Artificial intelligence (AI) refers to the field of computer science that deals with the creation of machines or software capable of performing tasks that typically require human intelligence. This includes skills such as learning, reasoning, problem solving, perception, and language comprehension.
The aim of AI is to develop systems that can act autonomously by learning from experience and understanding their environment and making decisions based on this knowledge. AI systems are used in a wide range of areas, from automating simple tasks to solving complex problems in science, medicine, industry, and everyday life.
The functioning of an AI system depends heavily on the type and the technologies used. In general, AI systems work by processing data and learning from this data to perform specific tasks or solve problems.
Collecting data
The first step is to collect a large amount of data that is relevant to the specific task. This data can be text, images, videos, audio files, or numerical values, depending on what the AI needs to learn.
Prepare the data
The collected data often needs to be cleaned and formatted to remove inconsistencies, missing values, or irrelevant information. This process is called data cleansing and is crucial for improving the quality of the AI training results.
Choose a model
An AI model is essentially a mathematical structure that determines how the input data is processed to produce the desired output. There are many different types of models, including neural networks, decision trees, support vector machines, and many others. The choice of model depends on the nature of the task, the data available, and the specific requirements of the project.
Training the model
During training, the AI model learns to recognize patterns and relationships in the data. This is done by adjusting the parameters of the model so that its predictions or decisions are as accurate as possible. In machine learning, this can be done using different approaches such as supervised learning, unsupervised learning, or reinforcement learning.
Evaluation and optimization
After the model has been trained, it is tested with new, previously unseen data to evaluate its accuracy and performance. Based on this evaluation, further adjustments can be made to improve the model’s performance.
Deployment
Once the model is working satisfactorily, it can be deployed in a real environment to perform the tasks for which it was developed. These steps may vary depending on the complexity of the AI system. Modern AI systems, especially those that use deep learning, can consist of millions of parameters and require large amounts of data and computing power for training.
Weak AI, also known as narrow AI, refers to systems that are designed for specific tasks and are able to perform these tasks without any real understanding or awareness of the world. The intelligence of these systems is limited to a narrow problem area.
Examples include chatbots, recommendation systems, image recognition software, and other applications that perform a specific function. These systems operate within a predefined framework or set of rules and have no ability to act or “think” beyond what they have been programmed to do.
Strong AI refers to a theoretical AI system that has real understanding and awareness. Such systems would have a general intelligence that enables them to learn and perform any task, solve problems and think for themselves, similar to the human brain.
A real example of strong AI does not yet exist, but it would be able to make complex decisions, be creative, understand emotions, and learn and adapt in unfamiliar situations. Strong Artificial Intelligence would include, for example, self-awareness and the ability to form abstract thoughts, going far beyond the capabilities of today’s AI systems.
Artificial Intelligence has a wide range of applications that permeate almost every sector of our society and economy.
Healthcare:
Financial services:
Education:
Automotive industry:
Retail:
Security:
Entertainment:
Environment:
Generative AI models are a class of algorithms in artificial intelligence that are designed to generate data that is similar to the real data they were trained with. Unlike discriminative models, which aim to predict the category or characteristics of input data, generative models focus on creating new data points. This capability makes them particularly useful for tasks such as creating realistic images, music, text, or video.
Generative Adversarial Networks (GANs)
GANs consist of two networks: a generator, which produces data, and a discriminator, which distinguishes whether the data is real or produced by the generator. Through this competition, the generator learns to produce ever more realistic data.
Variational Autoencoders (VAEs)
VAEs are a group of models that learn to encode a data set into a compressed latent space and reconstruct data from it. VAEs are often used to generate new data that is similar to the trained data, such as images or text.
Autoregressive models
These models generate sequences of data (such as text or music) by learning the probabilities of the sequence elements based on their predecessors. Examples of this are transformer models such as GPT (Generative Pretrained Transformer) from OpenAI, which is used for text generation.
Transformer-based models
A special type of autoregressive model that is based on transformer architecture and is characterized by its ability to recognize long-term dependencies in data. They are particularly effective at processing sequences such as speech or text and are used for a variety of generative tasks.
Generative AI models are used in a variety of areas:
