Page 157 - Ai Book - 10

u What: Under this block, the nature of a problem can be determined. At this stage, evidence should be
gathered either from newspaper articles, media, announcements, magazines, Internet, etc. to prove that
the problem you have selected actually exists. You will find various types of questions under this block
which are as follows:

z What is the problem?
z Enlist the evidence which proves that the problem selected by you really exists.
u Where: Under this block, you can focus on the situation in which the problem arises, the context of it, and
the locations where it is notable. This block contains many questions which are as follows:
z Display the point or location when you think that the selected problem really exists?

z Under which situation, stakeholders experience the problem?
u Why: Under this block, you can think and write about the project benefits for stakeholders as well as for
the society. This block contains many questions which are as follows:

z How would a stakeholder get a benefit from the solution?,

z Why will stakeholders prefer this solution?
DATA ACQUISITION

As you read earlier, an AI system is completely based on data. Thus, before initiating an AI project, all the data
requirements must be identified and gathered. This process of identifying and gathering data requirements is
known as Data Acquisition.

To put it simply, an AI model works on the basis of data which is being fed by the programmer. After that, it
is trained to predict the desired output by entering accurate and inaccurate datasets. Let us understand the
concept of dataset with the help of simple example.

Example: Suppose you want to develop an AI enabled system which can predict the difference between cat and
dog on the basis of images. To do this, you would feed hundreds of different images of both into the machine.
These images act as a dataset for an AI system using which it can predict the difference between both. The data
with which the machine can be trained is called training data.

Data Feature

The term ‘Data feature’ also plays an important role in the AI project life cycle. Data feature refers to the type of
data that you want to collect for the problem scoped. In this example of inviting employees,most appropriate
data features would be facial characteristics, name, employee ID, date of joining, department, level, etc.
Sources of Data

Nowadays, data is present everywhere, but many times, it is hard to find reliable sources of data. The different
types of data sources is like unlocking the key to a treasure trove of information. AI systems rely on various data
sources to learn, analyse, and make decisions.
There are various sources to collect relevant data:

u Surveys: Surveys are a way of collecting data form a group of people in order to gain information and insights
into various topics of interest. The process involves asking people for information through questionnaires
(telephonic or in-person) which can be online or offline. It can be considered as a data source.

u Web Scraping: Web scraping or Data scraping is the method of downloading information from Internet.
u Sensors: Sensors are connected through gateways which enable them to collect live data in the offline
mode.

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