Artificial intelligence (AI) is broadly
defined as the simulation of human intelligence processes by computers. The processes encompass:
- Learning: Acquisition of information, and the rules for using the information.
- Reasoning: Application of rules to reach probable conclusions.
- Self-correction.
Machine learning is a branch of artificial intelligence concerned with the
development of systems that can learn from empirical data. These systems learn to recognize complex
patterns and perform tasks based on these capabilities.
Optical character recognition (OCR) and
natural language processing are examples of machine learning. Printed
characters are recognized automatically, in OCR systems, based on previous
examples. A machine learning system could also be trained to classify different
types of transactions (e.g., identify fraudulent transactions) or emails (e.g.
distinguish spam).
Deep learning (aka deep structured learning or hierarchical learning) is a
branch of machine learning where knowledge is structured as a neural network. Algorithms learn as they
sift deeper down the layers of the network, progressively accumulating and triangulating knowledge
gleaned from nodes in the outer layers.
Applications of AI in its various forms include facial and speech recognition,
computer vision, natural language processing, optical character recognition (OCR), analysis of social
networks, analysis of medical data and images, material inspection, game playing, for instance, board
games like chess, and quiz shows like Jeopardy.
For many of these applications, the performance of artificial intelligence has
developed to such an extent that it surpasses natural intelligence, i.e., expert systems are getting smarter
than human experts.
Pattern Recognition
Pattern recognition is a set of machine learning techniques that classify raw data according to a
specific logic or learning procedure. Broadly speaking there are two types of
learning procedures — supervised learning and unsupervised learning.
Supervised learning uses
training data that consists of a set of pre-labelled instances. Each instance
is formally described by a vector of features, which together constitute a
description of all known characteristics of the instance. The learning
procedure generates a model that attempts to perform as well as possible on the
training data, and generalize as well as possible to new data.
Unsupervised learning,
on the other hand, works without pre-labelled training data, to find inherent
patterns in the data that can then be used to classify data instances.
Classification of data based on unsupervised learning is normally known as
clustering.
Neural networks
Neural network (aka artificial neural network) is a nonlinear predictive model that learns through
training, and resembles a biological neural network in structure. It can be
used for pattern recognition and optimization.
Like other machine learning methods (i.e., systems
that learn from data) neural networks have been used to solve a wide variety of
tasks including computer vision and speech recognition, classification of
customers (e.g., identifying high-risk, high-value customers) or classification
of transactions (e.g., fraudulent insurance claims).
Natural Language Processing (NLP)
Natural language processing is a branch of artificial intelligence (more specifically machine learning) and
linguistics that deals with the ability of computers to understand natural
languages; i.e., enabling computers to derive meaning from human or natural
language input. It relates to the area of human-computer interaction. A common
application in marketing is use of sentiment analysis on the web to determine
how people feel about a particular subject; e.g., a brand, a company or an
individual.
Sentiment Analysis
Sentiment analysis uses NLP and other analytic techniques to identify and extract subjective
information from textual content such as consumer generated media (social media). It seeks content relating to objects of interest, determines the
polarity (favourable, neutral, negative) of the content, and assesses the
intensity of the sentiment. The net advocacy index described in
Brand Equity, is one example of sentiment analysis
to assess how netizens feel about brands.
Ensemble Learning
Ensemble learning is the process by
which multiple predictive models, such as classifiers, are combined to improve
the predictive performance of the constituent models.