Tim Clements has done an absolutely incredible job with this diagram. Questions: For those of us working in healthcare, the area under "Mathematical and statistical concepts" in the lower-middle-left section of the diagram, I'm wondering if you think that what Tim has listed is a good representation of what you use in healthcare analytics? Have you seen anyone provide a visual map of the math & stats domain for healthcare? If you were to build on what Tim has done, for healthcare, what would you add? Well done Tim!

Dive into the complex realm of AI governance with this insightful diagram that helps shed some light on the topic! Share your comments below! #ArtificialIntelligence #MachineLearning #GenAI #DataScience #GovernanceofAI #BigData #DataGovernance https://lnkd.in/etyDtHEz

The 7 Pain Points of GraphRAG GraphRAG comes with a lot of challenges. A summary by Jérémy Ravenel 1) Data Quality: Inconsistent data, outdated information, and biased datasets. 2) Retrieval Process: Irrelevant information retrieval, missing context, and information overload. 3) Graph Construction: Missing relationships, over-complex graph structure, and incorrect entity linking. 4) LLM Integration: Interfacing structured knowledge and LLMs don't prevent hallucinations, misinterpretation, and inconsistent reasoning. 5) Knowledge Gaps: How do you handle novel entities? 6) Scalability & Performance: Slow processing and high computational needs. 7) Ethical & Privacy Risks: Information exposure and bias amplification. #AI #LLM #GenAI #EmergingTech #DataScience #RAG Link in comments

🚀 New Project Alert: Spam SMS Detection! 🚀 I'm excited to announce my latest endeavor: building an AI model to classify SMS messages as spam or legitimate. This project aims to enhance communication security by filtering out unwanted and potentially harmful messages. 📲🛡️ Here's a snapshot of what I'm working on: Data Preparation: Utilizing datasets containing SMS messages labeled as spam or legitimate. Feature Extraction: Employing techniques like TF-IDF and word embeddings to convert text data into meaningful features. Model Building: Experimenting with classifiers such as Naive Bayes, Logistic Regression, and Support Vector Machines to accurately detect spam messages. Model Evaluation: Assessing model performance using metrics like precision, recall, accuracy, and F1 score, and optimizing for the best results. This project is an essential step towards creating a safer communication environment and reducing the impact of spam messages. Stay tuned for more updates and insights as I progress on this exciting journey! 🚀 Link : https://lnkd.in/ga84BQHw #Encryptix #MachineLearning #SpamDetection #DataScience #TextClassification #NaiveBayes #LogisticRegression #SupportVectorMachines #ArtificialIntelligence 

📣 Exciting News! 🚀🌟 I am thrilled to share our latest blog post on "Asynchronous Clustered Federated Learning with DAG-DLT Framework". 🎉📚 Federated Learning (FL) is revolutionizing the way we train global models while safeguarding client data privacy. 🌐🔒 However, FL encounters challenges due to the non-IID data distribution among clients. That's where Clustered FL (CFL) comes in! 🤝 In our new post, we introduce DAG-ACFL, an asynchronous clustered FL framework based on DAG-DLT. 📑🌐 We dive deep into its components and unveil a groundbreaking tip selection algorithm, which aggregates models from clients with similar distributions using cosine similarity of model parameters. 🔄💡 But that's not all! We take it a step further by showcasing an adaptive tip selection algorithm that dynamically determines the number of selected tips using change-point detection. 📊⚙️ To top it off, we provide comprehensive experimental results on multiple datasets, analyzing the communication and storage costs of DAG-ACFL. 📈 Curious to learn more? Dive into our blog post here: 👉 https://ift.tt/ArDvby1 Don't miss out on this cutting-edge technology that combines DAG-DLT with clustered FL to achieve robust, decentralized, and private model training with efficient performance. 🌟🔒 Read the full blog post on InstaDataHelp AI News: 📰🤖 ☞ Asynchronous Clustered Federated Learning with DAG-DLT Framework ☞ https://ift.tt/ArDvby1 Stay ahead of the game with InstaDataHelp AI News! 🔥💡 #FL #data #privacy #AI #technology #Innovation #DAGDLT #asynchronouslearning #CLF #blogpost #InstaDataHelpAI

“We built really great general purpose machines that talk like humans, but just like humans [who] are not experts, they’re generalists. Now, what we’re saying is that these general purpose machines need to become experts. But “expert" training data is usually not public; it’s proprietary, held close by corporations. Gretel’s platform can be used to anonymize such data for use in training models." - Ali Golshan, Co-founder and CEO, Gretel https://lnkd.in/dp_rsrmq #syntheticdata #datacentricAI

In a recent interview Meta's Joe Spisak mentioned, "A bridge to this area of high entropy" in relation to open-source large language models (LLMs) like Meta's Llama and frameworks like PyTorch. Here's what Claude has to say about it: 1. High entropy in AI/ML: In the context of artificial intelligence and machine learning, "high entropy" can be understood as an area of great complexity, uncertainty, and rapid change. The field of AI, especially concerning LLMs, is evolving at a breakneck pace with new discoveries, techniques, and applications emerging constantly. 2. Open-source as a bridge: By describing open-source projects like Llama and PyTorch as "bridges" to this high-entropy area, we're suggesting that these tools provide access and understanding to a complex and rapidly evolving field. They serve as: a) Entry points for researchers and developers b) Platforms for collaboration and knowledge sharing c) Foundations for further innovation 3. Democratizing AI: Open-sourcing powerful models and tools democratizes access to cutting-edge AI technology. This allows a wider range of individuals and organizations to participate in and contribute to the field, rather than limiting progress to a few well-resourced companies or institutions. 4. "A rising tide raises all vessels": This proverb suggests that improvements in general conditions will benefit all participants in a given ecosystem. In the context of open-source AI: a) As more powerful models and tools become freely available, the baseline for what's possible in AI rises for everyone. b) Researchers and developers can build upon existing work, accelerating progress across the field. c) The open exchange of ideas and code fosters a collaborative environment where advancements by one group can quickly benefit others. 5. Accelerating innovation: By providing open access to advanced models and frameworks, projects like Llama and PyTorch enable: a) Faster iteration and experimentation b) More diverse applications of AI technology c) Increased opportunity for breakthroughs from unexpected sources In conclusion, describing open-source AI projects as "bridges to areas of high entropy" highlights their role in making complex, rapidly evolving technology more accessible and understandable. This accessibility, in turn, contributes to the "rising tide" effect, where advancements in the field benefit a broad range of participants and accelerate overall progress in AI research and development. https://lnkd.in/gKtgZDdD

I had to bookmark this post and have it next to the 12 RAG pain points and proposed solutions article by Wenqi Glantz, that I have been reading for a while now. I believe the identified pain points, for both the RAG & GraphRAG, are likely to inform the process of underwriting product liability insurance covers for RAG-powered systems. The likelihood of failure or error rate could be evaluated for each pain point and the pain points counted among the risk factors. I am thinking of how a claim is triggered in parametric insurance covers and how that approach could be considered for such insurance products. https://lnkd.in/dR2-KpkG #ai #insurance #underwriting #RAG #AIriskmanagement #liabilityinsurance

Tonight: Attend a LIVE 🔥🔥🔥(side) chat with Voltron Data's CEO, Joshua Patterson! 🚀 "Accelerating AI and Analytics: The Future of Data Processing" 📅 Date & Time: Thursday, June 27 | 10:00 PM - 11:00 PM EDT Join Hugo Bowne-Anderson and Outerbounds this evening for a conversation exploring the future of data processing. Our CEO, Josh Patterson, will cover how technologies like Arrow are transforming data analytics and machine learning strategies for cybersecurity use cases. 🔗 Don't miss out! RSVP now! https://lnkd.in/eXdUBPeP 🎙️ Discussion Topics: - Evolution of open standards like Apache Arrow and Ibis. - Overcoming challenges with legacy APIs in data processing. - Importance of performance and quick feature engineering with large language models. - Cybersecurity challenges in machine learning across diverse data frameworks. - Overview of Voltron Data’s innovative open-source projects. 👥 Perfect for data scientists, ML engineers, and data leaders looking to navigate the complexities of modern data processing. Tune in to reshape your approach to analytics and AI! #OpenSource #MachineLearning #AI #CyberSecurity #DataAnalytics #DataScience #DataProcessing