# DISRUPTION - [Data Architecture](https://training.continuumlabs.ai/disruption/data-architecture.md) - [What is a data pipeline?](https://training.continuumlabs.ai/disruption/data-architecture/what-is-a-data-pipeline.md) - [What is Reverse ETL?](https://training.continuumlabs.ai/disruption/data-architecture/what-is-reverse-etl.md): Combining generative AI with Reverse ETL in Modern Businesses - [Unstructured Data and Generatve AI](https://training.continuumlabs.ai/disruption/data-architecture/unstructured-data-and-generatve-ai.md) - [Resource Description Framework (RDF)](https://training.continuumlabs.ai/disruption/data-architecture/resource-description-framework-rdf.md) - [Integrating generative AI with the Semantic Web](https://training.continuumlabs.ai/disruption/data-architecture/integrating-generative-ai-with-the-semantic-web.md) - [Search](https://training.continuumlabs.ai/disruption/search.md): Generative AI and its relationship with search - [BM25 - Search Engine Ranking Function](https://training.continuumlabs.ai/disruption/search/bm25-search-engine-ranking-function.md) - [BERT as a reranking engine](https://training.continuumlabs.ai/disruption/search/bert-as-a-reranking-engine.md): Retrieval and Reranking - [BERT and Google](https://training.continuumlabs.ai/disruption/search/bert-and-google.md) - [Generative Engine Optimisation (GEO)](https://training.continuumlabs.ai/disruption/search/generative-engine-optimisation-geo.md): Navigating the New Frontier: A Guide to Generative Engine Optimisation - [Billion-scale similarity search with GPUs](https://training.continuumlabs.ai/disruption/search/billion-scale-similarity-search-with-gpus.md) - [FOLLOWIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions](https://training.continuumlabs.ai/disruption/search/followir-evaluating-and-teaching-information-retrieval-models-to-follow-instructions.md) - [Neural Collaborative Filtering](https://training.continuumlabs.ai/disruption/search/neural-collaborative-filtering.md): The highly popular 2017 paper that drove the advance of recommendation systems - [Federated Neural Collaborative Filtering](https://training.continuumlabs.ai/disruption/search/federated-neural-collaborative-filtering.md): Collabative Filtering - Matching Consumers with Products and Services with Privacy - [Latent Space versus Embedding Space](https://training.continuumlabs.ai/disruption/search/latent-space-versus-embedding-space.md) - [Improving Text Embeddings with Large Language Models](https://training.continuumlabs.ai/disruption/search/improving-text-embeddings-with-large-language-models.md): Liang Wang and the Microsoft team - [Recommendation Engines](https://training.continuumlabs.ai/disruption/recommendation-engines.md): There is major disruption coming to the recommendation engine industry - [On Interpretation and Measurement of Soft Attributes for Recommendation](https://training.continuumlabs.ai/disruption/recommendation-engines/on-interpretation-and-measurement-of-soft-attributes-for-recommendation.md) - [A Survey on Large Language Models for Recommendation](https://training.continuumlabs.ai/disruption/recommendation-engines/a-survey-on-large-language-models-for-recommendation.md) - [Model driven recommendation systems](https://training.continuumlabs.ai/disruption/recommendation-engines/model-driven-recommendation-systems.md) - [Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations](https://training.continuumlabs.ai/disruption/recommendation-engines/recommender-ai-agent-integrating-large-language-models-for-interactive-recommendations.md) - [Foundation Models for Recommender Systems](https://training.continuumlabs.ai/disruption/recommendation-engines/foundation-models-for-recommender-systems.md) - [Exploring the Impact of Large Language Models on Recommender Systems: An Extensive Review](https://training.continuumlabs.ai/disruption/recommendation-engines/exploring-the-impact-of-large-language-models-on-recommender-systems-an-extensive-review.md) - [AI driven recommendations - harming autonomy?](https://training.continuumlabs.ai/disruption/recommendation-engines/ai-driven-recommendations-harming-autonomy.md): "Artificial intelligence vs. autonomous decision-making in streaming platforms: A mixed-method approach" by Ana Rita Gonçalves, Diego Costa Pinto, Saleh Shuqair, Marlon Dalmoro, and Anna S. Mattila - [Logging](https://training.continuumlabs.ai/disruption/logging.md) - [A Taxonomy of Anomalies in Log Data](https://training.continuumlabs.ai/disruption/logging/a-taxonomy-of-anomalies-in-log-data.md) - [Deeplog](https://training.continuumlabs.ai/disruption/logging/deeplog.md) - [LogBERT: Log Anomaly Detection via BERT](https://training.continuumlabs.ai/disruption/logging/logbert-log-anomaly-detection-via-bert.md) - [Experience Report: Deep Learning-based System Log Analysis for Anomaly Detection](https://training.continuumlabs.ai/disruption/logging/experience-report-deep-learning-based-system-log-analysis-for-anomaly-detection.md) - [Log-based Anomaly Detection with Deep Learning: How Far Are We?](https://training.continuumlabs.ai/disruption/logging/log-based-anomaly-detection-with-deep-learning-how-far-are-we.md) - [Deep Learning for Anomaly Detection in Log Data: A Survey](https://training.continuumlabs.ai/disruption/logging/deep-learning-for-anomaly-detection-in-log-data-a-survey.md) - [LogGPT](https://training.continuumlabs.ai/disruption/logging/loggpt.md) - [Adaptive Semantic Gate Networks (ASGNet) for log-based anomaly diagnosis](https://training.continuumlabs.ai/disruption/logging/adaptive-semantic-gate-networks-asgnet-for-log-based-anomaly-diagnosis.md) --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://training.continuumlabs.ai/disruption.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.