By Lance MacArthur
Artificial intelligence refers to systems that mimic human intelligence to perform tasks and can be improved based on the information they collect. So the AI can come up with a whole story for us, or even generate a picture of the scene we need from a simple text query, for example, teamwork images.
Among the various forms that this technology can take, three types of AI include descriptive, predictive, and prescriptive AI, each of which plays a unique role in data analysis and generation.
By itself, artificial intelligence technology uses complex algorithms, so in order to create an image through Depositphotos AI image generator, it processes a huge number of all possible patterns in a few seconds and is based on really huge amounts of data.
Different AI classes: what is their difference?
Artificial intelligence can be divided into different categories, each of which differs in its capabilities, application, and complexity of the tasks to be solved. These classes are often grouped into three broad categories: strong AI, weak AI, and hybrid AI.
Strong AI (or General AI)
Strong AI represents the most advanced level of artificial intelligence capable of understanding and performing any intellectual task that a human can perform. It has the ability to reason, plan, learn, communicate, and make decisions autonomously in various contexts, without being limited to a specific set of pre-programmed tasks.
Weak AI (or specific AI)
Weak AI, also called applied AI or narrow AI, is designed to perform specific tasks in certain areas. It operates under a number of constraints and limitations, concentrating on a single task or group of tasks with high efficiency. This category includes most of the artificial intelligence systems that we use today, such as voice assistants, recommendation systems, and so on.
Hybrid AI
This class of AI combines elements of strong AI and weak AI, using the flexibility and adaptability of strong AI with the specialization and effectiveness of weak AI. Hybrid AI is designed to perform complex tasks by adapting its functionality to specific applications, which makes it especially useful in industries where close interaction between different AI capabilities is required.
3 AI types
Descriptive AI
Descriptive AI is designed precisely to do what its name suggests: describe and analyze large amounts of data in order to draw meaningful conclusions. This type of artificial intelligence is often the first level of interaction between raw data and solutions based on data processing and analysis. It represents one of the various types of AI that facilitates the transformation of extensive raw knowledge into practical ideas.
Using advanced statistical techniques, descriptive AI, one of three types of AI, analyzes historical data to identify trends, patterns, or anomalies. This process, which is a form of intelligence based on data analysis, begins with collecting data and then cleaning and structuring it.
Once the data is ready, the AI applies statistical models to create understandable reports, interactive dashboards, and even graphical visualizations summarizing the extracted information.
Usage examples
- Analysis of sales data. You can use descriptive AI to analyze past sales figures, identify the best and worst products, and understand seasonal fluctuations or trends in your market.
- Detecting anomalies in financial data. In the financial sector, this AI helps identify unusual transactions that may indicate errors, fraud, or security breaches.
- Social network performance analytics. Brands are turning to descriptive artificial intelligence to measure user engagement, track brand mentions, and evaluate the effectiveness of advertising campaigns across platforms.
Predictive AI
Among the various types of AI, there is a predictive AI. Predictive AI is one of three types of AI that focuses on using models and algorithms to predict outcomes and identify future trends. This branch of artificial intelligence, often based on machine learning, is necessary for organizations that want to make decisions based on reliable and accurate forecasts obtained as a result of processing large amounts of data.
Usage examples
- Demand forecasting: Applications of this type help businesses anticipate future demand for their products, thereby optimizing inventory management and production planning.
- Forecasting customer behavior. Predictive AI analyzes customer interactions and behaviors to gain insight into future customer actions, helping to personalize marketing campaigns.
- Predicting failures and technical problems: It is also used to predict failures before they occur, which allows preventive measures to be taken to minimize downtime and maintenance costs.
Prescriptive AI
Finally, among the various types of AI, we find prescriptive AI. This is an advanced type of artificial intelligence, the main purpose of which is to create new data or “tokens” based on previous training.
This type of AI does not just predict or analyze, but acts by suggesting actions or generating new content that can be used for various practical applications, including image recognition. Prescriptive AI learns from huge amounts of existing data to create new content that looks or is functionally similar to that data.
For example, by analyzing thousands of photos, it can generate completely new images that look just as real. This capability makes it extremely useful for tasks such as creating artistic content, writing texts, or even developing voice assistants capable of generating dynamic responses.
Generative AI: a subcategory of prescriptive AI
Generative AI, considered a specialization of prescriptive AI, stands out for its ability to automatically create new content without external interference. While prescriptive AI combines prediction, analysis, and generation functions to provide recommendations and actions, generative AI focuses solely on content creation.
This type of AI uses deep learning models to create elements that are not directly derived from input data but are inspired by it, such as creating new images, music, or writing. He is especially appreciated for his ability to innovate and enrich creative fields, thus offering almost unlimited possibilities for applications that require original and interesting content.
Usage examples
- Content writing: Artificial intelligence-based tools such as ChatGPT, used for example in digital marketing, can compose articles, marketing emails, or even entire scripts that are both relevant and interesting.
- Code generation. Help developers create code faster by suggesting strings or correcting errors.
- Idea generation: It can offer creative suggestions for the development of new products or marketing campaigns.
- Create visual images and videos: Convert text descriptions into images or animations for use in marketing and advertising.
Conclusion
The importance of this distinction lies in understanding the application of AI in different contexts: although classes can be considered as levels of technological development of AI, types are more focused on practical applications in real-world scenarios.
