Ken Priyadarshi

👩‍🎨 The Era of Everyday AI: Navigating the New Prompt/Response Structure 🤖 😕 As enterprises implement Generative AI enabled solutions, a new challenge has surfaced: helping employees adapt to the unfamiliar prompt/response structure that these applications require. Unlike traditional software with visual interfaces, interacting with large language models (LLMs) demands a new way of thinking and working. Key Challenges: ➡ Adapting to a New Workstyle: Employees are used to working with specific applications like documents, spreadsheets, or databases. However, GenAI solutions require a shift in mindset, as they gather and generate content from multiple sources based on user prompts. This transition has proven difficult for many, leading to frustration and reduced productivity. ➡ Crafting Effective Prompts: The success of GenAI applications heavily depends on the quality of the prompts users provide. Simple or poorly structured prompts often result in generic or unhelpful responses, causing dissatisfaction among early adopters. How to Improve Adoption: 📗 Training & Best Practices: Enterprises that invest in formal training and create channels for sharing best practices are seeing better outcomes. Setting up dedicated spaces in collaboration tools like #Teams or #Slack allows employees to exchange tips and quickly learn how to use GenAI tools effectively. 💻 Structured Dialogue Interfaces: To ease the transition, some solutions are incorporating guided dialogue interfaces that help users formulate better prompts. This approach not only improves the quality of output but also serves as an educational tool, teaching employees how to craft more detailed and effective prompts over time. The difference between success and frustration with GenAI solutions often comes down to preparation. Enterprises that prioritise training and create supportive environments for learning will see stronger adoption and better productivity gains. As the technology evolves, expect to see more structured and user-friendly interfaces that bridge the gap between traditional workstyles and GenAI’s new demands. ➡ P.S. If you found this post helpful, please like, comment, or repost to share with others. Credit: Gartner

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👩‍🎨 The Era of Everyday AI: Navigating the New Prompt/Response Structure 🤖 😕 As enterprises implement Generative AI enabled solutions, a new challenge has surfaced: helping employees adapt to the unfamiliar prompt/response structure that these applications require. Unlike traditional software with visual interfaces, interacting with large language models (LLMs) demands a new way of thinking and working. Key Challenges: ➡ Adapting to a New Workstyle: Employees are used to working with specific applications like documents, spreadsheets, or databases. However, GenAI solutions require a shift in mindset, as they gather and generate content from multiple sources based on user prompts. This transition has proven difficult for many, leading to frustration and reduced productivity. ➡ Crafting Effective Prompts: The success of GenAI applications heavily depends on the quality of the prompts users provide. Simple or poorly structured prompts often result in generic or unhelpful responses, causing dissatisfaction among early adopters. How to Improve Adoption: 📗 Training & Best Practices: Enterprises that invest in formal training and create channels for sharing best practices are seeing better outcomes. Setting up dedicated spaces in collaboration tools like #Teams or #Slack allows employees to exchange tips and quickly learn how to use GenAI tools effectively. 💻 Structured Dialogue Interfaces: To ease the transition, some solutions are incorporating guided dialogue interfaces that help users formulate better prompts. This approach not only improves the quality of output but also serves as an educational tool, teaching employees how to craft more detailed and effective prompts over time. The difference between success and frustration with GenAI solutions often comes down to preparation. Enterprises that prioritise training and create supportive environments for learning will see stronger adoption and better productivity gains. As the technology evolves, expect to see more structured and user-friendly interfaces that bridge the gap between traditional workstyles and GenAI’s new demands. ➡ P.S. If you found this post helpful, please like, comment, or repost to share with others. Credit: Gartner

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#SciAgents: Revolutionizing Scientific Research Explore the future of science with MIT's SciAgents—a cutting-edge framework that automates research using AI-driven graph reasoning. This breakthrough approach has significant implications for business leaders and tech enthusiasts. By utilizing large-scale knowledge graphs, SciAgents organizes and links extensive scientific concepts, identifying complex patterns and unseen relationships within vast data sets. The use of large language models (LLMs) aids in forming and refining research hypotheses, ensuring they align with current knowledge. What makes SciAgents unique is its multi-agent system, where agents with specialized roles work together dynamically to advance scientific inquiry. This innovative method builds comprehensive ontologies and uncovers hidden connections across diverse scientific areas. With the potential to speed up discoveries in fields like materials science and biotechnology, SciAgents showcases AI's ability to transform R&D processes for businesses. The system's efficiency and scalability can drive innovation, foster interdisciplinary collaborations, and reveal new market opportunities. For more insights into AI's impact on research and discovery, delve into the book "AI and the Lost Work" [here](https://shorturl.at/imljn). #ArtificialIntelligence #Innovation #ResearchRevolution 😃💡🔬📚

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 Broadcom's AI-driven impressive growth, #BlinqIO's #AI revolution in software testing, and the United Nations report on human rights in the context of #generativeAI. Vertex Inc. acquires AI tax capabilities, Amazon Web Services (AWS) symposium highlights AI's impact on finance, MongoDB uses AI in fraud detection, and Automation Anywhere's new AI plus Automation platform: https://lnkd.in/e_RSXmPS

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#HarvardBusinessReview discusses the practical steps and strategies for deploying and scaling #GenAI efficiently and cost-effectively 🦾🤖 K͟e͟y͟ ͟p͟o͟i͟n͟t͟s͟ ͟i͟n͟c͟l͟u͟d͟e͟: ✅️ #Infrastructure Building robust and scalable infrastructure to support #AIworkloads. ✅️ #Security Ensuring the secure deployment of #AIsystems to protect sensitive information. ✅️ #CostManagement Balancing innovation with cost-effective solutions to maximize #ROI. ✅️ 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 #Learning Adapting and evolving #AI systems through continuous learning and improvement. Only a small percentage of companies are currently using #GenerativeAI at scale, representing a significant opportunity for businesses to leverage this technology for competitive advantage 🚀🚀 #TechInnovation #AIinnovation

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When building custom hashtag #LLM or hashtag #SLM models in the hashtag #GenerativeAI hashtag #Lab, it can now automatically generate tests hashtag #Bias, hashtag #Fairness, hashtag #Robustness, and other hashtag #ResponsibleAI categories, Tests can then be run, edited, shared, and reused across projects. https://lnkd.in/g5y3aqbg hashtag #ai hashtag #datascience hashtag #ethicalai hashtag #largelanguagemodels

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How does an enterprise boost their confidence in Generative AI? 🤔💡 As enterprises move from experimentation to production with genAI, a crucial question looms: What if something goes wrong? The fear of AI hallucinations and reliability issues is real, but innovative solutions are emerging to address these concerns. 🚀 Patronus AI, a San Francisco-based startup is making waves in the AI evaluation space. They've just closed a $17 million Series A funding round, led by Notable Capital and joined by Datadog Inc., Lightspeed Venture Partners, and Factorial Capital. 💰 But why is this significant for you and your business? Their platform promises to: 1️⃣ Detect and mitigate hallucinations 2️⃣ Test LLM reliability with adversarial prompts 3️⃣ Provide comprehensive evaluation tools for model performance Patronus AI recently unveiled Lynx, an open-source model for detecting and mitigating hallucinations in LLMs. It addresses one of the most significant challenges in AI adoption -> ensuring the reliability and safety of LLMs. Few highlights: ▪ Outperforms industry giants: Lynx surpasses GPT-4o, Claude-3-Sonnet, and other closed and open-source models in hallucination detection tasks. ▪ Impressive accuracy: Lynx (70B) achieved 87.4% overall accuracy on HaluBench, outperforming GPT-4 by 8.3% in detecting medical inaccuracies! ▪ Two Powerful Versions: Lynx comes in 70B and 8B parameter versions, catering to different computational needs while maintaining high performance. ▪ Innovative Approach: Lynx employs a novel method of generating hard-to-detect hallucination examples using semantic perturbations. ▪ Introducing HaluBench: A comprehensive hallucination evaluation benchmark with 15k samples across real-world domains, including finance and medicine. ▪ Open-source availability: Both Lynx and HaluBench are freely accessible on HuggingFace. As enterprises increasingly rely on LLMs and RAG systems, tools like Lynx could be crucial in building confidence and accelerating AI integration into critical business processes. #AI #EnterpriseAI #OpenSource #TechInnovation Read the full story here: https://lnkd.in/dAg_dN5c

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