Expert Systems

Expert systems are artificial intelligence applications designed to emulate human expert decision-making in specific domains (Á. Rocha & S. Anwar, 2019; T. Todorov & J. Stoinov, 2019). These systems solve complex problems by reasoning through knowledge bases, often represented as if-then rules (Á. Rocha & S. Anwar, 2019). They are applied in various fields, including milk quality monitoring and animal health (T. Todorov & J. Stoinov, 2019), as well as information management, organizational models, and software systems (Á. Rocha & Lima, 2018). As technology advances, expert systems are evolving to demonstrate superb decision-making skills and conform to social norms for expertise, behaving more like human experts (Papageorgiou, 2021). This development has implications for various fields, including bioinformatics, and raises questions about the concept of "expert generalist" (Papageorgiou, 2021). The continued research and development of expert systems contribute to their growing capabilities and applications across diverse domains.

Reference:

1. Rocha, Á., & Anwar, S. (2019). The journal of knowledge engineering special issue on WorldCist'17—fifth world conference on information systems and technologies. Expert Systems, 36. https://doi.org/10.1111/exsy.12414
2. Todorov, T., & Stoinov, J. (2019). Expert System for Milk and Animal Monitoring. International Journal of Advanced Computer Science and Applications.https://doi.org/10.14569/ijacsa.2019.0100604
3. Rocha, Á., & Lima, S. (2018). Expert systems: The journal of knowledge engineering special issue on WorldCist'16 ‐ 4th world conference on information systems and technologies. Expert Systems, 35. https://doi.org/10.1111/exsy.12260
4. Papageorgiou, K.G. (2021). Expert Characteristics: Implications for Expert Systems. Advances in experimental medicine and biology, 1338, 155-164 . https://doi.org/10.1007/978-3-030-78775-2_18

Artificial Intelligence

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think and learn like humans. The goal of AI is to develop systems that can perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation.

There are two main types of AI:

1. Narrow AI (Weak AI): This type of AI is designed to perform a specific task or a narrow range of tasks. It operates within a limited context and is not capable of generalizing its knowledge to other domains. Examples include voice assistants like Siri or Alexa, image recognition software, and recommendation algorithms.
2. General AI (Strong AI): This refers to a hypothetical level of AI where the system has the ability to understand, learn, and apply knowledge across a wide range of tasks, similar to a human being. General AI is still largely theoretical and does not currently exist.

AI can be categorized into two main approaches:

1. Symbolic or Rule-based AI: This traditional approach involves programming explicit rules to enable machines to perform specific tasks. However, this method has limitations in handling complex, unstructured data and adapting to new situations.
2. Machine Learning (ML): This approach involves training machines to learn from data. Instead of being explicitly programmed with rules, machines use algorithms that allow them to learn patterns and make predictions or decisions based on the input data. Deep learning, a subset of machine learning, involves neural networks with many layers (deep neural networks) and has been particularly successful in tasks such as image and speech recognition.

Key techniques and subfields within AI include:

• Natural Language Processing (NLP): AI systems that can understand, interpret, and generate human language.
• Computer Vision: AI systems that can interpret and make decisions based on visual data, such as images and videos.
• Robotics: The use of AI to control and enhance the capabilities of robots, allowing them to perform tasks in various environments.
• Expert Systems: Computer systems designed to mimic the decision-making ability of a human expert in a specific domain.
• Reinforcement Learning: A type of machine learning where an agent learns to make decisions by interacting with its environment and receiving feedback in the form of rewards or penalties.
• Ethical AI: The study and implementation of AI systems that adhere to ethical principles and guidelines, addressing concerns such as bias, transparency, and accountability.

AI has a wide range of applications across industries, including healthcare, finance, education, and entertainment. While it holds great promise for improving efficiency and solving complex problems, it also raises ethical and societal challenges that require careful consideration and regulation.