like there the agent should call tool “Review and revise provided draft”), delegation worked very unstable, agents frequently missed some arguments. Maybe crewAI is optimized for GPT-4, yet in the same time agents from Langchain worked reasonably well with GPT-3.5-turbo. And I need to stick with GPT-3.5 for costs reasons. #multi-agent solution still looks promising. I earlier played with Autogen, but it is also a bit finicky, here I need more control. So decided to create my own lightweight solution with multiple collaborating agents using Langchain as the base (but replacing AgentExecutor). And it is turning out to be a good decision as it helps me to better understand how agents function. But more on that next time 🙂 #ai_journey #multi_agents

6 Open Source Tools to Build Your Own #AI #Model #Foundation Foundation Setup: How to Start Your Autonomous #LLM This article explains how to use #open-source tools to build a prototype system where #intelligent #agents can answer work-related questions and act as dedicated assistants for specialized reports. First, the article introduces #Langchain, a tool that efficiently connects large language models (LLMs) with various ecosystem components like planning, memory, and tool components, enabling complex interactions. Next, it covers #Flowise, a zero-code quick setup tool that helps users build LLM application platforms in just one minute, boosting development efficiency. The article then discusses the domain knowledge base, showing how to create an automatic Q&A #bot. This bot generates semantic vectors and stores them in a vector database to help answer technical questions. Throughout, the article highlights how these tools can quickly build prototype systems, improve development efficiency, and enable automatic Q&A functions. It also details how to use #LocalAI to set up an autonomous open-source LLM foundation and how to enhance agent performance using the #Llama series models. Finally, it covers using #AutoGPT to create an army of intelligent agents that can think, divide tasks, plan, and complete missions independently. Overall, the article provides an in-depth guide on leveraging open-source tools to build intelligent agent systems, demonstrating their practical applications in the workplace. https://lnkd.in/gMqQ3Ntn

📰 𝗔𝘂𝗴𝘂𝘀𝘁 𝗔𝗜 𝗡𝗲𝘄𝘀 𝗥𝗼𝘂𝗻𝗱𝘂𝗽 (𝗣𝗮𝗿𝘁 𝟭): 𝗠𝗮𝗷𝗼𝗿 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁𝘀 𝗶𝗻 𝗔𝗜 𝗠𝗼𝗱𝗲𝗹 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁, 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲, 𝗮𝗻𝗱 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 📰 It was an action-packed summer in AI land…here are some key updates from August! 𝗔𝗜 𝗠𝗼𝗱𝗲𝗹 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁: ° 𝗢𝗽𝗲𝗻𝗔𝗜 𝗚𝗣𝗧-𝟰𝗼 𝗙𝗶𝗻𝗲-𝗧𝘂𝗻𝗶𝗻𝗴: OpenAI released fine-tuning for GPT-4o. ° 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗣𝗵𝗶 𝟯.𝟱: Launched multilingual and scientific-focused AI models. ° 𝗚𝗼𝗼𝗴𝗹𝗲 𝗚𝗲𝗺𝗺𝗮 𝟮: Released models for improved safety and content classification. ° 𝗔𝗜𝟮𝟭 𝗝𝗮𝗺𝗯𝗮 𝟭.𝟱: New model generates tokens faster for long contexts. 𝗔𝗜 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲: ° 𝗔𝗽𝗽𝗹𝗲 𝗮𝗻𝗱 𝗡𝘃𝗶𝗱𝗶𝗮’𝘀 𝗢𝗽𝗲𝗻𝗔𝗜 𝗧𝗮𝗹𝗸𝘀:Major investment discussions to boost AI capabilities. ° 𝗔𝗠𝗗’𝘀 𝗭𝗧 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 𝗔𝗰𝗾𝘂𝗶𝘀𝗶𝘁𝗶𝗼𝗻: Strategic $4.9 billion move to bolster AI infrastructure. ° 𝗖𝗲𝗿𝗲𝗯𝗿𝗮𝘀 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗧𝗲𝗰𝗵: Offering substantial speed in AI inference. ° 𝗚𝗿𝗼𝗾 𝗔𝗜 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲: Raised $640 million to expand fast AI hardware solutions. 𝗔𝗜 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀: ° 𝗦𝗦𝗜: OpenAI founder Ilya Sutskever raised $1B for a new safety-focused AI startup. ° 𝗖𝗹𝗮𝘂𝗱𝗲’𝘀 𝗔𝗿𝘁𝗶𝗳𝗮𝗰𝘁𝘀: Anthropic introduced Artifacts to enhance real-time collaboration. ° 𝗥𝘂𝗻𝘄𝗮𝘆 𝗚𝗲𝗻-𝟯 𝗔𝗹𝗽𝗵𝗮 𝗧𝘂𝗿𝗯𝗼: New AI tool for faster video generation. 𝗟𝗼𝗼𝗸𝗶𝗻𝗴 𝗔𝗵𝗲𝗮𝗱: Stay tuned for Part 2 as we continue to explore the latest industry news and insights. Follow us for more updates on how Celara is pioneering innovation in technology and AI. #AI #Innovation #Technology #Celara #AIAugustRoundup