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Q 716. Compare RAG with fine-tuning for an LLM-powered Agent. When will you use RAG? When will you use fine-tuning? When would you like to use a combination? Note for website visitors - This platform hosts two weekly questions, one on Tuesday and the other on Friday. All previous questions can be found here: https://www.benchmarksixsigma.com/forum/lean-six-sigma-business-excellence-questions/. To participate in the current question, please visit the forum homepage at https://www.benchmarksixsigma.com/forum/. The question will be open until Tuesday or Friday at 5 PM Indian Standard Time, depending on the launch day. Responses will not be visible until they are reviewed, and only non-plagiarised answers with less than 5-10% plagiarism will be approved. If you are unsure about plagiarism, please check your answer using a plagiarism checker tool such as https://smallseotools.com/plagiarism-checker/ before submitting. All correct answers shall be published, and the top-rated answer will be displayed first. The author will receive an honorable mention in our Business Excellence dictionary at https://www.benchmarksixsigma.com/forum/business-excellence-dictionary-glossary/ along with the related term. Some people seem to be using AI platforms to find forum answers. This is a risky approach as AI responses are error prone as our questions are application-oriented (they are never straightforward). Have a look at this funny example - https://www.benchmarksixsigma.com/forum/topic/39458-using-ai-to-respond-to-forum-questions/ We also use an AI content detector at https://quillbot.com/ai-content-detector. Only answers with less than 45-50% AI-generated content will be approved. -
Named Entity Recognition (NER)
Vishwadeep Khatri posted a question in We ask and you answer! The best answer wins!
Q 714. How do Named Entity Recognition (NER) systems handle ambiguous terms, and what techniques can enhance their accuracy in real-world applications? Try running this through different large language models (LLMs) and share the varied responses as examples. Feel free to compare their outputs for added insights! Note for website visitors - This platform hosts two weekly questions, one on Tuesday and the other on Friday. All previous questions can be found here: https://www.benchmarksixsigma.com/forum/lean-six-sigma-business-excellence-questions/. To participate in the current question, please visit the forum homepage at https://www.benchmarksixsigma.com/forum/. The question will be open until Tuesday or Friday at 5 PM Indian Standard Time, depending on the launch day. Responses will not be visible until they are reviewed, and only non-plagiarised answers with less than 5-10% plagiarism will be approved. If you are unsure about plagiarism, please check your answer using a plagiarism checker tool such as https://smallseotools.com/plagiarism-checker/ before submitting. All correct answers shall be published, and the top-rated answer will be displayed first. The author will receive an honorable mention in our Business Excellence dictionary at https://www.benchmarksixsigma.com/forum/business-excellence-dictionary-glossary/ along with the related term. Some people seem to be using AI platforms to find forum answers. This is a risky approach as AI responses are error prone as our questions are application-oriented (they are never straightforward). Have a look at this funny example - https://www.benchmarksixsigma.com/forum/topic/39458-using-ai-to-respond-to-forum-questions/ We also use an AI content detector at https://quillbot.com/ai-content-detector. Only answers with less than 45-50% AI-generated content will be approved. -
The development of multi-agent AI systems, such as those utilizing platforms like crewai, autogen, and chatdev, represents a transformative approach to complex decision-making. These systems bring together multiple AI agents that work independently but collaborate to achieve shared goals. By combining the expertise of different agents, industries like healthcare, finance, and transportation are benefiting from automated decision-making and resource optimization. At the heart of this innovation is OpenAI’s Swarm, a framework designed to facilitate seamless coordination between agents. Unlike traditional systems that can be rigid, Swarm offers a lightweight and modular design, making it highly adaptable and accessible. This flexibility allows developers to experiment with various agent behaviors, creating sophisticated workflows for complex tasks. Multi-Agent Systems (MAS) enable autonomous agents to work together, leveraging AI's capacity for learning, data analysis, and decision-making. Each agent in a MAS operates independently but interacts with others to achieve a collective outcome. This leads to improved efficiency, enhanced decision-making, and greater adaptability in changing environments. Swarm, with its open-source framework, encourages collaboration and innovation, enabling developers to build complex, multi-agent orchestration systems. It allows for agent handoffs—the seamless transition of tasks between agents—while supporting the use of context variables for dynamic, personalized interactions. The benefits of such systems are clear in applications like personalized healthcare, efficient risk management, and optimized logistics. Swarm’s emphasis on simplicity and customization makes it a powerful tool for those exploring the next frontier of AI-driven collaboration.
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Nvidia has quietly launched its new large language model, NeMoTron 70B, which has outperformed OpenAI's GPT-4 on multiple benchmarks. This model, equipped with 70 billion parameters, demonstrates impressive capabilities in natural language understanding and generation, crushing GPT-4 in specific areas such as text coherence, reasoning, and task-solving efficiency. NeMoTron 70B is part of Nvidia's strategy to lead in AI by optimizing models for high-performance computing, leveraging their advanced hardware, including their powerful GPUs. The benchmarks used to evaluate NeMoTron 70B include complex tasks requiring nuanced reasoning and problem-solving, areas where it excelled compared to GPT-4. The model's success stems from Nvidia's integrated ecosystem, which combines hardware and AI models to create highly efficient and powerful solutions. This development signals significant competition in the AI space, especially as Nvidia continues to enhance both the hardware and the models that utilize it, making it a formidable contender against existing leaders like OpenAI. For further details, you can explore the complete article on MarkTechPost - https://www.marktechpost.com/2024/10/16/nvidia-ai-quietly-launches-nemotron-70b-crushing-openais-gpt-4-on-various-benchmarks/
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Shekhar Kirani from Accel discusses the potential of building AI applications on top of Large Language Models (LLMs), emphasizing India’s strength in leveraging proprietary data in services and SaaS industries. He highlights successful AI-driven startups like CareStack and Zenoti, which focus on specific sectors such as dental and wellness. Kirani notes India's opportunity in integrating AI with business needs, despite Western dominance in core LLM development. He also believes that Indian entrepreneurs can harness AI’s transformative power to enhance operational efficiency. For more details, read the full article here-https://www.business-standard.com/technology/tech-news/advantageous-to-build-new-ai-applications-on-top-of-llms-accel-s-kirani-124101600393_1.html. (Business Standard, 17th October, 2024)