Artificial Intelligence

• AI Ethics: Bias, Fairness, and Algorithmic Accountability
  Bias detection and mitigation: crucial.
• AI-Driven Automation: Sectoral Transformations Underway
  AI automation is reshaping industries; efficiency gains.
• AI-Driven Industry Transformation: Healthcare & Finance
  Healthcare & Finance: AI Transformation.
• AI-Powered Cybersecurity: Threat Detection and Automation
  AI boosts threat detection & automates responses.
• AI-Powered Knowledge Augmentation: Trends and Applications
  AI boosts knowledge: trends and uses.
• AI, Machine and Deep Learning, Blockchain
  Discover Dalton Bly, your source for clear, practical insights on AI, machine learning, and blockchain technologies—without the buzzwords.
• AI’s Trajectory: Emerging Trends and Societal Impact
  AI: Deep Learning & Ethical Considerations.
• Applying topology concepts to understand connectivity patterns between different features in complex datasets.
  Topology reveals hidden connectivity in complex datasets.
• Auto-generation of WordPress post drafts with Qdrant retrieval and LLM writing
  Leveraging Qdrant’s vector search capabilities, we auto-generate WordPress post drafts using powerful Language Model writing assistance for efficient content creation.
• Building Explainable AI Pipelines with Neo4j and LLMs
  Leverage the power of graph databases and large language models to create transparent, interpretable AI solutions using Neo4j and LLMs.
• Color theory applications in data visualization to represent multiple dimensions beyond traditional 3D graphs.
  Applying color theory to visualize multidimensional data.
• Comparing Vector-Based Reasoning to Rule-Based Knowledge Systems
  Vector-based reasoning leverages distributed representations to capture nuanced knowledge, contrasting with rule-based systems’ explicit if-then logic.
• Data Foundation of AI: Quality, Quantity, and Preparation
  Data quality, quantity, & preparation: AI’s core.
• Deploy a Personal AI Chatbot With Secure API Gateway
  Secure Chatbot API Gateway Deployment.
• Deploying Hybrid LLM+Graph Systems for Intelligent Question Answering
  Leveraging large language models (LLMs) combined with graph databases enables sophisticated question-answering systems, offering enhanced accuracy and context-aware responses.
• Dimension reduction techniques for preserving insights when projecting high-dimensional business data onto simpler visualizations.
  Dimension Reduction: Preserving Insights in Visualization
• Dockerized TensorFlow Lite for Edge AI on Personal Devices
  Dockerized TFLite for Edge AI.
• Document classification using LocalAI for embeddings and Ollama for categorization
  Leverage LocalAI embeddings to enhance document classification accuracy when combined with Ollama’s powerful categorization capabilities.
• Embedding Structured Business Data for Enhanced Semantic Search
  Leverage rich schemas to optimize content, enabling more precise search results through semantic understanding.
• Encrypt All Model Inputs and Outputs Using PySyft in Docker
  Secure model data with PySyft in Docker.
• Enriching Tabular Data with AI-Generated Text for Vector Embedding
  Enhance tabular data with AI-generated text to create rich vector embeddings, unlocking new insights and predictive power.
• Evaluating Graph Databases for Real-Time Relationship Inference
  Graph databases enable rapid relationship inference, crucial for real-time applications. Evaluation is key to selecting optimal solutions.
• Exploring tensor mathematics for representing multi-dimensional data structures in business intelligence applications.
  Tensor mathematics enhances BI multi-dimensional analysis.
• Federated Learning With Flower Framework in Docker
  Flower framework: Federated learning in Docker.
• Fine-Tune a HuggingFace Model With Dockerized PyTorch
  Dockerized PyTorch for HuggingFace fine-tuning.
• Foundational AI: Unpacking Core Concepts & Definitions
  Foundational AI: Core Concepts Defined.
• Geometric transformations as a framework for understanding organizational change from current state to future vision.
  Using geometric transformations to model change dynamics
• Human-AI Synergy: Privacy and Knowledge Enhancement
  Human-AI Synergy: Data privacy and knowledge gains.
• Hybrid Semantic Search with Vector Databases and Knowledge Graphs
  Leveraging vector databases and knowledge graphs enhances semantic search, enabling precise query processing across vast datasets.
• Identifying Ideal Customers Using Vector Similarity and Support Scoring
  Leverage vector similarity and support scoring to pinpoint precise customer segments for targeted marketing strategies.
• Intent detection and response generation using two cooperating LLMs
  This paper explores the synergy between two Large Language Models (LLMs) to enhance intent detection and response generation capabilities.
• Leveraging LangChain to Bridge Knowledge Graphs and Vector Stores
  LangChain’s modular architecture enables seamless integration of knowledge graphs with vector stores, unlocking powerful multi-modal AI solutions.
• Leveraging vector spaces to create meaningful distance metrics between current performance and strategic objectives.
  Vector spaces quantify gaps in strategic performance.
• LLM-based content summarization and keyword tagging using Ollama and LocalAI
  LLMs like Ollama and LocalAI are revolutionizing content summarization and keyword tagging, offering efficient and accurate solutions for information management.
• Markdown generation from research PDFs using extractor model and formatter LLM
  Transforming scholarly PDFs into Markdown documents with AI-powered tools.
• Mathematical approaches to visualizing customer journey maps across different touchpoints using hypercube representations.
  Hypercube models visualize customer journey touchpoints.
• Mathematical foundations of weighted importance metrics when prioritizing improvement areas across multiple dimensions.
  Analyzing weighted metrics for multidimensional priorities.
• Matrix operations for analyzing gaps between actual and ideal states across multiple business metrics simultaneously.
  Matrix analysis: bridging actual-ideal metric gaps
• Multilingual translation pipeline with dedicated translation model and summarizer
  Introducing a cutting-edge multilingual translation pipeline, featuring a specialized translation model and advanced summarization capabilities.
• Privacy-Preserving AI: XAI, SMPC, FL, and Cryptography
  Focus: Secure XAI, leveraging advanced techniques.
• Private chatbot with one model for conversation and another for factual grounding
  Combining a conversational model with a factual grounding model enables private chatbots to engage users while maintaining accuracy.
• Question answering over structured data using embedding model and context-aware LLM
  In this article, we explore the integration of embedding models with context-aware Large Language Models (LLMs) to perform effective question answering over structured data.
• RAG (Retrieval-Augmented Generation) with Qdrant and Custom Metadata
  Leverage Qdrant’s vector search capabilities to enhance RAG models, enabling efficient retrieval of relevant information based on custom metadata.
• Risk Assessment of Emerging Tech: AI, ML, and Blockchain
  AI/ML/Blockchain: Risk Assessment Crucial.
• Run a Secure LLM API in Docker Using Ollama and LLaMA 3
  Secure Ollama LLM API with Docker & LLaMA 3.
• Scoring and Clustering Clients Based on Contract Profiles and Embeddings
  Leverage AI to segment clients effectively using contract analysis and embedding techniques.
• Secure AI Model Serving With HTTPS + JWT in Docker
  Secure Dockerized AI serving: HTTPS + JWT.
• Semantic search over PDF archives with embedding model and RAG model pairing
  Leverage embedding models and Retrieval Augmented Generation (RAG) to efficiently index and query large PDF collections, enabling precise semantic search capabilities.
• Streaming Insights from NocoDB to Qdrant Using Local Embeddings
  Leverage NocoDB data with Qdrant, using local embeddings for efficient streaming insights.
• Use GPT Embeddings to Index Private Files With Docker
  Use GPT embeddings for private file indexing with Docker.
• Use Homomorphic Encryption in ML With Concrete-ML in Docker
  Secure ML: Concrete-ML & Docker Leveraging homomorphic encryption (HE)
• Using Qdrant and Ollama for Local AI-Powered Data Discovery
  Leverage Qdrant’s vector search and Ollama’s generative models to enable efficient, localized AI-driven data exploration.
• Vectorization approaches to quantifying progress pathways from current state to ideal state in organizational development.
  Vectorization methods quantify organizational progress.
• Voice-to-text plus summarization using Whisper model and LLM for digest generation
  Leverage Whisper’s advanced voice-to-text capabilities combined with LLMs to generate concise summaries, revolutionizing digest creation.
• Zero-Knowledge Proofs With Zokrates in Docker
  Zokrates & Docker: Zero-Knowledge Proofs