AI applications and methodologies

Question 1

Chatbots

Answer 1

Chatbot is one of the applications of AI which simulate conversation
with humans either through voice commands or through text chats or
both.
We can define Chatbot as an AI application that can imitate a real
conversation with a user in their natural language.
They enable communication via text or audio on websites, messaging
applications, mobile apps, or telephones.

Question 2

Types of chatbots

Answer 2

• Rule based
  Machine based

Question 3

Rule based chatbots

Answer 3

This is the simpler form of Chatbot which follows a set of pre-defined rules in
responding to user’s questions.
* For example, a Chatbot installed at a school reception area,
- Rule based Chatbot can only answer questions that it is trained to. If you ask a question
that it is not trained, it will not be able to answer

Question 4

AI based chatbots

Answer 4

Such Chatbots are advanced
forms of chatter-bots capable
of holding complex
conversations in realtime.
They process the questions
(using neural network layers)
before responding to them. AI
based Chatbots also learn
from previous experience and
reinforced learning and it
keeps on evolving.
- . AI-based or NLP-based bot identifies the language, context
and intent and then it reacts accordingly. A rule-based bot only
understands a pre-defined set of options.

Question 5

NLP

Answer 5

• The technology which enables the machines (software) to understand
  and process the natural language (of humans), is called natural language
  processing (NLP).
• it is defined as branch of Artificial
  Intelligence that deals with the interaction between computers and
  humans using the natural language
• NLP is a
  sub – area of Artificial Intelligence deals with the capability of software to
  process and analyse human language, both verbal and written language

Question 6

Natural language processing has found applications

Answer 6

• Text Recognition (in an image or a video)
• Camera / machine captures the image of number plate, the image is transferred to
  neural network layer of NLP application, NLP extracts the vehicle’s number from
  the image. However, correct extraction of data also depends on the quality of
  image
• Voice recognition
• Speech processing
• Summarization
• Information extraction

Question 7

how is NLP used?

Answer 7

NLP is natural language processing and can be used in scenarios
where static or predefined answers, options and questions may not
work. In fact, if you want to understand the intent and context of the
user, then it is advisable to use NLP.

Question 8

Summarization by NLP

Answer 8

NLP not only can read and understand the paragraphs or full article
but can summarize the article into a shorter narrative without
changing the meaning. It can create the abstract of entire article.
There are two ways in which summarization takes place – one in
which key phrases are extracted from the document and combined to
form a summary (extraction-based summarization) and the other in
which the source document is shortened (abstraction-based
summarization).

Question 9

Information Extraction

Answer 9

Information extraction is the technology of finding a specific information in a document
or searching the document itself. It automatically extracts structured information such
as entities, relationships between entities, and attributes describing entities from
unstructured sources.

Question 10

Speech processing

Answer 10

• The ability of a computer to hear human speech and analyse and
  understand the content is called speech processing.

Question 11

What happens when we speak to our device?

Answer 11

The microphones of the device hears our audio and plots the graphs of our
sound frequencies. As light-wave has a standard frequency for each colour,
so does sound. Every sound (phonetics) has a unique frequency graph. This
is how NLP recognizes each sound and composes an individual’s words and
sentences

Question 12

CV

Answer 12

It is a subfield of AI and involves extraction of information
from digital images like the videos and photographs,
analysing and understanding the content.

Question 13

Applications of CV

Answer 13

• Object detection
• Optical Character Recognition
• Fingerprint Recognition

Question 14

How does computer see an
image?

Answer 14

• The computer sees images as a matrix of 2-dimensional array (or threedimensional array in case of a colour image).
• The above image is a grayscale image, which means each value in the 2D
  matrix represents the brightness of the pixels. The number in the matrix
  ranges between 0 to 255, wherein 0 represents black and 255 represents
  white and the values between them is a shade of grey.
• If we have to represent
  the above locomotive image in colour, the 3D matrix will be 9x9x3.
  Each pixel in this colour image has three numbers (ranging from 0 to
  255) associated with it. These numbers represent the intensity of red,
  green and blue colour in that particular pixel

Question 15

There are primarily four tasks that Computer vision accomplishes

Answer 15

• 1. Semantic Segmentation (Image Classification)
• 2. Classification + Localization
• 3. Object Detection
• 4. Instance Segmentation