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Intelligent Systems

[The Components of Intelligence - Tutorialspoint]


- Overview

In a technical sense, intelligent systems are technologically advanced machines that perceive and respond to the world around them. They can take many forms, from automated vacuums to facial recognition programs. Intelligent systems are based on neural networks, fuzzy logic, or evolutionary algorithms. 

 Here's some more information about intelligent systems and expert systems:

  • Intelligent systems: These are advanced machines that can perceive and respond to their environment. 
  • Expert systems: These are computer programs that use artificial intelligence (AI) to simulate the judgment and behavior of a human or an organization with expertise in a particular field. Expert systems are designed to solve complex problems using a set of rules or algorithms that can mimic human reasoning processes. 


- The Five Categories of Expert Systems

Expert systems (ESs) and intelligent systems are both types of computer programs.

ESs are computer programs that use artificial intelligence (AI) to simulate the decision-making ability of human experts. They are designed to solve complex problems by reasoning through knowledge bases, mainly as if–then rules. Expert systems are intended to complement, not replace, human experts. 

Expert systems can be classified into five categories:

  • Rule-based expert systems
  • Frame-based expert systems
  • Neural expert systems
  • Fuzzy expert systems
  • Neuro-fuzzy expert systems


- The Main Areas Within Intelligent Systems

Artificial intelligence (AI) is defined as the ability of digital computers or computer-controlled robots to perform tasks usually associated with intelligent beings. AI is also defined as,

  • Intelligent entities created by humans.
  • Ability to perform tasks intelligently without explicit instructions.
  • Ability to think and act rationally and humanely.


Intelligent systems are technologically advanced machines that sense and respond to the world around them. Smart systems can come in many forms, from autonomous vacuum cleaners like the Roomba to facial recognition programs to Amazon's personalized shopping recommendations.

Two major areas in intelligent systems are: how machines perceive their environment and how those machines interact with the environment.

One way an intelligent system perceives its environment is through vision. The study of how computers understand and interpret visual information from still images and video sequences arose in the late 1950s and early 1960s. 

It has grown into a powerful technology that is at the heart of the country's industrial, commercial and government sectors. Key factors contributing to this growth are exponential increases in processor speed and memory capacity, as well as advances in algorithms.

- Intelligent Systems

Intelligent systems are the same thing as AI. AI is a more popular term, but intelligent systems is an appropriate term.  

The field of intelligent systems also focuses on how these systems interact with human users in ever-changing and dynamic physical and social environments. 

Early robots had little autonomy in making decisions: they assumed a predictable world and made the same actions over and over again under the same conditions. Today, a robot is considered an autonomous system that senses its environment and can act in the physical world to achieve certain goals.

Intelligent systems are poised to fill a growing number of roles in today's society, including:

  • Artificial Intelligence 
  • Machine Vision
  • Robotics  
  • Ambient Intelligence


- AI Technologies

AI can automate repetitive tasks, improve efficiency and productivity, and provide valuable insights for decision-making. AI can also process and analyze large amounts of data quickly, making it easier to find and access information.

Essentially, AI is the wider concept of machines being able to carry out tasks in a way that could be considered “smart”. In the broadest sense, AI refers to machines that can learn, reason, and act for themselves. 

They can make their own decisions when faced with new situations, in the same way that humans and animals can. If a machine can solve problems, complete a task, or exhibit other cognitive functions that humans can, then we refer to it as having artificial intelligence. 

There are various ways to create AI, depending on what we want to achieve with it and how we will measure its success. It ranges from extremely rare and complex systems, such as self-driving cars and robotics, to parts of our everyday lives, such as facial recognition, machine translation, and email categorization. 

The path you choose will depend on what your AI goals are and how well you understand the intricacies and feasibility of various approaches. 

AI technologies are categorized according to their ability to mimic human traits, the techniques they use to do so, their real-world applications, and theory of mind. Using these characteristics as a reference, all AI systems — real and hypothetical — fall into one of three categories: Narrow artificial intelligence (ANI), with a narrow range of capabilities; Artificial General Intelligence (AGI) comparable to human capabilities; or Artificial Superintelligence (ASI), more capable than humans. 

Some other AI technologies include:
  • Machine Learning
  • Deep Learning
  • Neural Networks
  • Fuzzy Logic
  • Expert Systems
  • Computational Intelligence
  • Natural Language Processing
  • Data Mining
  • Neuromorphic systems
  • Biometrics
  • Sentiment Analysis


[Zebras and wildebeest Mount Kilimanjaro, Africa]

- Machine Vision 

Machine vision is a technology and method used to provide imaging-based automated inspection and analysis for applications such as automated inspection, process control, and robotic guidance, typically used in industry. Machine vision refers to many technologies, software and hardware products, integrated systems, actions, methods and expertise.

Machine vision as a systems engineering discipline can be thought of as distinct from computer vision, which is a form of computer science. It attempts to integrate existing technologies in new ways and apply them to solve real-world problems. The term is a common term for these functions in the context of industrial automation, but is also used for these functions in vehicle guidance in other environments.

The entire machine vision process includes planning the requirements and details of the project, and then creating the solution. At runtime, the process begins with imaging, and then automatically analyzes the images and extracts the required information. 

  • Human Computer Interaction
  • Pattern Recognition
  • Image/Video Processing
  • Intrusion Detection
  • Brain-Machine Interface
  • Geographic Information Systems
  • Signal Processing
  • Medical Diagnosis
  • Segmentation Techniques
  • Augmented/Virtual Reality


- Robotics  

Robotics, the design, construction and use of machines (robots) to perform tasks traditionally performed by humans. Robots are widely used in industries such as automobile manufacturing to perform simple repetitive tasks, as well as in industries where work must be performed in environments that are harmful to humans. 

Many aspects of robotics involve artificial intelligence; robots may have the same senses as humans, such as sight, touch, and the ability to sense temperature. Some are even capable of making simple decisions, and current robotics research is working toward designing robots with a degree of self-sufficiency, allowing movement and decision-making in unstructured environments. 

Today's industrial robots are not like humans. Robots in human form are called androids.

  • Humanoid Robots
  •  Space and underwater robots
  •  Assistive Robots
  •  Mobile Robots
  •  Autonomous Robots
  •  Human-Robot Interaction
  •  Telerobotics
  •  Walking and Climbing Robots
  •  Robotic Automation
  •  Robot Localization and Map Building


- Ambient Intelligence  

Ambient Intelligence (AmI) is a technology that combines Artificial Intelligence (AI), Internet of Things (IoT), Big Data, Pervasive Computing, Networking and Human-Computer Interaction (HCI). 

This technology provides an ideal combination of IoT and artificial intelligence. IoT devices and sensors implanted in the user's surroundings, such as their home and office, will collect contextual data and leverage artificial intelligence to predict the user's needs.

  • Smart Cities
  • Internet of Things
  • Ambient Assisted Living
  • Smart Healthcare
  • Intelligent Transportation
  • Data Science
  • Sensing and Sensor Networks
  • Affective computing
  • Agents and Multi-agent Systems
  • Context-aware pervasive systems


- Challenges in Intelligent Systems

Research in intelligent systems faces numerous challenges, many of which relate to representing a dynamic physical world computationally. 

  • Uncertainty: Physical sensors/effectors provide limited, noisy and inaccurate information/action. Therefore, any actions the system takes may be incorrect both due to noise in the sensors and due to the limitations in executing those actions.
  • Dynamic world: The physical world changes continuously, requiring that decisions be made at fast time scales to accommodate for the changes in the environment.
  • Time-consuming computation: Searching for the optimal path to a goal requires extensive search through a very large state space, which is computationally expensive. The drawback of spending too much time on computation is that the world may change in the meantime, thus rendering the computed plan obsolete.
  • Mapping: A lot of information is lost in the transformation from the 3D world to the 2D world. Computer vision must deal with challenges including changes in perspective, lighting and scale; background clutter or motion; and grouping items with intra/inter-class variation.



[More to come ...]

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