1. Artificial Intelligence
Artificial intelligence is a field of computer science which makes a computer system that can
mimic human intelligence. It is comprised of two words "Artificial" and "intelligence", which
means "a human-made thinking power." Hence we can define it as,
Artificial intelligence is a technology using which we can create intelligent systems that can
simulate human intelligence.
The Artificial intelligence system does not require to be pre-programmed, instead of that, they
use such algorithms which can work with their own intelligence. It involves machine learning
algorithms such as Reinforcement learning algorithm and deep learning neural networks. AI is
being used in multiple places such as Siri, Google?s AlphaGo, AI in Chess playing, etc.
Advantages of Artificial Intelligence
o High Accuracy with less errors: AI machines or systems are prone to less errors and high
accuracy as it takes decisions as per pre-experience or information.
o High-Speed: AI systems can be of very high-speed and fast-decision making, because of
that AI systems can beat a chess champion in the Chess game.
o High reliability: AI machines are highly reliable and can perform the same action multiple
times with high accuracy.
o Useful for risky areas: AI machines can be helpful in situations such as defusing a bomb,
exploring the ocean floor, where to employ a human can be risky.
What is Business Intelligence?
BI (Business Intelligence) is a set of processes, architectures, and technologies that convert raw
data into meaningful information that drives profitable business actions. It is a suite of software
and services to transform data into actionable intelligence and knowledge.
BI has a direct impact on organization’s strategic, tactical and operational business decisions.
BI supports fact-based decision making using historical data rather than assumptions and gut
feeling.
BI tools perform data analysis and create reports, summaries, dashboards, maps, graphs, and
charts to provide users with detailed intelligence about the nature of the business.
BI technology can be used by Data analyst, IT people, business users and head of the company.
2. BI system helps organization to improve visibility, productivity and fix accountability.
Example
A bank gives branch managers access to BI applications. It helps branch manager to determine
who the most profitable customers are and which customers they should work on.
The use of BI tools frees information technology staff from the task of generating analytical
reports for the departments. It also gives department personnel access to a richer data source.
The first two phases use ETL (extract, transform, and load) process to handle the data.
1. Data Integration: An organization needs to deal with the data that comes from different
sources.
First, extract the data from different sources which can be your separate database,
servers.
Then the data is integrated into a standard format and stored at a common area that's
called staging area.
2. Data Processing: Still, the integrated data is not ready for visualization because the data
needs processing before it can be presented. This data is pre-processed.
For example, the missing values or redundant values will be removed from the data sets.
After that, the business rules will be applied to the data, and it transforms into
presentable data.
3. Then this data will be loaded into the data warehouse.
3. Data presentation: Once the data is loaded and processed, then it can be visualized much
better with the use of various visualization that Power BI offers.
By using of dashboard and reports, we represent the data more intuitively.
These visual reports help business end-users to take business decision based on the
insights.
Machine learning
Machine learning is about extracting knowledge from the data.
Machine learning is a subfield of artificial intelligence, which enables machines to learn from past
data or experiences without being explicitly programmed.
Machine learning enables a computer system to make predictions or take some decisions using
historical data without being explicitly programmed.
Machine learning uses a massive amount of structured and semi-structured data so that a
machine learning model can generate accurate result or give predictions based on that data.
Machine learning works on algorithm which learn by using historical data.
It works only for specific domains such as if we are creating a machine learning model to detect
pictures of dogs, it will only give result for dog images, but if we provide a new data like cat image
then it will become unresponsive.
Machine learning is being used in various places such as for online recommender system, for
Google search algorithms, Email spam filter, Facebook Auto friend tagging suggestion, etc.
Example: google translation, Chatbot, speech recognition, Self-driving cars, Image recognition
The future of the automobile industry is self-driving cars. These are driverless cars, which are
based on concepts of deep learning and machine learning.
4. Artificial Intelligence Machine learning
Artificial intelligence is a technology which enables
a machine to simulate human behavior.
Machine learning is a subset of AI which allows a
machine to automatically learn from past data without
programming explicitly.
The goal of AI is to make a smart computer system
like humans to solve complex problems.
The goal of ML is to allow machines to learn from data
so that they can give accurate output.
In AI, we make intelligent systems to perform any
task like a human.
In ML, we teach machines with data to perform a
particular task and give an accurate result.
Machine learning and deep learning are the two
main subsets of AI.
Deep learning is a main subset of machine learning.
AI has a very wide range of scope. Machine learning has a limited scope.
AI is working to create an intelligent system which
can perform various complex tasks.
Machine learning is working to create machines that
can perform only those specific tasks for which they are
trained.
AI system is concerned about maximizing the
chances of success.
Machine learning is mainly concerned about accuracy
and patterns.
The main applications of AI are Siri, customer
support using catboats, Expert System, Online
game playing, intelligent humanoid robot, etc.
The main applications of machine learning are Online
recommender system, Google search
algorithms, Facebook auto friend tagging suggestions,
etc.
On the basis of capabilities, AI can be divided into
three types, which are, Weak AI, General AI,
and Strong AI.
Machine learning can also be divided into mainly three
types that are Supervised learning, Unsupervised
learning, and Reinforcement learning.
It includes learning, reasoning, and self-correction. It includes learning and self-correction when introduced
with new data.
5. Comparison
Artificial Intelligence Business Intelligence
Philosophy AI is started with the intention of
creating similar intelligence in
machines that we find in humans
It helps in analyzing business performance
through data-driven insight i.e understand
the past and predict the future
Goals To create expert systems and
implement human intelligence in
machines
It should provide information that can enable
efficient and effective business decisions at
all levels of the business.
Areas that
contribute
Artificial Intelligence is a combination
of science and technology based on
computer science, maths, Biology,
Psychology
It combines business analysis tools which
include OLTP, enterprise
reporting, OLAP(online analytical
processing)
Applications Artificial Intelligence is used in
various fields such as Gaming,
Natural language processing, Expert
systems, Vision systems, Speech
recognition, Handwriting recognition,
Intelligent Robots.
It is used in Spreadsheets, querying and
reporting software, Digital dashboards, Data
mining, Data warehouse, Business activity
monitoring.
Research
Areas
Research areas for Artificial
Intelligence are Expert systems,
Neural networks Natural language
processing, Fuzzy logic, Robotics.
Research areas for Business Intelligence
include Data mining in social
networks, process analytics, Bigdata, OLAP
Issues Artificial Intelligence faces three
issues. They are Threat to Privacy,
Threat to Human dignity, Threat to
safety.
Business Intelligence issues are classified
into two types. They are Organization and
People and Technology and data
6. Business Intelligence Artificial Intelligence
What is it? Traditional analytics that make
analyzing data easier but
leaves decision-making in the
hand of humans
Advanced analytics that
enables computers to make
critical business decisions
themselves
Goal To streamline the process of
collecting and analyzing data
and provide businesses with
useful information and analysis
to aid decision-making
To mimic human intelligence,
behavior, and thought process
and help businesses to make
rational decisions
Why and when is
it used?
Develops insights based on
data already gathered; it is
used to answer what has
happened in the past
Analyzes large quantities of big
data; it is used to answer what
will happen in the future
How does it work? It works on the principles of
statistical analysis
It uses sophisticated machine
learning and deep learning
algorithms
How does it help
businesses?
It analyzes past data and
enables businesses to make
better data-driven decisions to
improve business processes,
customer service, and
employee satisfaction
It creates human-like
intelligence in machines and
enables businesses to forecast
and predict customer demand,
competitor standing, and
market changes
Hierarchy BI comes first in the hierarchy
of analytics
AI comes after BI in the
hierarchy of analytics
Application Majorly used in reporting, data
mining, data warehousing, and
highlights key matrices and
visuals out of historic data in
the form of modern, intuitive
dashboards
Majorly used for predictive
analytics, gaming, forecasting,
natural language processing,
robotics, image recognition,
and self-driving cars
7. Business Intelligence Artificial Intelligence
Goal and final
outcome
It starts with gathering and
analyzing data points from
various data sources, and ends
with visual reports and
dashboards
It starts by training computer
systems to think and work like
humans, and ends with
predictive insights about the
future
