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AI

AI refers to Artificial Intelligence. The term was first coined in 1956 at a conference at Dartmouth University by, an American Computer and Cognitive Scientist, John McCarthy.


 "AI is the Science and Engineering of making Intelligent Machines" 
-John McCarthy



The idea behind the study has always been to give machine the consciousness and cognitive abilities which include Learning, Understanding, Thinking and Using Experiences. These abilities are the traits of intelligence. 

Major Keywords Timeline:

  • 1943 - Warren S. McCulloch and Walter H. Pitts came up with a model of an Artificial Neuron, a Threshold Logic Unit (M-P Neuron).
  • 1950 - Alan Turing wrote a paper titled - "Computing Machinery and Intelligence". He proposed to consider the question "Can machines think?"   Based on the paper, Turing Test (paper stated the Imitation Game) was invented. It is a simple method of determining whether a machine can demonstrate human intelligence i.e. Can a machine exhibit the intelligent behavior indistinguishable to as that of a human. 
  • 1956 - The term Artificial Intelligence was coined by John McCarthy.
  • 1957 - Perceptron was proposed by, an American psychologist, Frank Rosenblatt. Perceptron is an artificial neuron model similar to M-P neuron but improved by involvement of weights associated with inputs, which improved the neuron model and its efficiency.
  • 1959 - The term Machine Learning(ML) was coined by Arthur Samuel as "the field of study that gives computers the ability to learn without being explicitly programmed. "
  • Mid-2000s - The term Deep Learning(DL) was coined. Deep Learning is a branch of Machine Learning which is completely based on Artificial Neural Networks(ANN). It is to mimic the human brain. Our brain has network of biological neurons that process the data, similar concept has been put to work using artificial neurons. 


Deep Learning leads to Machine Learning and Machine Learning leads to Artificial Intelligence.

Data Science engineering is the process of using data and extracting useful insights from data using variety of tools, algorithms and machine learning fundamentals.


Requirements to be a Machine Learning/Deep Learning Engineer/Data Scientist:

  • Mathematics
    • Algebra
    • Statistics
    • Probability
    • Calculus
  • Computer Programming Fundamentals
  • Any Programming Language(Preferably Python or R)
  • Algorithms and Data Structures
  • Hard Work




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