Georg Huettenegger

Businesses implementing #GenAI are discovering that off-the-shelf large language models can fall short of delivering accurate and effective professional-grade solutions. Consequently, there is a growing need for domain-specific genAI solutions. In this article for CIO Online, EXL's experts discuss how implementing domain-specific LLMs can cut costs, speed up processes, and ultimately yield a substantial ROI. Learn how your business can harness the power of domain-specific genAI solutions today!

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Large Language Models (#LLMs) and #GenerativeAI possess significant transformative potential for various industries but face challenges due to their lack of intrinsic business-specific knowledge. The Retrieval Augment Generation (#RAG) architecture offers a solution by equipping these #AI models with contextual knowledge, thereby enhancing their effectiveness within enterprise settings. Watch on-demand this #TechTalk session where Felix Liao, our Product Management Director APAC, will highlight how Denodo can be used to simplify and accelerate #RAG implementations. https://buff.ly/4cGVyys

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Punchy and salient piece by Dr. Shannon Vallor on what it means to be human - and how #AGI is not it. https://lnkd.in/edMYBDzr Defining #ArtificialGeneralIntelligence as outperforming humans at "most economically valuable work" is "a massive erasure of our human capacity and a reduction of ourselves that we should resist. Are you no more than the work you completed today? Are you any less human or less intelligent if you spent your waking hours doing things that do not have well-defined 'solutions,' that are not tasks that can be checked off a list, and that have no market price?" “Maybe the moral and experiential poverty of AI will bring the most vitally human dimensions of our native intelligence back to the center of our attention and foster a cultural reclamation and restoration of their long-depreciated value.” “What if, instead of replacing humane vocations in media, design and the arts with mindless mechanical remixers and regurgitators of culture like ChatGPT, we asked AI developers to help us with the most meaningless tasks in our lives?”

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Accenture’s new AI Refinery framework, powered by NVIDIA, launches today to enable clients to build custom LLM models with the Llama 3.1 collection of openly available models. The framework has four key elements to help enterprises adapt and customize prebuilt foundation models and deploy them to reflect their unique business needs: 1) Domain model customization and training: distill and refine prebuilt foundation models with customers’ own data and unique processes to drive reinvention and value powered by NVIDIA AI Foundry. 2) Switchboard platform: allows a user to select a combination of models to address the business context or based on factors, such as cost or accuracy. 3) Enterprise cognitive brain: scans and vectorizes all corporate data and knowledge into an enterprise-wide index to empower gen-AI machines. 4) Agentic architecture: enables AI systems to act autonomously—to reason, plan and propose tasks that can be executed responsibly with minimal human oversight. https://lnkd.in/gQyp_kaU #Accenture #AIRefinery #Framework #NVIDIA #Llama3.1

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The Stanford Human-Centered AI Report is out. This is the 7th year that Stanford has published this report and it is a must-read if you're trying to keep track of the developments in this field. https://lnkd.in/gwsHtzsq

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Can AI Evaluate AI Better Than Humans? The quest to make LLM-as-a-Judge a reliable, cost-effective alternative to human evaluation for text quality is evolving fast. But there's still a gap—human evaluators bring a nuance that current AI lacks. Enter RevisEval, a novel approach designed to narrow that gap by using response-adapted references. Here’s the big idea: Instead of using static references, RevisEval uses the text revision capabilities of large language models (LLMs) to dynamically revise responses and create highly relevant "adapted" references. This means that the reference itself changes to better reflect the response being evaluated, making the evaluation process more accurate. The results are impressive—RevisEval outperforms traditional evaluation methods, boosts metrics like BLEU and BERTScore, and even rivals human evaluation in certain scenarios. This could be a game-changer for AI evaluation—making assessments more dynamic, relevant, and accurate, while still being highly scalable. Interested in how this works and why it matters? Check out the details here: https://lnkd.in/gHhPUKSq #AI #MachineLearning #TextGeneration #LLMasJudge #RevisEval #NLP #ArtificialIntelligence #Innovation #LLM #NaturalLanguageProcessing #AIResearch #TechInnovation

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The key to building trust lies in ensuring transparency, high-quality data, and ethical AI practices. Organizations that embrace explainable AI will improve accountability and help avoid biased outcomes that can erode trust. Trust once lost takes years and huge efforts to recover. This was an interesting article featuring Praveen Thakur. https://lnkd.in/gJqUHT7a

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https://hackersearch.net/ is most useful to search in Hacker News. LLMs are making a big difference in search as expected. #llms #genai #generatieveai #capgemini #llm #opensourceai #capgeminiindia #ai #artificialintelligence #software #search

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I wrote an article for infoq with practical examples of using generative AI to accelerate: * Learning and discovery * Articulating and understanding requirements * Maintaining alignment with stakeholders I think the hype over autonomous LLM agents is missing the immediate benefits today for seasoned professionals “who can describe what they want the LLM to complete and critically evaluate the result. These tools enable us to swiftly cross divides of domain language and scale large repetitive tasks down to interesting ones on a human scale. When used carefully, they even facilitate work fundamentally about human interaction.”

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“AI is more than just automation—it’s about augmenting human decision-making and interactions through new insights and fostering richer collaboration within and among teams. Scaling AI solutions across different use cases and departments can be difficult, so grit and determination are essential. Ultimately, the most significant risk with AI is not using it at all.” #AIAdoption #Risk

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The CREATE AI Act (Creating Resources for Every American to Experiment with Artificial Intelligence Act), introduced in 2023, aims to establish a national AI research resource for the United States. Here are five key points from the bill: 1. National AI Research Resource (NAIRR) Creation 2. Support for AI Research and Development: 3. Equitable Access for Researchers 4. Focus on Privacy and Security 5. Interagency Coordination #responsibleai #aiforgood #aiforall #ethicalai

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Microsoft's AI model, Aurora, quickly predicts levels of six major air pollutants worldwide, including carbon monoxide and particulate matter. Read more in this article from Nature. #msftadvocate #ai https://lnkd.in/g-AppzdB

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Generative AI is not just a fleeting trend, but a transformative force reshaping industries, boosting efficiency, and enhancing human capabilities. Our colleagues Tim Moody, Head of Portfolio & Strategy, and Stephen Pye, Technical Consultant, shared inputs about how we can harness GenAI to transform business processes, enhance efficiency, and drive innovation. Read here: https://okt.to/msUnMh #GenAI #GenerativeAi #Transformation #HybridIT

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Generative AI (GenAI) can benefit from integrating knowledge graphs because these structures provide a rich, organized database of interconnected facts and relationships, which helps the AI understand context, reason over data, and generate more accurate, informed, and relevant outputs. Now there is a smarter way to build GenAI apps like #RAG with #Neo4j #GraphRAG (source: https://lnkd.in/gcrZG3Xy) . By combining knowledge graphs and vector search, GraphRAG infuses your AI with deep context and multi-hop reasoning for more accurate, relevant, and explainable results. Want to find out more? Watch this video below to grasp the core concepts and benefits of GraphRAG in less than 8 minutes: #knowledgegraph #graphdatabase #genai #llm #gql #query #datascience #vectorindex #vectordb #kbqa #kgqa #responsibleai #explainability https://lnkd.in/gETqH4ke

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