Mike Dillinger, PhD’s Post

https://lnkd.in/edevkNHg WARM DATA Just encountered the concept of "warm data" while listening to a Nate Hagens Roundtable (10th) Discussion see https://lnkd.in/eG5YiWMr Curiosity followed a link in the excellent show notes to: https://lnkd.in/edevkNHg Nora Bateson's "Warm Data Lab" So this post is just a nudge to any and all AI, ML, NN, DL, Big Data experts, practitioners. The vectors and matrices, that fragment your huge data sets to train your AIS (Artificial Intelligence Systems*) Keeping the context in which the data arises maybe as important, if not more important than extracting the fields, attributes and profit opportunities from the data, to see the "wood as well as the trees" The living ecology from which you extract you data to consume in pursuit of progress. Warm Data: "Information that does not take into account the full scope of interrelationality in a system is likely to inspire misguided decision-making, which compounds already “wicked” problems. Warm Data is not meant to replace or in any way diminish other data, but rather it is meant to keep data of certain sorts “warm” — with a nest of relations intact. — Nora Bateson Nora Bateson #Betterment *AIS leaving the S off this TLA seems to give mental prominence to "Intelligence" which seems to detracted from the fact that the AIS and the Global IT systems produce a huge carbon footprint and the AI Arms Race is adding to that every minute the race continues.

Could LLM-powered Unstructured Analytics (LUnA) replace RAG? While RAG works well for small behaved datasets, a simple RAG approach does not scale. RAG accuracy degrades quickly as you ask more complex questions, add more data, or work with more complex data. #LLM #RAG #LUnA #GenerativeAI #AI #MachineLearning

I'm interested in this approach because it addresses some of the rigidity and contextualization problems associated with retrieval augmented generation.

🌟 Day 16/90: Naive Bayes – A Simple Yet Effective Classifier Today, let’s explore Naive Bayes, a probabilistic algorithm that’s both simple and highly effective for certain types of data. 🔹 What is Naive Bayes? Naive Bayes is a probabilistic classifier based on Bayes’ Theorem. It assumes that all features in the data are independent of each other (the "naive" part), which simplifies the model's computation. How It Works: It calculates the probability of a class given the input features by multiplying the conditional probabilities of the features. Formula: P(Class|Data) = (P(Data|Class) * P(Class)) / P(Data) Types of Naive Bayes: -> Gaussian Naive Bayes: For continuous data assuming Gaussian (normal) distribution. -> Multinomial Naive Bayes: For count-based data (e.g., text classification). -> Bernoulli Naive Bayes: For binary data (e.g., features that are either true or false). Real-World Example: Sentiment analysis: Naive Bayes is often used for classifying text into categories like positive or negative sentiment based on word frequencies. 📌 Why Use Naive Bayes? It’s fast, computationally efficient, and works well for high-dimensional data like text or spam filtering. 👉 Question of the Day: Have you ever tried Naive Bayes for text classification or spam detection? What challenges did you face? Let’s discuss below! 💬 #Day16Of90 #NaiveBayes #MachineLearning #AIChallenge #ProbabilisticModel #DataScience #LearningTogether

Happy to share that my new Medium article, "Generating Domain-Specific Data using Evol-Instruct," is up! It explores innovative techniques for creating custom datasets tailored to specific domains. If you're into data science or curious about cutting-edge data generation methods and LLMs, this article is worth a read! Hope you find it interesting! Let me know your thoughts.

Check out the latest blog from our fellow Data Scientist and Data Surgeon Drew Lehe sharing his thoughts on Graph Data Science (GDS). Graph Data Science is a cutting-edge field helping solve many complex data problems. In this particular blog, Drew demonstrates how GDS helped improve the results of an entity resolution issue for an existing Data Surge client. Click the link below to learn more. At Data Surge innovation is a key piece of what we deliver. That innovation includes AI/ML, MLOps, and GenAI. We apply these innovations to support Network Analytics, Entity Resolution as well as Data Governance and Responsible AI. To learn more about Data Surge's services or book a demo of our solution accelerators, visit our website at www.datasurge.com. #AI #ML #GraphDataScience #GenAI #Data #Modernize #Democratize #Transform

You can store you data or... You can add intelligence to it 🧠 Check out the VAST Catalog on this upcoming Webinar. #VAST #DataCatalog #AI #Innovation Corey Reagan Christopher Ginder Dennis Sheehan

🚀 Unlocking the Power of Data Science! 🚀 In today’s fast-paced world, understanding the data science process and mastering essential tools is key to transforming raw data into actionable insights. Whether you're just starting or looking to refine your skills, my latest article dives deep into the simplified data science process, the collaborative roles involved, and the 15 must-know tools & technologies that drive innovation and decision-making. 💡 Ready to explore how data science can revolutionize your work? Check out the full article below! #DataScience #MachineLearning #DeepLearning #DataAnalytics #AI #Technology #Innovation #BigData

🚀 𝐄𝐱𝐩𝐥𝐨𝐫𝐢𝐧𝐠 𝐁𝐢𝐧𝐚𝐫𝐲 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧 𝐨𝐟 𝐏𝐨𝐢𝐬𝐨𝐧𝐨𝐮𝐬 𝐌𝐮𝐬𝐡𝐫𝐨𝐨𝐦𝐬 𝐰𝐢𝐭𝐡 𝐒𝐲𝐧𝐭𝐡𝐞𝐭𝐢𝐜 𝐃𝐚𝐭𝐚 🍄 In the world of data science, the ability to predict outcomes based on available data is key. Imagine applying this to something as crucial as identifying whether a mushroom is poisonous or safe to eat. But 𝒘𝒉𝒂𝒕 𝒊𝒇 𝒘𝒆 𝒅𝒐𝒏’𝒕 𝒉𝒂𝒗𝒆 𝒆𝒏𝒐𝒖𝒈𝒉 𝒓𝒆𝒂𝒍-𝒘𝒐𝒓𝒍𝒅 𝒅𝒂𝒕𝒂 𝒕𝒐 𝒕𝒓𝒂𝒊𝒏 𝒐𝒖𝒓 𝒎𝒐𝒅𝒆𝒍𝒔? This is where 𝒔𝒚𝒏𝒕𝒉𝒆𝒕𝒊𝒄𝒂𝒍𝒍𝒚-𝒈𝒆𝒏𝒆𝒓𝒂𝒕𝒆𝒅 𝒅𝒂𝒕𝒂𝒔𝒆𝒕𝒔 come into play. By generating artificial data that mimics various mushroom species, environmental conditions, and growth stages, we can create a robust dataset to train our binary prediction models. 𝘛𝘩𝘪𝘴 𝘢𝘱𝘱𝘳𝘰𝘢𝘤𝘩 𝘢𝘭𝘭𝘰𝘸𝘴 𝘶𝘴 𝘵𝘰 𝘱𝘳𝘦𝘱𝘢𝘳𝘦 𝘧𝘰𝘳 𝘳𝘢𝘳𝘦 𝘣𝘶𝘵 𝘤𝘳𝘪𝘵𝘪𝘤𝘢𝘭 𝘴𝘤𝘦𝘯𝘢𝘳𝘪𝘰𝘴—like identifying a dangerous mushroom type that isn't frequently encountered. By using synthetic data, we can enhance our predictive models, ensuring they're more accurate and reliable, even when real-world data is scarce. #DataScience #MachineLearning #SyntheticData #BinaryClassification

🔍 How Vector Databases Store Data: A Deep Dive into High-Dimensional Storage 🔍 Read detailed blog at: https://lnkd.in/g_NvjuUU Vector databases are changing the game in high-dimensional data storage, fueling AI applications such as NLP, recommendation systems, and image search. Unlike traditional databases, they work with numerical vectors that encapsulate unstructured data. Advanced structures like k-d trees, HNSW, and LSH are the backbone of vector databases, efficiently managing and organizing vectors. Techniques like quantization and approximate nearest neighbor (ANN) search ensure rapid retrieval, while compression and partitioning strategies further enhance storage and speed. For AI and ML experts, grasping how these databases store and index data is essential for developing scalable and high-performance applications. The future of smart data management is all about vectorization! 🌐 #AI #MachineLearning #DataStorage #VectorDatabases #Tech