yolandamuriel.comARTIFICIAL INTELLIGENCE (44) – Natural Language Processing (22) RAG (Retrieval‑Augmented Generation)
Retrieval‑Augmented Generation (RAG) is an architecture that combines: Information retrieval (searching relevant documents), and Text generation (using a Large Language Model). Instead of relying only on what the model learned during training, RAG allows the model to: Look up external, up‑to‑date, or private knowledge, and Use that retrieved information as context when generating an answer. This drastically improves accuracy, grounding, and trustworthiness. Overview of the Main […]
Hacker NewsNatural Language Autoencoders: Turning Claude's Thoughts into Text
https://www.anthropic.com/research/natural-language-autoencoders
#HackerNews #NaturalLanguageProcessing #Autoencoders #Claude #AIResearch #TextGeneration
ARTIFICIAL INTELLIGENCE (40) – Natural Language Processing (18) Few-Shot Prompting
Few-shot prompting is a technique used with large language models where the model is given a small number of examples inside the prompt itself to show what kind of task it should perform and how the output should look. Importantly, the model is not retrained or fine-tuned. Instead, it learns on the fly from the examples provided in the context. This behavior is often called in-context learning. Emergent few-shot learning highlights that this ability of modelsl was not explicitly programmed, […]
ARTIFICIAL INTELLIGENCE (39) – Natural Language Processing (17) Historical Evolution of Language Models (NLP)
1948 – Bag-of-Words & Distributional Hypothesis Bag-of-Words (BoW): Text is represented as an unordered collection of words, ignoring grammar and word order. Only word frequency matters. Distributional Hypothesis: “A word is characterized by the company it keeps.” This idea states that words with similar meanings tend to appear in similar contexts. © Image. Rahul Sharma NLP: Bag of Words 1954 – N-gram Models N-gram Model: Predicts the next word in a sequence based on the […]
ARTIFICIAL INTELLIGENCE (39) – Natural Language Processing (17) Historical Evolution of Language Models (NLP)
1948 – Bag-of-Words & Distributional Hypothesis Bag-of-Words (BoW): Text is represented as an unordered collection of words, ignoring grammar and word order. Only word frequency matters. Distri…
ARTIFICIAL INTELLIGENCE (35) – Natural Language Processing (13) Understanding Attention in Sequence‑to‑Sequence Models
Sequence‑to‑sequence (Seq2Seq) models are a common type of neural network used in tasks where one sequence is transformed into another, such as machine translation, speech recognition, and image captioning. A typical Seq2Seq model is composed of two parts: an encoder and a decoder. Seq2Seq Models Without Attention In a traditional Seq2Seq model without attention, the encoder reads the entire input sequence (for example, a sentence in English or Chinese) and compresses all the […]
ARTIFICIAL INTELLIGENCE (35) – Natural Language Processing (13) Understanding Attention in Sequence‑to‑Sequence Models
Sequence‑to‑sequence (Seq2Seq) models are a common type of neural network used in tasks where one sequence is transformed into another, such as machine translation, speech recognition, and image ca…
PPC LandFYI: Amazon's AI matches shelter pets with families using natural language queries: Amazon Ads and PetArmor combined natural language AI, generative video, and shelter construction to match adoptable dogs and cats with prospective families. https://ppc.land/amazons-ai-matches-shelter-pets-with-families-using-natural-language-queries/ #AdoptDontShop #PetAdoption #ShelterPets #AmazonAI #NaturalLanguageProcessing
Emotion concepts and their function in a large language model
https://www.anthropic.com/research/emotion-concepts-function
#HackerNews #EmotionConcepts #LanguageModels #AIResearch #NaturalLanguageProcessing #MachineLearning
Python for Data ScienceWe have now significantly expanded our collection of open data and organised it by topic: https://www.python4data.science/en/latest/data-processing/opendata.html
#Python #DataScience #OpenData #Agriculture #Biology #Chemistry #Climate #Weather #ComputerNetwork #Energy #Finance #GeoScience #Healthcare #ImageProcessing #Medicine #NaturalLanguageProcessing #NeuroScience #Physics #SocialScience #Transport #Traffic
#Python #DataScience #OpenData #Agriculture #Biology #Chemistry #Climate #Weather #ComputerNetwork #Energy #Finance #GeoScience #Healthcare #ImageProcessing #Medicine #NaturalLanguageProcessing #NeuroScience #Physics #SocialScience #Transport #Traffic
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