Samsung Semiconductor’s Post

Enterprises are turning to Generative AI to build the next generation of their core platforms. Learn how a global enterprise enhanced their smart data platform with Generative AI capabilities as a proof of concept. Read the success story: https://lnkd.in/dRUZgEEm #GenerativeAI #GenAI #DigitalTransformation #CurrentlyOrion

Here is a nice case study on how Gen AI, using RAG techniques, enhanced the operations of an enterprise. Many enterprise platforms struggle with underutilized data or inertia due to factors like large databases, traceability issues, entity mapping challenges, and a lack of data experts. With the rise of Gen AI, we now have better methods that provide surprisingly fast, effective solutions for generating the right business intelligence and improved decision-making. Here are my additional thoughts: Solutions are always tailored to specific problems. There’s no one-size-fits-all approach. The techniques vary depending on needs like the level of customization, available data sources, and system complexity. The strategy should be based on these factors. Here are a few alternative approaches we can consider, if RAG is optional: - Embedding-Based Search with Pretrained Models: This is a relatively easy approach to implement, but it doesn’t offer the same capabilities as RAG. It works well when simple retrieval is enough and there’s no need for complex reasoning. - Knowledge Graphs with AI Integration: Best for situations where structured reasoning and relationships are key. It requires manual effort and can be tricky to integrate, but it offers powerful semantic search capabilities and supports reasoning tasks. - Fine-Tuned Language Models: This is ideal for stable, well-defined datasets where real-time data isn’t crucial. Since the data is straightforward, generating responses is easier. It performs well when the data is comprehensive but may struggle with queries outside the trained data. - Hybrid Models: A mix of retrieval and in-context learning. While it’s a bit more complex to implement, it delivers high accuracy and flexibility because it combines different techniques. Use this when you need high accuracy and rich content. - Multi-Modal Models: These models handle different types of data (eg., images, text) and provide combined insights. For example, they can retrieve images from documents and analyze them. However, they require solid infrastructure, which can get expensive. - Rule-Based Systems: These expert systems rely on predefined rules to generate responses. They’re great for regulated industries like finance & legal, as they offer transparency and auditability. However, they’re typically not scalable and may not handle unstructured data effectively. - End-to-End Neural Networks (for Q&A): These models are trained specifically for question-answering tasks. They perform well for defined tasks like Q&A and give concise answers without the need for complex pipelines. But they require large, annotated datasets and may underperform if there isn’t enough related data. Since this field is still evolving, it’s important to stay on the lookout for new or improved techniques based on the specific requirements

AI Connect, powered by NVIDIA, goes beyond sharing AI insights and news. What really drives me is bringing together a diverse community. Whether you’re a seasoned developer, an entrepreneur, or just starting to explore AI, this is your platform to connect, learn, and share experiences. AI Connect is for everyone interested in how AI Technology can innovate and improve our world. We’re here to facilitate connections that bridge industries and backgrounds, creating a network of professionals ready to explore the possibilities of AI together. Join us at our next event and stay tuned for more updates! #AIConnect #ArtificialIntelligence #CommunityBuilding #TechInnovation

We've been recognized as a 'Leader' in AI Services by the Forrester Wave report, Q2 2024. It's a privilege to be part of a company that's at the forefront of AI innovation, delivering value and steering business success for our clients. #ForresterWave #AIServices #DataAndAI #GetTheFutureYouWant Read more about the report and our AI journey!

Data Dependency: The Unsung Hero of Generative AI The success of generative AI doesn’t just depend on algorithms—it hinges on high-quality data. Yet, many enterprises grapple with the challenges of building the necessary data foundations for effective AI deployment. 📈 Without structured, accurate, and reliable data, even the most advanced AI systems can falter. So how do you overcome this roadblock? 📅 Learn more at our exclusive webinar: "Beyond the Hype: Real-World Impacts and Adoption Challenges of Gen AI," led by industry veteran Dr. Sridhar Srinivasan on Dec 3, 2024, at 7:00 PM IST. 🔗 Reserve your spot today:  https://lnkd.in/dxsVRayE #DataDependency #GenerativeAI #FutureOfAI #Predikly

Dataiku has secured the #6 position in Everest Group’s AI Top 50™ 2024! Over 7,000 AI technology providers have emerged in the past decade, according to the Everest Group. This recognition underscores our position as the only Universal AI Platform — empowering over 700+ global customers to build and deploy impactful and innovative AI solutions, from #ModernAnalytics to #GenAI. Read the full list: https://bit.ly/3Zi0uqf

So excited that Dataiku can be found on the #6th position in the Everest Group's AI Top 50™ 2024! 🌟 As Everest Group states themselves: "In today’s rapidly evolving technological landscape, it is increasingly vital to understand the dynamic AI landscape. With its groundbreaking innovation, AI continues to reshape industries, optimize processes, and redefine human-machine interactions." Find the full list here: https://lnkd.in/d2mDps_e #AI #top50 #valuation

Exciting news from Aquant! 🚀 We are thrilled to announce the release of several new product features. These new features reflect our focus on personalizing AI to better understand and address the complex challenges that service organizations in the manufacturing industry face. 🤖💡 Every service situation is unique, each with its own challenges and requirements. Generic AI models only offer recommendations based on the frequency of the solution in the data, which is only helpful with simple, common issues. Complex machinery requires AI that understands the context of each challenge. 🛠️🔧 Learn how Service Co-Pilot is reshaping the future of service organizations in the manufacturing sector. Check out the full press release here: https://hubs.la/Q02w8dX80 #Aquant #AI #Manufacturing #Innovation #ServiceSolutions

We realized fast that this isn't just about industry-specific or custom-built solutions—it's about creating an experience that truly understands our customer service business, with a unique angle on every asset, part, customer, and user. A heartfelt shoutout to our team for their passion and commitment to pushing the boundaries of what AI can do in service management. Check out our latest blog to learn more about how we're pioneering personalized AI solutions. #AI #FieldService #PersonalizedAI #Aqunat

Our latest blog article, "RAG Pipeline Diagram," provides a visual roadmap for understanding the retrieval-augmented generation process. This builds on our previous discussion about AI-ready data, where we emphasized the importance of quality data as the foundation for effective AI systems. In that earlier piece, we highlighted how well-structured data can enhance AI performance. Now, we take it a step further by illustrating how a well-designed pipeline can optimize that data for retrieval and generation, maximizing your AI's capabilities. Explore this new article to see how these concepts come together: https://lnkd.in/dPbWHZg5 Let’s advance our AI strategies together! #RAGPipeline #MultimodalAI