Artificial Intelligence - Research Areas

The domain of artificial intelligence is huge in breadth and width. While continuing, we consider the extensively common and prospering exploration areas in the domain of AI −



Speech and Voice Recognition

These both terms are common in robotics, expert systems and natural language processing. Though these terms are utilized interchangeably, their objectives are different.

Speech Recognition Voice Recognition
The speech recognition targets at understanding and comprehending WHAT was spoken. The goal of voice recognition is to recognize WHO is speaking.
It is utilized in hand-free computing, map, or menu navigation. It is utilized to recognize a person by analysing its tone, voice pitch, and accent, etc.
Machine doesn't need training for Speech Recognition as it is not speaker dependent. This recognition system needs training as it is person oriented.
Speaker independent Speech Recognition systems are hard to develop. Speaker dependent Speech Recognition systems are similarly easy to develop.

Working of Speech and Voice Recognition Systems

The user input spoken at a microphone goes to sound card of the system. The converter transforms the analog signal into equivalent digital signal for the speech processing. The database is utilized to compare the sound patterns to recognize the words. Finally, an opposite feedback is given to the database.

This source-language text becomes input to the Translation Engine, which changes over it to the target language text. They are supported with interactive GUI, large database of vocabulary, and so on.

Real Life Applications of Research Areas

There is a large array of applications where AI is serving common people in their everyday lives −

Sr.No. Research Areas Real Life Application

Expert Systems

Examples − Flight-tracking systems, Clinical systems.

Expert Systems Application

Natural Language Processing

Examples: Google Now feature, speech recognition, Automatic voice output.

NLP Application

Neural Networks

Examples − Pattern recognition systems such as face recognition, character recognition, handwriting recognition.

Neural Networks Application


Examples − Industrial robots for moving, spraying, painting, precision checking, drilling, cleaning, covering, cutting, and so on.

Robotics Application

Fuzzy Logic Systems

Examples − Consumer electronics, automobiles, and so on.

Fuzzy Logic Application

Task Classification of AI

The domain of AI is classified into Formal tasks, Mundane tasks, and Expert tasks.



Task Domains of Artificial Intelligence
Mundane (Ordinary) Tasks Formal Tasks Expert Tasks
  • Computer Vision
  • Speech, Voice
  • Mathematics
  • Geometry
  • Logic
  • Integration and Differentiation
  • Engineering
  • Fault Finding
  • Manufacturing
  • Monitoring
Natural Language Processing
  • Understanding
  • Language Generation
  • Language Translation
  • Go
  • Chess (Deep Blue)
  • Ckeckers
Scientific Analysis
Common Sense Verification Financial Analysis
Reasoning Theorem Proving Medical Diagnosis
Planing   Creativity
  • Locomotive

Humans learn mundane (ordinary) tasks since their birth. They learn by recognition, speaking, using language, and locomotives. They learn Formal Tasks and Expert Tasks later, in a specific order.

For humans, the mundane tasks are simplest to learn. The same was considered true before trying to implement mundane tasks in machines. Earlier, all work of AI was concentrated in the mundane task domain.

Later, it turned out that the machine requires more knowledge, complex knowledge representation, and complicated algorithms for handling mundane tasks. This is the reason why AI work is more prospering in the Expert Tasks domain now, as the expert task domain needs expert knowledge without common sense, which can be simpler to represent and handle.

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