Different types of AI: distinctions and applications

After several years of research, AI has evolved rapidly, taking a prominent place in the business world. Many companies are now integrating AI-based solutions to improve their performance, make informed decisions, or create personalized experiences for customers. However, artificial intelligence is still a broad field, with different types capable of meeting specific business needs. In this article, Lemon Learning aims to introduce you to the three main types of artificial intelligence used by companies. We will also help you understand their differences, capabilities, and limitations.

Rule-Based AI

Considered the simplest form of artificial intelligence, rule-based AI relies on predetermined instructions to solve very specific problems. Typically, these rules are created by humans with proven experience in the relevant field. While rule-based AI is limited in its ability to adapt to new situations, it is an effective tool for accomplishing tasks where the rules are clearly established.

How Do Rule-Based Systems Work?

This method of data processing uses “If this, then that” logic to select known information, process it, and present a result. Easy to build, these systems are based on principles established from lines of code. For example, a bank that regularly receives loan applications can use a rules-based system to approve or reject a loan. Chatbots that answer customer questions also use this technology. 

Understanding, modifying, and maintaining rule-based AI is straightforward. In most cases, rules are stored in a database and can be accessed at any time.

Difference Between Expert Systems and Machine Learning

In artificial intelligence, expert systems are applications capable of reproducing human reasoning. Based on known information and rules implemented from the start, they can draw conclusions and explain the results obtained.

In contrast, machine learning systems examine large amounts of data to make informed decisions. This system is ideal for finding patterns in data, even those not initially apparent. However, machine learning models can only assimilate data that has already been processed. Machine learning includes different subcategories, such as deep learning, which uses neural networks to solve complex problems through supervised and unsupervised learning.

While expert systems allow for problem resolution based on precise rules, machine learning is a method that improves performance over time. Unlike expert systems, which reproduce human decisions based on established rules, machine learning models enhance their abilities through continuous learning.

Weak (or Narrow) AI

This form of artificial intelligence refers to AI systems specialized in very specific tasks. Designed to perform a single task efficiently, these systems do not have the learning capacity or the ability to understand the general context, as humans do.

While narrow or weak AI is now part of everyday life, its operation relies on advanced algorithms that can solve specific problems. This problem-solving ability does not imply the use of all the skills of the human brain. Therefore, weak AI cannot become self-aware.

Limitations of Narrow AI

Weak AI can be exposed to risks of system failure. For example, autonomous cars can make poor assessments of situations, potentially causing accidents. Similarly, the risks of malicious use of this technology cannot be ruled out.

The automation of certain tasks may lead to the elimination of many jobs. However, supporters of artificial intelligence believe that the emergence of this technology will generate new jobs.

Technologies Working with Weak Artificial Intelligence

The idea of robots conquering the world is still science fiction. However, the use of narrow artificial intelligence has become widespread. Recommendation systems on e-commerce sites and applications like Netflix use narrow AI to suggest content based on user history.

Search engines and virtual assistants like Siri or Alexa also operate using weak AI. Similarly, it is used for natural language processing and voice recognition systems like Google Translate. Image recognition has become democratized, aiding radiologists in interpreting medical images. In the business world, narrow AI is used to analyze data and improve risk management. Machines in industries and drones perform tasks for which they were programmed using narrow AI.

Narrow AI also finds its place in predictive analytics, studying historical data using machine learning and algorithms. Marketing experts commonly use this technique for decision-making.

Strong (or General) AI

Strong artificial intelligence is a theoretical form of AI. If research allows its development, machines will possess knowledge similar to humans. They will be self-aware, capable of solving problems, planning for the future, and learning. The goal of strong AI is to create intelligent machines that are indistinguishable from human intelligence. However, they will need to learn from various experiences, progressing and improving their abilities over time.

This level of artificial intelligence is still largely theoretical, despite significant progress in machine learning and neural networks. While general AI is an exciting prospect, it should not be confused with generative artificial intelligence, which focuses on creating new data sets (text, music, images, videos, etc.).

Current Status of Strong Artificial Intelligence Development

To date, strong AI remains an ambitious and speculative goal. Its realization raises various ethical questions, and its practical implementation is challenging due to the complexity and variety of intellectual tasks humans perform.

Nevertheless, significant advances in artificial intelligence and machine learning are enabling AI development in various sectors. Some people are optimistic about the potential achievements through AI, while others believe such systems cannot be developed.

Trends in Strong Artificial Intelligence

There are still no clear examples of general AI. However, the field is constantly evolving, with theories such as artificial superintelligence emerging. Though it is expected to far surpass human capabilities, it remains purely speculative.

AI plays an important role in many areas, starting with cybersecurity, by improving the detection of security breaches and risk analysis. It also contributes to content creation, whether writing text or generating images. Prediction algorithms enhance AI’s capabilities in areas such as weather forecasting, stock market predictions, and human behavior analysis.

Conclusion

Artificial intelligence offers numerous opportunities for businesses. Different types of AI are adapted to perform specific tasks or meet particular needs. Rule-based AI automates simple and repetitive processes, while weak AI performs specific tasks efficiently. Strong AI aspires to simulate human knowledge, raising many ethical questions. Each AI model has its advantages and limitations. When integrating them into their operations, companies must thoroughly understand the current and future roles of artificial intelligence. This understanding will enable them to leverage AI’s potential and remain competitive in their fields. Clear definitions of AI tasks will ensure that all employees in the company embrace this new technology.