Jiun Kim

We're facing more and more questions about the use of synthetic data in market research. I'd recommend this blog post, which really resonated with me. So much of the conversation is "how closely can this replicate human data?" But there are so many other considerations researchers should be thinking about. https://lnkd.in/eSQfSTPj

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"We can just plug in the historical data right?" My head: "No, because it's captured differently with different segmentation, at a detail level we don't support through two different systems with inconsistent naming conventions and saved in CSVs on someones hard drive but we don't remember who it is, except for the weeks they went on PTO which are just missing and can't be replaced." My mouth: "We'll look into it."

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Dr. Ramla Jarrar, an MMM expert, shares an important perspective every marketer using MMM should read. The customer journey is complex, and while MMM is a powerful tool for understanding how spend impacts that journey, its limitations are often overlooked and nuances brushed over once results are packaged in polished decks. The tension I often see: marketers need one strong KPI to track large marketing investments, but the complexity of measurement means no single framework provides that holy grail metric. Measurement methodologies are sophisticated and technically robust, yet each has constraints. Triangulating across multiple frameworks—MMM, incrementality testing, attribution, brand tracking—provides better insights, though interpretation remains as much art as science. This requires partnership. Marketers need to understand what each methodology can and cannot deliver. Analytics partners, whether internal teams or external consultancies, have the responsibility to explain assumptions, acknowledge limitations, and help interpret results responsibly. When both sides engage with this complexity honestly, we make better decisions. A question for marketers: where do you find the biggest gaps between marketing performance measurement and the questions you need answered? #MarketingMixModeling #MMM #MarketingAnalytics #MarketingMeasurement #MarketingROI #DataDrivenMarketing #MarketingStrategy

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https://lnkd.in/gMiv7ZQB Special thanks and sincere appreciation to Vijay Mehrotra and Kara Tucker for publishing a highly favorable review of Why Data Science Projects Fail: The Harsh Realities of Implementing AI and Analytics, Without the Hype, the new book that I co-authored with Evan Shellshear, and my new book The Art of Data Science: A Practitioner's Guide in INFORMS Analytics online magazine. Both of these books provide invaluable insights delivered in the context of real-world case studies in the form of lessons learned, through both failure and success, best practices, and practical tools to increase Analytics, Data Science, and AI project success rates. As the AI hype train risks de-railing due to a dearth of wide spread, significant, tangible business value and economic impact delivery, now is the time to understand the fundamental elements of risk that so often jeopardize these projects and initiatives. Contrary to the instincts of many leaders and practitioners, the primary challenges lie not with the AI technology or mathematics--- but rather "human factors" and "business factors" are the most salient determinants of success and failure. Change management, a focus on the underlying business problems & processes, prioritizing and selecting projects based on the most relevant business problems and opportunities and their commensurate business value potential, overcoming data issues, committed leadership, a culture of getting and doing better, and delivering models from the "sandbox" into production systems, all play a meaningful and vital part in achieving success, and sidestepping failure.

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Quantization in Large Language Models (LLMs) As LLMs scale, intelligence is no longer the bottleneck — memory, latency, and cost are. Quantization turns out to be one of the most powerful (and misunderstood) techniques enabling real-world deployment of large models. In this blog, I break down: • Why LLMs are surprisingly quantization-friendly • INT8 vs INT4 vs NF4 — what actually works in practice • How modern architectures absorb low-bit noise • Where quantization fails and how to design around it Quantization is no longer an optimization step — it’s foundational infrastructure for production LLM systems. Link: https://lnkd.in/gqGje75G #LLMs #AIEngineering #Quantization #MachineLearning #GenAI #MLOps

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The Quiet Shift Redefining AI Roadmaps NVIDIA’s latest research on SLM Agents reveals an underappreciated truth: the AI landscape is undergoing a fundamental transformation that most roadmaps haven’t accounted for yet. 𝗪𝗵𝗮𝘁’𝘀 𝗥𝗲𝗮𝗹𝗹𝘆 𝗖𝗵𝗮𝗻𝗴𝗶𝗻𝗴 1️⃣ Small > Big - The race to ever-larger models is giving way to optimized ensembles of specialized SLMs (Small Language Models). Why? Because real-world applications demand: 10-100x lower inference costs Sub-second latency for customer-facing applications 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 𝗼𝗻 𝗲𝗱𝗴𝗲 𝗱𝗲𝘃𝗶𝗰𝗲𝘀 2️⃣ Agents, Not Monoliths - Tomorrow’s AI won’t be a single LLM trying to do everything, but coordinated teams of models where: Each handles what it does best (reasoning, retrieval, validation) Components can be independently updated/swapped 𝗙𝗮𝗶𝗹𝘂𝗿𝗲𝘀 𝗮𝗿𝗲 𝗶𝘀𝗼𝗹𝗮𝘁𝗲𝗱 𝗮𝗻𝗱 𝗿𝗲𝗰𝗼𝘃𝗲𝗿𝗮𝗯𝗹𝗲 3️⃣ The End of the "One Model" Fantasy - The industry is quietly moving beyond the myth that a single LLM can optimally handle all use cases. Hybrid architectures (SLMs + symbolic systems + RL) are proving more robust for: Compliance-heavy industries Real-time operational systems Mission-critical applications Strategic Implications for Leaders 1. 𝘊𝘰𝘴𝘵 𝘌𝘧𝘧𝘪𝘤𝘪𝘦𝘯𝘤𝘺 𝘢𝘵 𝘚𝘤𝘢𝘭𝘦 SLMs reduce inference costs from dollars to pennies per query Enable profitable deployment in high-volume applications (e.g., customer support, transaction monitoring) Change cloud spend from capital drain to competitive advantage 2. 𝘜𝘯𝘮𝘢𝘵𝘤𝘩𝘦𝘥 𝘈𝘥𝘢𝘱𝘵𝘢𝘣𝘪𝘭𝘪𝘵𝘺 Swap compliance modules for new regulations in days, not months Fine-tune customer-facing models to brand voice without full retraining Incorporate live data (inventory, market feeds) through specialized agents 3. 𝘉𝘶𝘪𝘭𝘵-𝘪𝘯 𝘙𝘦𝘴𝘪𝘭𝘪𝘦𝘯𝘤𝘦 No single point of failure (outages affect components, not entire systems) Clear audit trails for regulated decision-making Avoid vendor lock-in through modular architecture The Bottom Line: This isn’t just another technical improvement - it’s a fundamental shift in how we architect AI solutions. Companies that recognize this early will gain: ✔️ Faster time-to-production ✔️ Better unit economics ✔️ Future-proof systems The question isn’t if this will impact your AI strategy - it’s when you’ll need to adapt. 🔗 Dive deeper: SLM Agents Research Are your AI plans accounting for this shift? Let’s discuss in the comments. #AIStrategy #TechLeadership #BusinessTransformation #ArtificialIntelligence #DigitalInnovation For those of you, who has read so far 😏 𝙸̲𝚗̲𝚜̲𝚒̲𝚐̲𝚑̲𝚝̲𝚏̲𝚞̲𝚕̲ ?? Now what if i told you this post was written by a bunch of agents 🤖 !!! who picked, curated, formated and critiqued countless time, before bringing this to me.. felt like editor in chief of Daily Planet.. just that 🦸🏻‍♂️clark is powered by Gemini than the Sun  #crewai #AgentsEveryWhere

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Excited to share a little side project I've been tinkering with: statlingua! 🗣️ For those of us who spend a good chunk of our time translating statistical concepts into plain English (whether it's for stakeholders, collaborators, or even just our future selves!), I thought it would be neat to have a tool that could help automate some of that. statlingua is a pretty simple R package that aims to generate natural language descriptions of common statistical models and results. Think of it as a way to get a first draft of an explanation for things like regression coefficients, model summaries, and maybe even some basic visualizations down the line! It's still very much in development, so expect rough edges, potential inaccuracies, and a healthy dose of "coming soon" features. However, I'm putting it out there in the open in case anyone else finds the idea intriguing or wants to contribute. You can check out the (very basic!) code and contribute at https://lnkd.in/g3py6d6f. #rstats #datascience #statistics #opensource #genai #LLM

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