There are different types of AI systems that exist within the narrow AI category. The AI Act proposes to regulate the development and use of these, and other, AI systems. Under the AI Act, AI systems will be regulated on a risk-based approach. It is thus helpful to have a basic understanding of the different types of AI systems and how they work. This will make understanding the AI Act easier.
Rule-based AI is an AI system that follows a set of predefined rules to make decisions or take actions. Developers of AI along with experts in a particular field create the rules that the AI system will follow.
Example of rule-based AI:
- Expert systems used in the medical field to diagnose diseases based on symptoms.
Machine learning is an AI system that uses algorithms to learn from data without requiring explicit programming. It allows computers to improve their performance on a specific task by learning from experience.
Examples of machine learning:
- spam filters
- recommendation systems
- fraud detection systems
Deep learning is a type of machine learning which involves artificial neural networks to learn from large datasets, to solve complex problems like natural language processing and image recognition.
Examples of deep learning:
- self-driving cars
- speech recognition systems
- facial recognition systems
Natural language processing
Natural language processing (NLP) is a type of AI that enables computers to understand, interpret, and manipulate human language. Chatbots, virtual assistants, and language translation systems make use of this technology.
Examples of NLP:
- Google Translate
Computer vision is a type of AI that enables computers to interpret and understand visual information from the world around them. Facial recognition, autonomous vehicles, and image and video analysis are some of the applications of this type of AI system.
Examples of computer vision:
- facial recognition systems used by law enforcement
- self-driving cars.
Robotics is a type of AI that involves the use of robots to perform tasks that are too dangerous, difficult, or repetitive for humans. Manufacturing, healthcare, and space exploration are some of the fields that make use of this technology.
Examples of robotics:
- automated assembly lines
- surgical robots
- Mars rovers.
Expert systems are AI systems that mimic the decision-making ability of a human expert in a particular domain. People working in areas such as finance, medicine and engineering use this type of AI system to solve complex problems and make decisions.
Examples of expert systems:
- tax preparation software
- financial planning software.
Cognitive computing is a type of AI that mimics the way the human brain works to solve complex problems. Healthcare, finance, and customer service are some of the fields that make use of this technology.
Examples of cognitive computing:
- IBM’s Watson
- chatbots used in customer service.
Generative AI is a type of AI that creates new content such as images, music, and text.
Examples of generative AI:
- GPT-3 language model
- StyleGAN image generator.
These are some of the major types of AI, and each has its own unique characteristics and applications. As AI continues to evolve and improve, we can expect to see even more types of AI emerge in the future.
While this is exciting, it makes developing future-orientated laws that much more challenging. Lawmakers have the challenge to create laws which retain their applicability and relevance in the future as AI continues to advance.