The 7 types of AI that everyone can use

the artificial intelligence It is a technology that allows machines to learn and do things that require human intelligence. Instead of following strict instructions, the tool can adapt and improve with experience.

It’s like computer education Thinking and making decisions Just like a person would, but with algorithms and data (provided by humans) instead of a brain.

Although artificial intelligence is a broad and diverse field that includes a variety of approaches and technologies, most are not aware that artificial intelligence can be categorized. Types of artificial intelligence according to its capabilities and applications.

Here are some categories:

Refers to artificial intelligence systems designed for performance Specific and limited tasks. These systems are experts in solving specific problems, such as voice and facial recognition, image processing, chatbots, recommendation systems, and more.

This technology is highly specialized and can outperform humans at specific tasks, but it lacks awareness and general cognitive abilities.

The idea behind strong AI is that it will be a capable machine equal to or greater than human intelligence In all respects. For example, a user can imagine a machine that can understand, learn, and think in different areas, adapt to different situations, and solve complex problems that are similar to or even better than humans.

However, until now, this has not been reached and is still the subject of research, as it still relies on information provided by humans. Achieving this poses significant ethical and technical challenges, and is considered a long-term goal in the field.

It is based on a set of Predefined rules and formal logic to make decisions and solve problems. Created by human experts and applicable to specific situations. It works well in well-structured environments with clear rules, but it can become difficult to maintain and scale as the situation becomes more complex.

It is a branch of artificial intelligence that includes algorithms and models that allow machines to learn and improve by experimenting with data.

There are three main categories:

Supervised learning: Models are trained on classified data, enabling them to make accurate predictions or classifications.

Unsupervised education: Here they are trained on unlabeled data, which enables them to discover hidden patterns and structures in the data.

Learning reinforcement: They learn by interacting with an environment and are rewarded or punished based on their performance on the task.

It is a specific form of machine learning which uses multi-layered artificial neural networks to learn hierarchical representations of data. Deep learning has led to significant advances in areas such as image recognition, natural language processing, speech recognition, and more.

It is based on the representation of knowledge through Symbols and relationships, which allows reasoning and reasoning. It is used in expert systems and in complex logic problems, where human knowledge is encoded in the form of rules and facts, and the system uses that information to make decisions.

It is inspired by biological evolution that seek to improve solutions to problems. It uses genetic algorithms and strategies to automatically create and improve programs or models. It generates a set of possible resources using selection, intersection and mutation factors to evolve. It is useful in solving complex and nonlinear situations where a direct analytical answer cannot be obtained.