46 docs tagged with "applied-ai"

Learn how adaptive learning systems model student knowledge state and sequence educational content using IRT, CAT, spaced repetition, and multi-armed bandits to maximize learning outcomes.

Timeline extraction, deposition analysis, exhibit classification, chronology building, and the AI systems that help litigators prepare and try cases.

Regulatory landscape for healthcare AI - FDA SaMD classification, 510(k) vs PMA clearance, EU AI Act, HIPAA compliance for AI, bias auditing, and post-market surveillance for deployed medical AI systems.

Learn how AI systems automatically score essays, grade short answers, generate feedback, detect plagiarism, and audit for bias in educational assessment pipelines.

Learn how to detect anomalies in industrial sensor data using statistical baselines, isolation forests, LSTM autoencoders, multivariate deep learning methods, and real-time streaming architectures.

How AI is deployed across industries - real architectures, domain constraints, and production patterns from finance, healthcare, legal, retail, manufacturing, and education.

Building NLP pipelines on Electronic Health Records - named entity recognition for clinical text, negation detection, de-identification for HIPAA compliance, and fine-tuning BERT variants on medical corpora.

Regulatory change detection, gap analysis automation, policy compliance checking, and building AI systems that track regulatory requirements across jurisdictions.

Learn how AI-powered visual inspection systems detect manufacturing defects using anomaly detection, semantic segmentation, and real-time inline inspection pipelines.

Learn how LLMs generate educational content - questions, explanations, worked examples, and quizzes - with quality control, Bloom's taxonomy alignment, and hallucination mitigation.

Clause extraction, obligation detection, risk identification, and building NLP systems for commercial contract analysis at law firm and enterprise scale.

CLV prediction with BG/NBD probabilistic models, Gamma-Gamma monetary value, deep learning on purchase sequences, RFM segmentation, and the ML systems that drive acquisition and retention budget decisions.

Hierarchical time series forecasting at retail scale - classical methods, gradient boosting, deep learning with TFT, and the engineering behind forecasting millions of SKUs in real time.

Learn how digital twins combine physics-based simulation with machine learning to create virtual replicas of manufacturing systems for prediction, optimization, and what-if analysis.

e-Discovery, technology-assisted review (TAR), predictive coding, and building ML systems that process millions of documents for legal discovery in weeks instead of years.

How AI accelerates pharmaceutical research - AlphaFold protein structure prediction, graph neural networks for molecular property prediction, generative chemistry, and virtual screening for drug candidates.

Price elasticity estimation, competitor-aware pricing, markdown optimization for seasonal goods, causal inference for pricing decisions, and the ML systems behind Amazon's real-time repricing engine.

Learn how to deploy AI models on industrial edge hardware using TensorRT quantization, ONNX Runtime, OpenVINO, MQTT-based edge-cloud architectures, and fleet management for hundreds of edge devices.

Learn FERPA compliance, algorithmic bias in educational AI, surveillance concerns, data minimization, transparency requirements, and responsible deployment of AI in learning environments.

Training ML models across hospital systems without sharing patient data - FedAvg algorithm, differential privacy, non-IID data challenges, NVIDIA FLARE, and practical multi-hospital federated learning with Flower.

AI for genomics and protein science - AlphaFold 2 architecture, variant calling, polygenic risk scores, DNA language models, and practical protein structure prediction with ESMFold.

Why LLM hallucination is malpractice in legal contexts, grounding strategies, citation verification pipelines, and architecture patterns for trustworthy legal AI.

Learn how to build IIoT data pipelines connecting industrial protocols (OPC-UA, MQTT, Modbus) to time-series databases, Kafka, and ML inference systems for manufacturing intelligence.

Patent analysis, prior art search, trademark similarity detection, and the ML systems that support patent prosecution, portfolio management, and IP litigation.

Newsvendor problem, safety stock optimization, reorder point prediction, multi-echelon inventory, and ML-driven policies that balance stockouts against carrying costs at retail scale.

Learn Bayesian Knowledge Tracing (BKT), Deep Knowledge Tracing (DKT), SAKT, and AKT - models that estimate student knowledge state over time from interaction sequences.

Domain adaptation of LLMs for legal tasks - LegalBench evaluation, instruction tuning on legal data, and building legal AI models that outperform general-purpose LLMs on specific tasks.

Dense retrieval over case law, citation graph analysis, precedent finding, and building legal research AI that surfaces relevant authorities without hallucinating fake cases.

Deep learning for radiology and pathology - CNN architectures, DICOM pipelines, transfer learning from ImageNet to medical domains, and clinical deployment considerations including FDA clearance.

Building ML systems under HIPAA constraints and FDA regulation - medical imaging, clinical NLP, drug discovery, and patient outcome prediction.

Contract analysis, legal research automation, compliance monitoring, and document review at scale - building AI where hallucination is malpractice and every output needs a citation.

Demand forecasting, personalization at scale, dynamic pricing, inventory optimization, and supply chain AI - the ML systems behind recommendations and prices.

Predictive maintenance, computer vision for quality control, digital twins, and process optimization - deploying ML on the factory floor where downtime costs thousands per minute.

Adaptive learning systems, AI-powered assessment, knowledge tracing, and personalized tutoring - building educational AI that actually improves learning outcomes.

Learn readability scoring, educational NER, automatic summarization, curriculum alignment, concept map generation, and question difficulty estimation for educational NLP pipelines.

Building clinical prediction models for hospital readmission, ICU mortality, and sepsis onset - feature engineering from EHR data, LSTM models for vital sign time series, survival analysis, calibration, and deployment in clinical workflows.

Two-tower retrieval models, real-time feature serving, ANN search, and the full ML architecture that powers personalized recommendations for hundreds of millions of retail users.

Learn how to build AI tutoring systems using Socratic dialogue, LLM-based hint generation, worked example fading, affective state detection, and multi-session context management.

Learn how AI systems predict equipment failures before they happen using sensor data, feature engineering, anomaly detection, and remaining useful life prediction.

Learn how to formulate manufacturing process control as an MDP, design safe reward functions, use offline RL from historical data, and deploy RL policies in production industrial settings.

Deploying radiology AI into clinical workflows - PACS integration, DICOM processing, FDA clearance, worklist prioritization, and monitoring for distribution shift in live hospital environments.

POS data streams, customer data platform architecture, real-time feature computation with Flink, medallion data lake architecture for retail, privacy compliance, and event streaming pipelines for retail ML.

Learn how to build early warning systems for at-risk students, predict dropout and grades, audit for fairness, and design interventions using ML on LMS engagement data.

Lead time prediction, supplier risk scoring, demand sensing, disruption detection, route optimization, and the ML systems that build resilient and efficient retail supply chains.

Learn how AI transforms supply chain management through probabilistic demand forecasting, supplier risk scoring, inventory optimization, disruption detection, and vehicle routing.

Image embedding models for retail visual search, CLIP-based product discovery, FAISS similarity retrieval, multimodal search combining image and text, and the systems behind shop-the-look features.