ARTIFICIAL INTELLIGENCE
CLASS - 9
UNIT 1: AI REFLECTION, PROJECT CYCLE AND ETHICS
INTRODUCTION - AI
● Artificial Intelligence (AI) is a branch of computer science that simulate human
intelligence into machines, especially in computer systems, so that they can
think and perform actions similar to humans.
● The main aim of artificial intelligence is to develop machines with intelligence
like humans and having qualities like perception [ the acquisition of information
and rules for using the information] reasoning [ using rules to reach the
approximate or definite conclusions] and learning [ self-corrections]
TYPES OF AI
AI is a broad term which includes Machine Learning, Big Data or Natural Language
Processing (NLP) as its subsets. These subsets can also be classified by the level of
intelligence.
AI can be classified into three categories:
● Weak AI
● Strong AI
● Super AI
Weak AI: It is also known as narrow AI. It is the most common type of AI that we can
see and use all around us. Characteristics of this AI are:
● It performs dedicated tasks with intelligence.
● It cannot perform beyond its capabilities. It is trained to perform a specific task.
It fails in unpredictable situations.
● Apple’s Siri is a good example of Weak AI systems. It is a digital virtual assistant
that operates with a predefined limited range of operations.
● IBM’s Watson Supercomputer comes under the category of weak AI. It is an
Expert system combining Machine Learning and Natural Language Processing.
● Some other examples include Self-driven cars, Image Processing, e-commerce
websites using chatbots, AI system playing chess, etc.
Strong AI: It is also termed as General AI. Machines with following characteristics will
fall in this category:
● Machines that would perform a wider range of different tasks with the
intelligence like that of humans.
● It is a step ahead of Weak AI by including problem solving, learning and
development.
● Strong AI can control complex robots capable of performing tasks with high
precision and adaptability.
● Strong AI’s ability to analyse vast amounts of medical data could lead to more
accurate diagnoses and advancements in medical research.
● Another example of Strong AI could be a machine hearing “Good Morning” and
deciding to turn on the coffee maker.
Super AI: They are in their hypothetical stage. Following are the characteristics of
Super AI:
● Systems that would surpass human intelligence in performing any task will fall
in the category of Super AI.
● Super AI will be able to understand humans and will be able to evoke their own
emotions and desires as well.
● It can be considered as the next stage of Strong AI and developing such
machines will be a challenging task.
How do machines become intelligent?
AI makes the machines intelligent. Programming along with some aspects of human
intelligence like decision making and problem solving gives rise to intelligent
machines. Machines are fed with enough data and correct algorithms to make them
intelligent.
DOMAINS OF AI
AI APPLICATIONS
1. Face Lock in Smartphones: Smartphones nowadays come with the feature of
face locks in which the smartphone’s owner can set up his/her face as an
unlocking mechanism for it. The front camera detects and captures the face and
saves its features during initiation. Next time onwards, whenever the features
match, the phone is unlocked.
2. Smart assistants: Smart assistants like Apple’s Siri and Amazon’s Alexa
recognize patterns in speech, then infer meaning and provide a useful response.
3. Fraud and Risk Detection: Finance companies were fed with bad debts and
losses every year. However, they had a lot of data which used to get collected
during the initial paperwork while sanctioning loans. They decided to bring in
data scientists to rescue them from losses. Over the years, banking companies
learned to divide and conquer data via customer profiling, past expenditures,
and other essential variables to analyse the probabilities of risk and default.
Moreover, it also helped them to push their banking products based on
customer’s purchasing power.
4. Medical Imaging: For the last decades, computer supported medical imaging
application that has been a trustworthy help for physicians. It doesn’t only create
and analyse images, but also becomes an assistant and helps doctors with their
interpretation. The application is used to read and convert 2D scan images into
interactive 3D models that enable medical professionals to gain a detailed
understanding of a patient’s health condition.
AI PROJECT CYCLE
PROBLEM SCOPING
This is the first step to understand and define the problem that we want AI to solve.
Problem scoping is the state where we set clear goals and outline the objectives of the
AI project. It includes precisely outlining the issues, defining them explicitly, identifying
their causes and developing a plan to fix them.
DATA ACQUISITION
This stage focuses on collecting the relevant data required for the AI system. Since the
data forms the base of the project, care must be taken to ensure that the data is
collected from the reliable sources and the information is authentic. Also the data
should also be in sufficient quantity to provide proper analysis.
DATA EXPLORATION
This stage involves the exploration and analysis of the collected data to interpret
patterns, trends and relationships. The data is in large quantities. In order to easily
understand the patterns, you can use different visual presentations like graphs,
databases, flowcharts and maps.
MODELLING
After exploring the patterns, we need to select the appropriate AI model to achieve the
goals. This model should be able to learn from the data and make predictions.
TESTING AND EVALUATION
The selected AI model now needs to be tested, and the results need to be tested nd
result need to be compared with expected outcomes. This helps in evaluating the
accuracy and reliability of the model and improving it.
DEPLOYMENT
Once the evaluation is complete and generates accurate results., the AI model can be
deplo.
yed in the real world.
This canvas helps us in identifying 4 crucial parameters we need to know for solving a
problem. So, what are the 4Ws? It refers to Who, What, When and Why.”
Acquiring Data from reliable sources
Surveys
Web Scraping
Sensors
Cameras
Observations
API(Application Program Interface)
Government Portals
SYSTEM MAPS
A system map shows the components and boundaries of a system and the components
of the environment at a specific point in time. With the help of System Maps, one can
easily define a relationship amongst different elements which come under a system.
Example - Water Cycle
In this System Map, all the elements of the Water cycle are put in circles. The map here
shows cause & effect relationship of elements with each other with the help of arrows.
The arrow- head depicts the direction of the effect and the sign (+ or -) shows their
relationship. If the arrow goes from X to Y with a + sign, it means that both are directly
related to each other. That is, If X increases, Y also increases and vice versa. On the
other hand, If the arrow goes from X to Y with a – sign, it means that both the elements
are inversely related to each other which means if X increases, Y would decrease and
vice versa.
1. DATA EXPLORATION
Exploring different types of graphs used in data visualisation and will be able to
find trends and patterns out of it.
2. MODELLING
Defining the terms:
1. Artificial Intelligence, or AI, refers to any technique that enables computers to mimic
human intelligence. The AI-enabled machines think algorithmically and execute what
they have been asked for intelligently.
2. Machine Learning, or ML, enables machines to improve at tasks with experience. The
machine learns from its mistakes and takes them into consideration in the next
execution. It improvises itself using its own experiences.
3. Deep Learning, or DL, enables software to train itself to perform tasks with vast
amounts of data. In deep learning, the machine is trained with huge amounts of data
which helps it into training itself around the data. Such machines are intelligent enough
to develop algorithms for themselves.
Rule Based Approach
Refers to the Al modelling where the rules are defined by the developer. The machine
follows the rules or instructions mentioned by the developer and performs its task
accordingly. For example, we have a dataset which tells us about the conditions on
which we can decide if a child can go out to play golf or not. The parameters are:
Outlook, Temperature, Humidity and Wind. Now, let's take various possibilities of these
parameters and see in which case the children may play golf and in which case they
cannot. After looking through all the cases, we feed this data into the machine along
with the rules which tell the machine all the possibilities.
A drawback/feature for this approach is that the learning is static. The machine once
trained, does not take into consideration any changes made in the original training
dataset.
Refers to the Al modelling where the machine learns by itself. Under the Learning Based
approach, the Al model gets trained on the data fed to it and then is able to design a
model which is adaptive to the change in data. That is, if the model is trained with X
type of data and the machine designs the algorithm around it, the model would modify
itself according to the changes which occur in the data so that all the exceptions are
handled in this case.
