Inteqral Inc.’s Post

Knowledge graphs are the way for making your products and services aligned with AI economy. Actions you can take now: 1. Build your public dataset for AI bots to learn about your business, products and services. 2. Ensure they(AI) learn the ways to work with your business. Newer generation is not going to come again asking if you are not transparent in the first place. So be open and transparent. Hiding and layering is not going to work. 3. Keep feeding the AI bots with knowledge graphs, as that is an easy way to learn about your services without retraining. 4. Make your K-graphs available to search engines for newer SEO models where AI friendliness takes higher seat. 5. Ensure that your K-graphs are complete and updated at regular basis. Drive your internal processes to be run with K-graphs so that you can automate many of the old processes arounf documentation and learnings with K-graphs. Learn more at cyberium.info

Can we automatically find the best clustering algorithm and appropriate values for its parameters for a given dataset? AutoML aims to provide an answer to this question. Interested to find out how? Check out our survey. #automl #machinelearning #clustering in the context of EU-funded projects: MobiSpaces and Green.DAT.AI in the Department of Digital Systems

Automated Machine Learning Explore the future of data science with Arus, leveraging Automated Machine Learning. Unleash efficiency and accuracy in model creation, optimization, and deployment. Elevate your analytics effortlessly with Arus. https://lnkd.in/gKTK84d8 #Arus #AutomatedMachineLearning

RouteLLM is such an incredible concept for the trade-off between model performance and computational costs 💰 This approach allows for the efficient use of resources by reserving powerful models for challenging tasks while routing simpler queries to more economical options (with 2X or more cost saving as claimed in this paper) 🗞 Paper "RouteLLM: Learning to Route LLMs with Preference Data". arxiv.org/abs/2406.18665 One of the key innovations of RouteLLM is its use of human preference data for training the router. The researchers leveraged data from the Chatbot Arena, a platform where users compare responses from different LLMs, to create a rich dataset of human preferences. This data provides valuable insights into the relative strengths and weaknesses of various models across different types of queries. The RouteLLM framework employs several sophisticated routing techniques: 1. Similarity-weighted (SW) ranking 2. Matrix factorization 3. BERT-based classifier 4. Causal LLM classifier Interestingly, the RouteLLM approach demonstrates strong transfer learning capabilities. The routers maintained their performance even when the underlying strong and weak models were changed at test time, suggesting a robust and generalizable solution for LLM deployment.

Dive into the art of content extraction. From sifting through data to shedding excess! It's a dance of precision. Watch as machine learning unveils the gems, distilling raw information into actionable wisdom. Let's decode the secrets hidden within the bytes. 𝐅𝐨𝐥𝐥𝐨𝐰 𝐮𝐬 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞 𝐮𝐩𝐝𝐚𝐭𝐞𝐬. 𝐂𝐨𝐧𝐭𝐚𝐜𝐭 𝐮𝐬 𝐭𝐨𝐝𝐚𝐲 𝐚𝐧𝐝 𝐥𝐞𝐭'𝐬 𝐰𝐨𝐫𝐤 𝐭𝐨𝐠𝐞𝐭𝐡𝐞𝐫 𝐭𝐨 𝐦𝐚𝐤𝐞 𝐲𝐨𝐮𝐫 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐀𝐈-𝐥𝐞𝐝. 📲 +1-307-828-1095 / +91- 9341171960 📍C-1101, 11𝐭𝐡 𝐅𝐥𝐨𝐨𝐫, 𝐔𝐫𝐛𝐭𝐞𝐜𝐡 𝐓𝐫𝐚𝐝𝐞 𝐂𝐞𝐧𝐭𝐫𝐞, 𝐒𝐞𝐜 𝟏𝟑𝟐, 𝐍𝐨𝐢𝐝𝐚. . . . #expeditext #technology #business #AIPower #exploretheworld #artificialintelligence #FutureIsNow #updates #ExpediText #FutureOfTech #artificialintelligencetechnology #skilldevelopment

🔍 Just read a fascinating article about how GraphRAG (Graph-enhanced Retrieval-Augmented Generation) is set to revolutionize the accuracy of Large Language Models! Here are the top benefits of GraphRAG, along with reasons why it’s revolutionary: 1. Enhanced LLM responses Technique: Combining knowledge graphs with RAG  ↳ How it Helps: Provides more contextually relevant answers. 2. Handles complex queries Technique: Multi-hop reasoning  ↳ Excels at understanding and responding to intricate questions. 3. Boosts accuracy up to 3x Technique: Integrating structured and unstructured data  ↳ Improves decision-making across various business questions. 4. Reduces AI hallucinations Technique: Grounding responses in factual data  ↳ Ensures more reliable and accurate information retrieval. 5. Efficient token usage Technique: Optimized response generation  ↳ Requires 26-97% fewer tokens, saving resources. 6. Transforms enterprise decision-making Technique: Leveraging hidden relationships  ↳ Generates more accurate insights for healthcare, finance, and supply chain management. GraphRAG isn’t just a minor upgrade. What excites me most is how this could transform enterprise decision-making. By combining knowledge graphs with RAG, organizations can finally make sense of their vast data landscapes and uncover hidden relationships that drive better outcomes. 💡 For those in tech leadership: This could be especially game-changing for industries requiring high accuracy, like healthcare and finance. #ArtificialIntelligence #GraphRAG #MachineLearning #Innovation #DataScience #Enterprise #LLM #KnowledgeGraphs #RAG

As the Machine Learning field expands, several algorithms have gained prominence, each distinct in approach and use. With industries increasingly adopting machine learning, knowledge and application of these algorithms are key to innovating and staying competitive in the digital era. Here are some of the most utilized Machine Learning Algorithms currently: . . . #TriSeedSoftware #MachineLearningMagic #TechInnovation #DataScience #AIRevolution #FutureIsNow #PredictiveAnalytics #AIForAll #DigitalTransformation

Interested in Automated Machine Learning? Check out Automated Machine Learning: Methods, Systems, Challenges! 👉 https://lnkd.in/g-hSajp2 An insightful resource that's already proving its value. #AutoML #MachineLearning #DataScience

Just wrapped up a pretty deep dive into optimizing the Data Fetch and Hyperparameter Tuning components of my TradingRobotPlug project... #DataProcessing has been smooth after adding advanced techniques for handling missing values, scaling, and feature engineering. I’m now able to automate a lot of the grunt work that goes into prepping stock data for model training... and the new DataHandler class is doing wonders for that. One of the big wins today was getting the fetch_and_preprocess_data method working seamlessly, even when dealing with those tricky date columns... now all data loading, scaling, and imputation is streamlined. Plus, I tightened up the hyperparameter optimization process by integrating Optuna to suggest and evaluate the best parameters for my models. So, what's next? I need to: Focus on enhancing the model training process—likely with more complex neural networks or deep learning models. Keep refining the real-time data fetchers, ensuring I can rely on the data quality across APIs, especially since IEX Cloud is no longer a thing. Finally, start documenting some of the more proprietary pieces of this work... #FinancialTechnology is moving fast, and I want to lock down the innovations in risk management and backtesting. It’s all coming together, and the end goal is clearer than ever... but the road’s long, and there’s more to build. #TradingAutomation #DataScience #MachineLearning #StockTrading #AlgoTrading

This is a great and practical write up how to use Ontotext GraphDB with RAG retriever.