Priyanshu Tariyal’s Post

AI and Qual Analysis: How much is it really scaling? AI has certainly changed the way we handle qualitative data. A lot of transcripts that once felt unmanageable can now be processed in hours. You no longer need to build exhaustive coding frames or Analysis plans. Just start asking questions. But here’s the catch: 1. AI often over-summarizes. The nuance, the edge cases, the contradictions the parts that actually spark insight get flattened. 2. Context blindness remains. Sarcasm, cultural nuance, implicit meaning still slip through the cracks. 3. With Generic purpose LLMs, there’s no traceability back to verbatims and researchers cant trust the output. The real opportunity is this: let machines crunch the haystack, but let humans make sense of the needles. #AI #MarketResearch #CustomerInsights #Innovation

AI hype is real, but simplicity wins often. People nowadays are jumping the gun towards AI, without even taking a 5 mins to think about: ->Do I really need AI to solve this? Or is the solution simpler and cheaper? I have seen some examples that can be solved with a simple key-word matching, but someone will jump to the famous line: ->Let's use AI. If you want to add the cherry on top, okay - use a simple AI API call to craft you a nice message after finishing the task you are trying to solve. What are some examples you've seen where AI was overkill and a simpler solution would’ve worked better? #ai #data #business

"Next token prediction" sounds technical, but it's reshaping our future. Do you know what it really means? 🧠 When an AI completes your sentence or generates content, it's not just statistics at work—it's a fundamental question about machine understanding. I just watched this insightful short video that explains the deeper implications of how language models work, beyond the buzzwords. It addresses the crucial question: Can AI truly understand human behavior and ideas, or is it all sophisticated pattern matching? This matters for anyone working with AI tools or making strategic decisions about implementing them in business contexts. Watch it now to gain clarity on what AI can and cannot do. Copy the link below to watch: https://lnkd.in/gzBstHmr #AIEthics #MachineLearning #BusinessTechnology

Not every Gen AI project fails because of the model. - Most fail because we miss the basics 👇 This week, I kept coming back to a simple truth: 🔑 Retrieval > Model. But that’s not the full story. The success of Gen AI systems often comes down to: * Search mechanism → right strategy for accurate retrieval * RAG evaluation → checking if outputs are actually grounded in facts * Guardrails → keeping responses safe, reliable, and aligned We can have the best LLM in the world, but without these, the system won’t deliver real value. My Takeaway: 👉 Before scaling, ask: Have we got retrieval, evaluation, and guardrails right? By the way, which part of Gen AI systems do you think is most overlooked and what’s the one thing you always check before deploying a Gen AI solution? #GenerativeAI #ArtificialIntelligence #RAG #AI #MachineLearning

🤖 Most LLMs sound smart. But are they really thinking—or just remembering? 🤔 The gap between memorization and reasoning is where AI’s true intelligence gets tested. ➡️ Memorization: spitting out facts and patterns it has seen before. ➡️ Reasoning: connecting ideas, solving new problems, and adapting on the fly. This distinction matters for everything from building reliable AI assistants to shaping the future of decision-making tools. If an AI can’t reason, it’s not much more than a clever search engine. 👉 Read the full blog here: https://lnkd.in/gv-ZZYSF #AI #MachineLearning #ArtificialIntelligence #LLMs #Reasoning #AIResearch #AITrends #TechInnovation

Where can AI deliver the most value? Start with the people closest to the service experience. In Liquid and The Mandarin’s survey, Let’s get real about AI, we took a customer-centric lens, asking public servants to reflect not only on internal frustrations but also on the barriers faced by the people they serve. The top five challenges clustered around service navigation, communication, responsiveness and fairness, common friction points that are widely understood and felt every day. These are real issues for service users, and clear opportunities for AI to make a meaningful difference. Ready to move forward with AI? Talk to us: https://lnkd.in/gPhNt6Gf Download the report: https://lnkd.in/gy3MZJh2

🚀Most AI models are incomplete, trained on snippets and summaries that miss the crucial "how." But what if your AI could know the full story? Models trained on full-text, expert-authored content deliver undeniable advantages: fewer factual errors, superior performance, and higher user trust. This is the power of a new approach: Model Context Protocol (MCP), which provides real-time access to authoritative sources. The architecture you choose today determines if you're building a simple tool or a trusted system. 📖 Read the full article - Building AI with Trust: The Expert Knowledge Advantage https://ow.ly/8ws650WVeIf #AI #MachineLearning #TechStrategy #DataScience

🚀Most AI models are incomplete, trained on snippets and summaries that miss the crucial "how." But what if your AI could know the full story? Models trained on full-text, expert-authored content deliver undeniable advantages: fewer factual errors, superior performance, and higher user trust. This is the power of a new approach: Model Context Protocol (MCP), which provides real-time access to authoritative sources. The architecture you choose today determines if you're building a simple tool or a trusted system. 📖 Read the full article - Building AI with Trust: The Expert Knowledge Advantage https://ow.ly/CiMk50WXkWX #AI #MachineLearning #TechStrategy #DataScience

🚀Most AI models are incomplete, trained on snippets and summaries that miss the crucial "how." But what if your AI could know the full story? Models trained on full-text, expert-authored content deliver undeniable advantages: fewer factual errors, superior performance, and higher user trust. This is the power of a new approach: Model Context Protocol (MCP), which provides real-time access to authoritative sources. The architecture you choose today determines if you're building a simple tool or a trusted system. 📖 Read the full article - Building AI with Trust: The Expert Knowledge Advantage https://ow.ly/wY1O50WVJfx #AI #MachineLearning #TechStrategy #DataScience

Explainable Knowledge Graph Retrieval-Augmented Generation (KG-RAG) with KG-SMILE Is AI’s increasing power coming at the cost of trust? 🤔 Generative AI is transforming industries, but the “black box” nature of models like LLMs is a serious concern, particularly in fields demanding accuracy – think healthcare, finance, and legal. Traditional RAG systems, while better, still lack transparency. That’s where KG-SMILE comes in. This groundbreaking research, detailed in a new arXiv paper (link in comments!), introduces a novel framework for *explainable* Knowledge Graph Retrieval-Augmented Generation. It uses controlled perturbations and weighted linear surrogates to pinpoint the most influential graph entities and relationships driving AI outputs. 🚀 The result? More stable, human-aligned explanations, boosting fidelity, faithfulness, and accuracy. KG-SMILE isn’t just about better results; it’s about building trust and understanding in AI. 💡 What are your thoughts on the importance of explainability in AI systems? Let’s discuss! 👇 #AI #ExplainableAI #KnowledgeGraph #RAG #MachineLearning #KG-SMILE Original article: https://lnkd.in/dWa8kbpc Automatically posted. Contact me if you want to know how it works :-)