Major Subfields of AI - Knowledge Engineering

 Knowledge engineering is not an important subfield of AI per se, but a process that facilitates other areas such as expert systems. It deals with acquisition, modeling, and management of knowledge required to construct intelligent systems, and as such, constitutes an important part of developing AI applications such as expert systems, which apply the knowledge of a human expert to solve problems. 

What it entails: It involves the design and construction of AI systems, which involves encoding human expert knowledge into a form accessible to the computer.

Key tasks:

• Learning process: Obtaining information about human experts.
• Knowledge representation: Organizing and storing the data in a computer readable format.
• Knowledge deployment: Applying the knowledge to AI system problem solving.

Example applications:

• Expert Systems: An artificial intelligent (AI) application that emulates something that a person who is a professional expert in a given area would make, such as a financial advisor or a medical diagnosis system.
• Other AI applications: Knowledge engineering is also applied in other applications, including machine learning model development or natural language processing applications.

Skills involved:

• Information processing and categorizing.
• Natural language processing.
• Machine learning.
• Systems design.

Role in AI: Knowledge engineering gives knowledge that the AI systems require to be able to function, be intelligent agents, and make decisions. 

Major Subfields of AI - Computer Vision (CV)

 Computer Vision (CV) is a very significant branch of AI that can help machines to see and analyze visual data in form of pictures and videos. Its subfields are object recognition, image classification, and object detection which find application in self-driving cars, medical imaging and security systems. Others include video processing, which includes motion estimation and event detection and video tracking. 

The major fields in computer vision.

• Image and Object Recognition: Object, face and other visual image recognition and classification.
• Object Detection: Moving past classification and locating objects within an image, in addition to classifying them, which may be achieved by drawing bounding boxes around them.
• Image and Video Segmentation: The process of splitting an image or a video into parts in order to identify various objects or areas. This is semantic (labeling pixels with class names) and instance (labeling individual instances of an object) segmentation.
• Motion and Tracking: The process of studying how Objects move over the timeframe of a video and estimating the movement direction of objects, generally applied in tracking objects, or decoding actions.
• 3D and Pose Estimation: Recalling a 3D representation of a scene or 3D locating an object or a person.
• Image Restoration: Restoration or repairing damaged or noisy images to enhance the quality of an image. 

Common applications

• Autonomous Vehicles: The idea of autonomous cars is to allow cars to sense the world around them by identifying other cars, pedestrians, and road signs.
• Healthcare: Helping with the analysis of medical imaging, e.g. by detecting tumors in CT or diseases in X-rays.
• Robotics: Giving robots visual abilities in performing such tasks as inventory management, defects and route-finding.
• Security and Surveillance: Easily the facial recognition feature is provided, the anomalous behavior is detected and monitored over a large area.
• Manufacturing: It is used to automatically check the products to help to check defects and assume product quality.
• Augmented and Virtual Reality (AR/VR): following and placing the virtual objects in the real world.

Types of AI: Narrow AI vs General AI vs Super AI

Key components of AI

Intelligence has a broader context that reflects a deeper capability to comprehend the surroundings. However, for it to qualify as AI, all its components need to work in conjunction with each other. Let’s understand the key components of AI.

Machine learning: Machine learning is an AI application that automatically learns and improves from previous sets of experiences without the requirement for explicit programming.

Deep learning: Deep learning is a subset of ML that learns by processing data with the help of artificial neural networks.

Neural network: Neural networks are computer systems that are loosely modeled on neural connections in the human brain and enable deep learning.

Cognitive computing: Cognitive computing aims to recreate the human thought process in a computer model. It seeks to imitate and improve the interaction between humans and machines by understanding human language and the meaning of images.

Natural language processing (NLP): NLP is a tool that allows computers to comprehend, recognize, interpret, and produce human language and speech.

Computer vision: Computer vision employs deep learning and pattern identification to interpret image content (graphs, tables, PDF pictures, and videos).

Types of AI

Artificial Intelligence can be broadly divided into two categories: AI based on capability and AI based on functionality. Let’s understand each type in detail.

1. Narrow AI

Narrow AI is a goal-oriented AI trained to perform a specific task. The machine intelligence that we witness all around us today is a form of narrow AI. Examples of narrow AI include Apple’s Siri and IBM’s Watson supercomputer.

Narrow AI is also referred to as weak AI as it operates within a limited and pre-defined set of parameters, constraints, and contexts. For example, use cases such as Netflix recommendations, purchase suggestions on ecommerce sites, autonomous cars, and speech & image recognition fall under the narrow AI category.

2. General AI

General AI is an AI version that performs any intellectual task with a human-like efficiency. The objective of general AI is to design a system capable of thinking for itself just like humans do. Currently, general AI is still under research, and efforts are being made to develop machines that have enhanced cognitive capabilities.

3. Super AI

Super AI is the AI version that surpasses human intelligence and can perform any task better than a human. Capabilities of a machine with super AI include thinking, reasoning, solving a puzzle, making judgments, learning, and communicating on its own. Today, super AI is a hypothetical concept but represents the future of AI.

Now, let’s understand the types of AI based on functionality.

4. Reactive machines

Reactive machines are basic AI types that do not store past experiences or memories for future actions. Such systems zero in on current scenarios and react to them based on the best possible action. Popular examples of reactive machines include IBM’s Deep Blue system and Google’s AlphaGo.

5. Limited memory machines

Limited memory machines can store and use past experiences or data for a short period of time. For example, a self-driving car can store the speeds of vehicles in its vicinity, their respective distances, speed limits, and other relevant information for it to navigate through the traffic.

6. Theory of mind

Theory of mind refers to the type of AI that can understand human emotions and beliefs and socially interact like humans. This AI type has not yet been developed but is in contention for the future.

7. Self-aware AI

Self-aware AI deals with super-intelligent machines with their consciousness, sentiments, emotions, and beliefs. Such systems are expected to be smarter than a human mind and may outperform us in assigned tasks. Self-aware AI is still a distant reality, but efforts are being made in this direction.