Glossary

To help you understand an ecosystem which, at first glance, seems excessively technical and complex, we have compiled a non-exhaustive list of brief, clear definitions of the terms used in the field of generative artificial intelligence.

AI Industrial Revolution

The AI Industrial Revolution refers to the significant transformation in industries and economies brought about by the adoption and integration of artificial intelligence technologies. This revolution is characterized by the extensive use of AI in automating tasks, enhancing productivity, and fostering innovation across various sectors, akin to the impact of previous industrial revolutions.

Artificial General Intelligence (AGI)

AGI, or Artificial General Intelligence, refers to a theoretical form of artificial intelligence that has the ability to understand, learn, and apply its intelligence broadly and flexibly, much like a human. Unlike specialized AI systems that are designed for specific tasks (like language translation, playing chess, or recognizing images), AGI would have the capacity to learn and excel in a wide range of cognitive tasks, adapt to new environments, solve problems, and, for some - although this is strongly debated -, even possess self-awareness and emotional understanding.

Computer Vision

Computer Vision is a field of artificial intelligence that trains computers to interpret and understand the visual world. Using digital images from cameras and videos and deep learning models, machines can accurately identify and classify objects — and then react to what they "see".

Data Vectorization & AI Augmentation

Data vectorization refers to the process of converting raw data into a numerical representation that can be processed by machine learning algorithms. This data can then be exploited to produce much more reliable results, and provide a transparent indication of the sources of the information produced by the AI. We call this an augmented AI model.

Deep Learning

Deep Learning is a subset of machine learning based on artificial neural networks. This learning method mimics the workings of the human brain in processing data and creating patterns for use in decision making. Deep learning is particularly useful in feature detection from raw, unstructured data.

Digital Disruption

Digital disruption refers to the changes that occur when new digital technologies and business models affect the value proposition of existing goods and services. It's often seen when a new, innovative technology quickly and significantly alters the way things are traditionally done in an industry, potentially displacing established companies.

Digital Transformation

Digital Transformation is the process of using digital technologies to create new — or modify existing — business processes, culture, and customer experiences to meet changing business and market requirements. This reimagining of business in the digital age is digital transformation.

Fine-tuning

Fine-tuning in AI involves taking an AI model that has already learned a lot from a general dataset and teaching it more about a specific topic or type of data. It's like having a student who's good at many subjects, and then giving them extra lessons in one subject, like math, to make them even better at it. This helps the AI to understand and perform tasks related to that specific subject much more accurately.

Generative Artificial Intelligence (Gen AI)

Generative AI refers to a category of artificial intelligence algorithms that are designed to generate new data samples that mimic a given dataset's characteristics. These algorithms learn patterns and structures from existing data and then use that knowledge to create new, original data samples. Generative AI models are capable of generating various types of content, including images, text, music, and even videos

Hallucination in AI

In AI, hallucination refers to a situation where a model generates incorrect, fabricated, or irrelevant information. This is often seen in complex language models or image generation models, where the AI produces outputs that are not grounded in the input data or reality.

IoT (Internet of Things)

The Internet of Things, or IoT, refers to the network of physical objects — "things" — that are embedded with sensors, software, and other technologies for the purpose of connecting and exchanging data with other devices and systems over the internet. These objects can range from ordinary household items to sophisticated industrial tools.

Large Language Models (LLMs)

Large Language Models (LLMs) are a type of artificial intelligence system that is trained on massive amounts of text data to understand and generate human-like language. These models are designed to process and generate natural language text. ChatGPT is based on an LLM.

Low-code

Low-code is a contemporary approach to application development that minimizes the reliance on extensive coding. With low-code platforms, you can construct and customize application functionalities by leveraging visual interfaces and pre-built components, reducing the need for extensive manual coding. It's akin to constructing a building with prefabricated modules, where developers can piece together various elements to form the desired application architecture. Low-code is especially beneficial for developers who possess moderate coding skills or domain expertise but may not be proficient programmers. Moreover, seasoned developers often appreciate low-code platforms for streamlining the process of iterative development and enhancing productivity through rapid prototyping and deployment.

Machine Learning

Machine Learning is a subset of AI that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. It focuses on the development of computer programs that can access data and use it to learn for themselves.

Multi-modal AI refers to artificial intelligence models that can process and understand multiple types of data, such as images, text, and audio, in a unified manner. These models combine techniques from computer vision, natural language processing, and audio processing to analyze and generate content across different modalities.

Neural Network Models

Neural network models are a class of machine learning algorithms inspired by the structure and function of the human brain. These models consist of interconnected nodes, called neurons, organized into layers. Each neuron receives input, processes it, and produces an output, which is then passed on to the next layer. Neural network models are capable of learning complex patterns and relationships in data without being explicitly programmed. They have been successfully applied in various tasks including image recognition, speech recognition, natural language processing, and many others. Examples of neural network models include feedforward neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers.

Natural Language Processing (NLP)

NLP stands for Natural Language Processing. It is a field of artificial intelligence (AI) that focuses on the interaction between computers and humans through natural language. The goal of NLP is to enable computers to understand, interpret, and generate human language in a way that is both meaningful and useful. NLP techniques are used in various applications such as language translation, sentiment analysis, text summarization, speech recognition, and more.

No-Code

No-code is a modern way to create applications without the need for complex coding. With no-code, you can design and build your app's functionality by connecting different elements using simple clicks instead of writing lines of code. It's akin to assembling blocks on a digital canvas. No-code is particularly useful for professionals who aren't expert programmers but have a clear vision of their app's requirements. Additionally, even experienced programmers find that using no-code makes modifying and updating apps quicker and more efficient.

On-premises Integration

On-premises integration refers to the process of integrating various software and systems within an organization's own physical premises, rather than relying on external or cloud-based solutions. This often involves setting up servers, databases, and applications in-house, allowing for greater control over data and infrastructure.

Prompt Engineering

Prompt engineering is the process of structuring text that can be interpreted and understood by a generative AI model. The more precise is the instruction (prompt), the better is the output. A well-engineered prompt can significantly enhance the quality and specificity of the AI's output, much like giving clear, concise directions helps a person understand and complete a task more effectively.

Retrieval Augmented Generation (RAG)

Retrieval Augmented Generation is a technique in AI, particularly in language models, where the system enhances its response generation by retrieving information from a large database or document collection. This allows the model to provide more accurate, detailed, and contextually relevant outputs.

Specialized AI Agents

A specialized AI assistant simply consists of a chatbot that is placed in a predetermined context (a website, a subject, a specific situation) and that responds accordingly without the user having to provide a complex instruction (prompt). With specialized AI Assistants, you can interact intuitively and get the output you need in seconds. Responses of a Specialized AI Agent are less prone to hallucination but still may produces incorrect information unless connected to an augmented AI model.

Supervised Learning

Supervised learning is a type of machine learning where the model is trained on labeled data. The training data includes input-output pairs, where the model learns to make predictions or decisions based on this known data.

Unsupervised Learning

Unsupervised learning is a type of machine learning that uses data without labeled responses. The system tries to learn the patterns and structure from such data by itself, often to discover hidden patterns or groupings in the data.