Making the transition to artificial intelligence requires an informed choice in terms of the technologies employed and the methods of design and use. On this page, you'll find essential definitions, along with an explanation of each Devana feature. An FAQ is also available. Don't hesitate to ask any questions you may have - they'll be of use to the next user.
Natural Language Processing (NLP) is a field of artificial intelligence that focuses on the interaction between computers and humans via natural language. The ultimate goal of NLP is to read, decipher, understand and make sense of human languages in a valuable and useful way. This is a difficult discipline, as human language is rarely precise and is always changing.
Large-scale language models (LLMs) are a type of machine learning model that can perform a variety of natural language processing tasks. They are trained on a large amount of text and can generate text that looks as if it had been written by a human. These models are able to understand context, generate coherent responses and write relevant texts.
GPT4 & GPT3.5
Claude 2 & 2.1
8x7B open-source
Fine-tuning in generative AI is a technique that involves taking a pre-trained model, which has already learned to perform a certain task, and training it further, usually on a new dataset, so that it can perform a specific task better.
In the context of generative AI, this method is often used to fine-tune the AI's ability to create content that is specifically aligned with certain guidelines or characteristics, such as a particular writing style, a certain type of content (e.g. blog posts about AI), and so on.
Fine-tuning is an effective method for obtaining an AI model that performs very well in a specific task, as it takes advantage of the learning the model has already done and adapts it to a new task.
AI embedding is a technique used in machine learning to represent data with many attributes, such as words in text. In short, it's a way of transforming non-numerical data into numerical vectors.
For everyday use, let's take the example of word embedding. In this context, each word in a vocabulary is represented by a dense vector (a list of real numbers) so that words with similar meanings have similar representations (vectors).
This technique is widely used in the field of natural language processing (NLP), which lies at the heart of AI applications such as voice assistants, chatbots and recommendation systems.
Retrieval-Augmented Generation (RAG) is a machine-learning framework. The RAG method, invented by Facebook, combines elements of retrieval-based text generation and generation-based machine learning to produce more accurate and informed text. RAG enables the AI to understand and answer questions by searching for information in a vast set of text documents at the time of generation, rather than relying solely on its prior, fixed-in-time learning.
For everyday use, let's take the example of word embedding. In this context, each word in a vocabulary is represented by a dense vector (a list of real numbers) so that words with similar meanings have similar representations (vectors).
The advantage of this method is that it enables the model to generate text that is more accurate and better informed, as it can draw on recent, relevant information rather than relying solely on what it has learned during training. The AI is no longer limited to what it has learned during training, and can take advantage of new information as it becomes available.
Fact-checking, also known as fact-checking, is the journalistic practice of verifying the truth and accuracy of public statements and information disseminated in the media and on the Internet. In the age of fake news, this technique has become essential to ensure the dissemination of authentic and reliable information.
Fact-checking is often practiced by dedicated media organizations, journalists and artificial intelligence like Devana, which uses real-time sources to verify and provide accurate information.
We are committed to meeting your needs
within 24H on the public FAQ or in mp.
