What is an AI technique?

AI problem solving involves complexity. To handle complex problems, the techniques need to have the capability to deliver acceptable solutions. Some of the common AI techniques in use are the following:

  1. Learning – Learning enables to perform correctly and efficiently an already encountered problem in the past that have been solved. In a game of chess between a human and a computer, if the computer encounters a similar board position and a move by the player which led to its defeat, the computer would have learnt from its mistake and try to circumvent the situation by playing a different game so as not to repeat his failure.
  2. Heuristics – Heuristics are intelligent guesses. When an AI chess program is your opponent in a game of chess, the intelligent program must be able to infer correctly (intelligent guess) the move that you would make …
  3. Decision making under uncertainty – Driving requires reasoning/decision making under uncertain situations by the driver. A driverless car should be equipped with similar capabilities to move on the roads. AI techniques such as Fuzzy logic are often used for arriving at decisions under uncertain situations.
  4. Natural Language Processing (NLP) – Text Mining and Analytics techniques, Sentiment Analysis and NLP techniques at word, sentence, paragraph and document level, the interpretation of unstructured text data at syntax and semantic level, effective speech recognition and synthesis methodology are essential for AI systems.
  5. Embed Common Sense– Humans have an innate ability of logical reasoning. If an individual is a Ph. D. it is common sense that the person possesses an under-graduate and post-graduate degree. Embedding machines with common sense reasoning is a non-trivial task.
  6. Knowledge Based (Logic) Reasoning – Deriving knowledge / inference from a given set of facts. Feeding parametric values of a patient into medical Expert Systems may result in proper diagnosis of the ailment and enable right prescription of drugs.
  7. Swarm intelligence – For combinatorial problems i.e., problems having a large number of possible alternatives, collective intelligence in the bird and animal kingdom such as ants (ant colony optimization) and birds (particle swarm optimization) for arriving at optimized values need be incorporated for problem solving by AI systems.
  8. Case Based Reasoning (CBR) – CBR systems make use of knowledge/experience gained in the past for current problem solving.
  9. Predictive ability – Neural Networks, Statistical techniques like Regression and K-Nearest Neighbor, Bayesian Classification enable good prediction and has wide applications …
  10. Soft Computing – The ability to perform computation with imprecise data using Genetic Algorithms, Fuzzy Logic and Neural Networks or a combination of them forming hybrid techniques are widely used in several AI problems …
  11. Fault Tolerance – In many situations, humans are able to interpret intelligently even from wrong answers. As an example, India has many linguistic groups with similar surnames. There have been instances when people kept referring to ‘Mr. Das’ and ‘Mr. Bose’ and I had to infer that they were actually referring to me – Ghosh. The three surnames are mostly seen in the Bengali community and others tend to err while addressing… such capabilities need to be incorporated into AI systems…

The above non-exhaustive listing is a pointer to the widely used AI methods and techniques in recent times…


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