Artificial Intelligence

Artificial Intelligence enables computer to think.First coined in 1956 by John McCarthy, Artificial Intelligence involves ” machines that can

perform tasks that are characteristic of human intelligence”.

AI is a sub? field of computer science aimed at the development of computers capable of doing things that are normally done by people?—?in particular, things associated with people acting intelligently.

Goals of Artificial Intelligence

To Create Expert Systems: The systems which exhibit intelligent behavior, learn, demonstrate, explain, and advice its users.

To Implement Human Intelligence in Machines: Creating systems that understand, think, learn, and behave like humans.

Categories of Artificial Intelligence

General AI or Strong AI would have all of the characteristics of human intelligence.

Narrow AI or Weak AI exhibits some facet(s) of human intelligence, and can do that facet extremely well, but is lacking in other areas.

While studying artificial intelligence, you need to know what intelligence is

So What is Intelligence?

The ability of a system to calculate, reason, perceive relationships and analogies, learn from experience, store and retrieve information from memory, solve problems, comprehend complex ideas, use natural language fluently, classify, generalize, and adapt new situations.

Machine Learning

Machine learning (ML) is a category of algorithm that allows software applications to become more accurate in predicting outcomes without being explicitly programmed. The basic premise of machine learning is to build algorithms that can receive input data and use stastical analsis to predict an output while updating outputs as new data becomes available.

Deep Learning

Deep Learning is an artificial intelligence function that imitates the workings of the human brain in processing data and creating patterns for use in decision making. Deep learning is a subset of machine learning AI that has networks capable of learning unsupervised from data that is unstructured or unlabeled.Also known as Deep Neural Learning or Deep Neural Network.

Why Artificial Intelligence Is a Great Career Option

Mostly Used Algorithms in Artificial Intelligence

1.Decision Trees: A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance-event outcomes, resource costs, and utility. Take a look at the image to get a sense of how it looks like.From a business decision point of view, a decision tree is the minimum number of yes/no questions that one has to ask, to assess the probability of making a correct decision, most of the time. As a method, it allows you to approach the problem in a structured and systematic way to arrive at a logical conclusion.

Decision Trees

2. Naive Bayes Classification: Naive Bayes classifiers are a family of simple probabilistic classifiers based on applying Bayes’ theorem with strong (naive) independence assumptions between the features. The featured image is the equation?—?with P(A|B) is posterior probability, P(B|A) is likelihood, P(A) is class prior probability, and P(B) is predictor prior probability.

Some of real world examples are:

•To mark an email as spam or not spam

•Used for face recognition software

3. Ordinary Least Squares Regression: If you know statistics, you probably have heard of linear regression before. Least squares is a method for performing linear regression. You can think of linear regression as the task of fitting a straight line through a set of points. There are multiple possible strategies to do this, and “ordinary least squares” strategy go like this?—?You can draw a line, and then for each of the data points, measure the vertical distance between the point and the line, and add these up; the fitted line would be the one where this sum of distances is as small as possible.

Linear refers the kind of model you are using to fit the data, while least squares refers to the kind of error metric you are minimizing over.

4. Logistic Regression: Logistic regression is a powerful statistical way of modeling a binomial outcome with one or more explanatory variables. It measures the relationship between the categorical dependent variable and one or more independent variables by estimating probabilities using a logistic function, which is the cumulative logistic distribution.

In general, regressions can be used in real-world applications such as:

•Credit Scoring

•Measuring the success rates of marketing campaigns

•Predicting the revenues of a certain product

5.Artificial Neural Networks (ANNs): Neural Networks is the most commonly used term in Artificial Intelligence.Yet another research area in AI, neural networks, is inspired from the natural neural network of human nervous system.

Okay So What are Artificial Neural Networks (ANNs)?

The inventor of the first neurocomputer, Dr. Robert Hecht-Nielsen, defines a neural network as

“A computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs.”

Basic Structure of ANNs

The idea of ANNs is based on the belief that working of human brain by making the right connections, can be imitated using silicon and wires as living neurons and dendrites.The human brain is composed of 86 billion nerve cells called neurons. They are connected to other thousand cells by Axons. Stimuli from external environment or inputs from sensory organs are accepted by dendrites. These inputs create electric impulses, which quickly travel through the neural network. A neuron can then send the message to other neuron to handle the issue or does not send it forward.

Good Old-Fashioned Artificial Intelligence