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AI language models

Sascha Block2025-02-05T13:55:57+01:00Tags: AGI, AI ethics, AI for companies, AI Governance, AI in everyday life, AI in research, AI language models, AI Research, AI safety, AI-supported creativity, Alexa, Algorithmic Bias, Artificial intelligence, Attention mechanism, Autoencoding, Autoregressive models, BERT, Bias in AI, bidirectional models, Chatbots, CLIP, Cloud computing, Computational linguistics, Computing power, Conversational AI, DALL-E, Data ethics, Deep learning, DeepMind, digital assistants, Embeddings, Energy-efficient AI, Explainability, Fairness in AI, Few-Shot Learning, Few-Shot Prompting, Fine tuning, GDPR, generative AI, generative language models, GitHub Copilot, GloVe, Google AI, Google Assistant, GPT, GPT-2, GPT-3, GPT-4, GPUs, Hallucinations in AI, Hugging Face, Information retrieval, Interpretability, Knowledge Graphs, Language processing, Large Language Models, LLM, LSTM, machine learning, machine translation, Masked Language Modeling, Meta AI, Model compression, multimodal AI, multimodal models, Named Entity Recognition, natural language processing, neural networks, NLP, Open source AI, OpenAI, OpenAI Codex, Perplexity, Privacy, Prompt Engineering, Prompt Tuning, Regulation, Reinforcement Learning, Responsible AI, Retrieval-Augmented Generation, RNN, Scalability, Self-Attention, Sentiment analysis, Siri, Supervised Learning, synthetic data, T5, Text classification, Text generation, Tokenizer, TPUs, Training data, Transfer Learning, Transformer, Unsupervised Learning, Voice assistants, Word2Vec, Zero-shot learning|

AI language models - what's behind them? Artificial intelligence is changing the way we interact with machines. Language models enable machines not only to understand language, but also to generate meaningful, human-like responses. Find out more now...

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Retrieval Augmented Generation (RAG) – using AI models effectively

Sascha Block2024-03-30T10:36:37+01:00Tags: Advanced RAG, AI language models, AI technology, Artificial intelligence, Data processing, Dialog systems, FAQ, Language models, Modular RAG, multimodal applications, Naive RAG, Question and answer systems, RAG Systems|

What is Retrieval Augmented Generation (RAG)? Retrieval-Augmented Generation (RAG) is an advanced AI technique in AI language modeling based on the integration of external information sources to improve and augment answer generation. A RAG system combines the comprehensive knowledge capacities of a Large Language Model (LLM) with the ability to obtain specific information from an external knowledge repository. This AI method allows the model to generate answers based not only on its internally trained knowledge, but also on current, specific and extensive external data. Find out more now!

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