Skip to main content

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




Labels:

Popular posts from this blog

Coding Problem: Area of Circle

 Write a program to print the area of a circle of given radius ' r ' up to 2 decimal places. Code: Output:
Read more

Image to Pencil Sketch using Python and OpenCV

In this post, we will go through a program to get a pencil sketch from an image using python and OpenCV.  Step 1:  To use OpenCV, import the library. Step 2: Read the Image. Step 3: Create a new image by converting the original image to grayscale. Step 4: Invert the grayscale image. We can invert images simply by subtracting from 255, as grayscale images are 8 bit images or have a maximum of 256 tones. Step 5: Blur the inverted image using GaussianBlur  method in OpenCV library and invert the blurred image.  Step 6: Divide the grayscale values of the image by the values of image received from step-5 ( Note: We inverted the grayscale image and we blurred this image and then again inverted it ). Diving an image from its smoothened form will highlight the edges and we get the image like Pencil Sketch. Steps Illustration: Code: Execution Output:
Read more

Coding Problem: Based on Caesar Cypher

This a coding problem based on Caesar Cypher(or Cipher). Caesar Cipher Technique is a simple and easy method of encryption. It is a simple type of substitution cipher in which each letter of plain text is replaced by a letter with some fixed number of positions down with alphabet. This is used in a way: Given: a=z  b=y ............ z=a For example: Input: abc Output should Be:  zyx Code:
Read more