Neural networks are algorithms inspired by the structure of biological brains that are used in Artificial intelligence systems to process complex data. They consist of layers of neurons (computing units) that are networked together. Each neuron processes input information and sends the result to neurons in subsequent layers. Neural networks are used in many fields, such as image analysis, speech recognition, forecasting, and decision-making automation.
Neural Networks
Type of technology
Description of the technology
Basic elements
- Neurons (computing units): Basic elements of a network that transform input data.
- Network layers: Ordered structures of neurons, usually divided into input, hidden, and output layers.
- Connection weights: Values that modify the signal strength between neurons that are tuned during training.
- Activation function: The mechanism that determines whether a neuron will “activate” and pass the signal on.
- Learning algorithms: Methods for adjusting weights to minimise model errors.
Industry usage
- Medicine: Detecting diseases from medical images and genome analysis.
- Finance: Risk analysis, stock price forecasting, and automation of investment decisions.
- Marketing: Customer segmentation, ad personalisation, and user behaviour analysis.
- Automotive industry: Autonomous vehicles and environment recognition systems.
- Art: Generating images and music and transforming content based on patterns.
Importance for the economy
Neural networks are revolutionising sectors such as medicine, finance, industry, and marketing by automating analysis, personalising offers, and optimising production processes. With the help of neural networks, companies can create new advanced products and services, making them more competitive and innovative in the market. In the future, the use of neural networks will increase as Artificial intelligence technology advances.
Related technologies
Artificial Intelligence
Specialised libraries and frameworks, such as TensorFlow and PyTorch, support the creation and training of neural networks.
Big Data
The development of powerful processors enables faster processing of data in neural networks.
Mechanism of action
- Neural networks are based on processing data through layers of neurons. The input data is passed to neurons in the input layer, which then process it and send it on to the hidden layers. In each layer, the activation function decides whether and to what extent the signal will be sent. Finally, the processing results reach the output layer, where they are transformed into final predictions or decisions. The learning process involves adjusting the weights of neuron connections to minimise the difference between the network’s predictions and actual results.
Advantages
- Pattern recognition: Neural networks are excellent at identifying complex patterns in data.
- Adaptability: Networks can adjust their operations as new data arrives.
- High accuracy: They offer better performance in tasks such as image or speech recognition compared to traditional algorithms.
- Automation: They enable automation of processes in many industries, saving time and resources.
Disadvantages
- Training costs: Large networks require significant computing resources and time to train.
- Lack of interpretability: Neural networks are often “black boxes” – it is difficult to understand how they make decisions.
- Overtraining: Networks can over-adapt to training data, reducing their effectiveness on new data.
- Risk of errors: Incorrect predictions can lead to serious consequences in applications such as medicine and finance.
Implementation of the technology
Required resources
- Large data sets: High-quality training data to train the network.
- IT infrastructure: Powerful graphics processing units (GPUs) for processing data in neural networks.
- Software: Tools and frameworks for building and training neural network models.
- Specialised team: Data scientists and AI engineers to develop and optimise models.
- Computing environment: Computing resources (local or cloud) to handle complex operations.
Required competences
- Knowledge of machine learning algorithms: Ability to create and train neural models.
- Programming: Knowledge of languages such as Python and ML libraries (TensorFlow, PyTorch).
- Model optimisation: Ability to optimise neural networks for performance.
- Data analysis: Ability to prepare and interpret large data sets.
- Mathematics: Understanding the numerical methods and statistics used in neural networks.
Environmental aspects
- Energy consumption: Training large neural networks requires considerable energy resources.
- Raw material consumption: The IT infrastructure needed for data processing requires raw materials, such as rare earth metals.
- Waste generated: IT resources must be upgraded regularly, which generates electronic waste.
- Emissions of pollutants: Development of data centres for training neural networks may lead to CO2 emissions.
Legal conditions
- Legislation governing the implementation of solutions, such as AI Act (example: regulations for transparency of algorithms).
- Safety standards: Certifications to ensure the security of AI systems (example: ISO/IEC 27001 standards).
- Data security: Regulations for the protection of personal data used to train models (example: GDPR).
- Intellectual property: Patents on AI algorithms and solutions (example: patent law on AI-based solutions).
- Export regulations: Regulations for the export of advanced computing technologies (example: restrictions on GPU exports to sanctioned countries).