Artificial Intelligence in d.velop software

A Large Language Model (LLM) is a large, pre-trained language model based on neural networks with transformer architecture and deep learning algorithms. The pre-training is done on very large amounts of text and fine-tuned for specific tasks.
Large Language Models (LLMs) “understand”, process, and generate natural language and can be used for many tasks, such as responding to questions, or summarising, supplementing, translating, or generating texts. LLMs can even handle complex texts, questions, or instructions, and generate grammatically and orthographically correct, coherent written results. [Admittedly, the text you’re reading right now was written and then translated by skilled human beings.]

Natural Language Processing (NLP) is a subfield within artificial intelligence (AI). The aim of NLP is to enable computers to understand human (or “natural”) language. Conversely, it could also enable people without programming skills to interact with intelligent machines. Important aspects within NLP include text processing, speech recognition, word and semantic similarity, sentiment analysis, language translation and generation, and question-answering systems. NLP-based AI engines can often engage in natural-like dialogue with humans, for example, through advanced chatbots or voice assistants.

Machine learning is a rapidly developing subfield within artificial intelligence. It is used and researched in NLP-related areas such as speech recognition or translation, as well as a wide range of other areas such as autonomous vehicles, medical diagnosis, game development, or manufacturing. The aim of ML is to enable computers to learn through experience and resolve certain challenges or complete certain tasks better and better over time. At the root of ML lies a form of data analysis, pattern recognition, and decision-making, allowing the AI model to perform tasks or make predictions.

The term AI encompasses a few different things.
Firstly, there are two main types of AI, defined by their scope:
1) Narrow (weak) AI: The AI does not have a comprehensive understanding and exists solely to perform one task.
2) General (strong) AI: The AI has comprehensive understanding and a consciousness, just like a human being. At present, this remains a theoretical form.
Now let’s look at three key subsets or applications of AI:
3) Machine Learning: A computer uses algorithms to learn from data and experience, enabling it to improve its performance on a specific task.
4) Natural Language Processing: The computer can understand and work with human language, e.g., in the form of chatbots.
5) Robots and autonomous systems: These use AI-based technology to autonomously perform tasks and make decisions, without constant human supervision. Autonomous robots are used in various fields, for example, car manufacturing, agriculture, or exploration.

In document recognition, the AI uses machine learning techniques to learn from example datasets: For example: Business documents are translated into an abstract form. This is then used to create a mathematical model of the documents. This in turn can be used to make statements about the documents. The learning process involves reviewing many thousands of examples in a dataset for which the desired answer is already known. Each time a new example is presented, the AI model makes a prediction. If this is not correct, the model is adjusted to reduce future errors. The AI model learns from its mistakes.