Artificial Intelligence

Definition

Basic kinds

• Machine learning (ML): Algorithms that learn from data and improve their performance over time, used for classification, prediction, and pattern analysis, among others.
• Natural language processing (NLP): Technologies that enable computers to understand, interpret, and generate human language, used in chatbots, text analysis, and automatic translation.
• Recognising images and computer vision: Algorithms that analyse images and video, used in object recognition, quality control, surveillance, and autonomous vehicles.
• Recommendation systems: Algorithms that personalise content and products, used in marketing, e-commerce, and streaming platforms.
• Autonomous robotics: Applying AI to robots, which are capable of performing tasks autonomously and adapting to changing conditions in industrial and logistics environments.

Main roles

• Smart factories – the ability to analyse large data sets and apply AI to manufacturing processes; organising and using data sets to improve quality control, standardisation and maintenance; creating analysis of equipment functionality and thoroughly improving production lines.
• Predictive maintenance – finding patterns that can help predict and ultimately prevent failures; automated and intelligent prediction enables better planning and determination of the condition of plant and equipment.
• Computer vision – detecting, identifying and tagging objects.
• Cyber-physical systems – smart grids, robotics and smart manufacturing; driving efficient and effective collaboration (from anywhere in the world) for a fully distributed manufacturing environment.
• Robotisation and human-robot collaboration – ensuring the safety of personnel, and giving robots more responsibility for decision-making that can further optimise processes based on real-time data collected from the shop floor.

Basic elements

• Machine learning (ML) algorithms: Methods that allow algorithms to learn from data autonomously, without manual programming. The basic types of ML include supervised learning, unsupervised learning, and reinforcement learning, which have various applications, such as classification, clustering, and process optimisation.
• Artificial neural networks: Structures that model how the human brain works, used for complex analysis, such as image recognition, language processing, and prediction. Neural networks are the basis of advanced techniques, such as deep learning.
• Natural language processing (NLP): Technologies for interacting with computers in natural language. NLP is used in chatbots, machine translation, and sentiment analysis, allowing machines to understand and interpret human speech.
• Predictive algorithms: Algorithms based on historical data that predict future behaviour or performance. They are used in production management, demand forecasting, and predictive maintenance.
• Recommendation systems: Algorithms that personalise content and recommend products or services to users based on the analysis of their behaviour. They are used in e-commerce, marketing, and streaming platforms.
• Optimisation algorithms: Mathematical methods used to optimise industrial and logistics processes. They help minimise costs, increase efficiency, and improve planning.

Mechanism of action

• Data collection: AI requires large amounts of data, which can come from a variety of sources, such as IoT devices, databases, multimedia, and documents. This data is collected, prepared, and cleaned before use.
• Data processing and preparation: The data is prepared for analysis, which includes preprocessing, cleaning, debugging, and transformation to a format suitable for AI algorithms.
• Training of models: AI models are trained on the data, allowing the algorithms to identify patterns and relationships. The model is trained on labeled data (in supervised learning) or on unlabeled data (in unsupervised learning).
• Evaluation and optimisation: After training, the model is tested on new data to assess its effectiveness and accuracy. AI models are optimised to minimise errors and adapt to application requirements.
• Implementation and application: Once tested, the model is deployed in a production environment, where it processes data on the fly and makes decisions or generates predictions depending on the application.
• Monitoring and updating: AI models require regular monitoring and updating to adapt to changing data and operational conditions. It is common to retrain models to maintain their accuracy and effectiveness.
• Interaction with users and systems: Depending on the application, AI systems can communicate with users through graphical interfaces or chatbots or with other systems in an automated manner, enabling remote monitoring and control.