RPA
(Robotic process automation)
1. RPA
is the process of integrating all the business operations with the help of
robots to reduce human interference.
2. It
is the wide application of the technology controlled by the business logic. It
is used to speed up the entire business process.
3. It
automates the business which is rule-based and structured.
4. It
uses ‘machine learning’ and ‘artificial intelligence’ capabilities to do the
bigger volume workload, which was previously done manually.
5. All
the tasks to be automated here are queries and record.
6. RPA
is included with the system bots. They can work as a human worker.
7. RPA
bots can handle the entire application.
8. RPA
bots perform repetitive tasks much faster. This allows employees to
perform higher-value work.
9. RPA
bot can be programmed to finish any repetitive task. This provides flexibility.
10. RPA
bots are easy to integrate. They can take the input and they can analyse the
output of all the windows applications.
11. RPA
bots are divided into three categories i.e. Probots, knowbots, and
chatbots.
12. Probots
follow the simple rules to process the data.
13. Knowbots
search the user-specified information through the internet.
14. Chatbots
are the virtual assistants. They keep on responding to the customer queries.
15. RPA
reduces the labour costs of the organization.
16. RPA
software adopts the newly changing environment.
17. RPA
is the scalable technology that can be applied to all the industrial
departments. It can be applied to the work which is more prone to manual
error, manual data entry, and high-volume work.
18. RPA
are classified as Attended automation, Unattended automation, and Hybrid RPA.
19. Attended
automation tools require manual interference to finish the process.
20. Unattended
automation tools have self-decision-making capabilities.
21. Hybrid
RPA is the combination of Attended and Unattended automation.
22. Decision
making is improved with the implementation of RPA bots.
23. Recent
RPA tools used are UiPath, Blue Prism, Automation anywhere.
24. RPA
has less technical barriers. It is basically code-free technology; hence anyone
can use it.
25. RPA
increases work accuracy.
26. RPA
bots work 24*7.
27. It
is used in collecting social media statistics.
28. It
is also in extracting structured data from documents, payroll, new recruitment
hiring.
29. It
is also used in the health industry for patient registration and billing.
30. In the insurance industry, it is used for claim processing.
31. In
manufacturing, it is used to create the bill of materials and calculate the
total sales, inventory management, and purchase order management.
32. In
travel and logistics, it used for the ticket booking, and passenger details.
33. In the banking sector, it is used for card activation and fraud claims.
34. In
BPO, it automates all the manual operations and it gives the best customer service.
35. It
is also used to collect the data from different web portals.
36. In the telecom sector, it is used to generate the bills.
37. Any
industry can use the RPA to increase its productivity, scale, and accuracy. Its
costs are recovered in 4 years.
Artificial
Intelligence (AI)
1. It
is the ability of the machine to perform all the tasks performed as like the
human beings.
2. AI
are the programmed devices and software’s that behave as humans do.
3. AI
prepares the programmed device or software for the specified task and allows it
to improve on its own.
4. AI
are trained programmes capable to perform and work independently.
5. AI
are classified as Narrow AI, Artificial general intelligence (AGI), and
Artificial superintelligence (ASI)
6. Narrow
AI is sometimes called as weak AI. It performs the single tasks and it
operates under the limited context. Ex. Alexa, Google Home, Siri, self-driving
cars.
7. ASI
is the hypothetical AI. It is the way in which all the programmed devices of AI
behave and think independently, and they suppress human intelligence.
8. AGI
are the strong AI, seen in the movies. Ex. Humanoid.
9. AI
is tested with the Turing Test, Cognitive Modelling Approach, Law of Thought
Approach and Rational Agent Approach.
10. Turing
test finds out the artificial intelligence entity is effectively holding the conversation
with the human agent.
11. Cognitive
Modelling Approach builds the AI model based on the human understanding.
12. Law
of Thought Approach applies the logical statements to the AI software’s.
13. Rational agent tries to achieve the best possible outcome in the circumstances. It acts
as a dynamic agent.
14. Types
of AI are reactive machines, limited memory, a theory of mind, and
self-awareness.
15. Reactive
machines have the capacity of not using the past memory for the current
situation decisions.
16. Limited
memory machine has the capacity to use the past memory to perform the current
task. Their memory is a short term.Ex. Self-driven cars, traffic lights etc.
17. Theory
of mind has the capacity to react as per the people emotions and thoughts.
It can adjust with the people around.
18. Self-awareness
machines are more aware of their needs and their senses than humans.
19. AI
enabled virtual assistant is widely used in the hospitals for the patient
visits.
20. AI
applications are Machine learning, Deep learning, Neural Networks, Evolutionary
Algorithm, Cognitive Computing, Natural Language Processing, Computer vision.
21. Machine
learning is the application that provides the computer systems with the ability to
learn automatically and learn from the experience without being programmed. Machine
learning requires a lot of data.
22. Machine
learning focuses on the development of the algorithms that analyse the given
data and make the prescribed predictions. Ex. Movie recommendations in the
Netflix.
23. Deep
learning uses many neutral networks for many layers of processing networks. Ex.
Speech recognition.
24. Neutral
Networks try to use the human brain approach to analyze the data. It finds out
the wide data for analysis that is much complex for the human brain.
25. Natural
language processing (NLP) allows the computer to recognize and produce the
human speech and language. The goal of NLP is to have effective interactions
between the man and the machine for the daily activities. Ex. Google
translator.
26. Computer
vision tries to understand the image and pictures with the PDF documents by
breaking it into the different parts.
27. The graphical processing unit is required for AI to carry out the millions of data.
28. The best example of AI is Alexa and google home.
29. AI
is used to plan the travel, trips and the stays with the help of virtual AI assistant.
30. AI
applications are used to provide the personalized X-ray copy and the medicines.
31. Stock
management is improved with the help of AI.
32. AI
techniques are used to identify fraudulent transactions in the banking sector.
33. AI
performs high volume computerized tasks easily.
34. Recent
examples are Amazon Alexa, Google Home, Apple Siri, Tesla, Cogito.
Machine
Learning (ML)
1. Machine
learning is the application of “Artificial Intelligence’’ which provides a system
ability to learn, think, react and work automatically and improve from its
experience without any manual programming.
2. ML
focuses on the programmes that can access the data and use it to learn for
themselves.
3. The basic motive of the ML is to allow the computer programmes to learn and work
themselves without any external output.
4. ML
is the statistics that make the predictions using the computers.
5. Machine learning methods are classified as Supervised Machine learning, Unsupervised
Machine learning, Semi-supervised machine learning, Reinforcement machine
learning.
6. Supervised
Machine learning is the learning in which training is given to the machine with
the help of data which is tagged with the correct answer. Ex. Basket is filled
with different types of fruits. The first step is to teach the machine the different
shapes of fruits along with the colour one by one.
7. Unsupervised
learning is the training of the machine using the information that is never
classified and it allows the algorithm to act on it.
8. Machine
gathers the unsorted information according to the similarities without any
training. In this case, no training is given to the machine.
9. Semi-
supervised machine learning is a combination of supervised and unsupervised
machine learning.
10. Reinforcement
machine learning is the technique which helps the systems to learn by trial and
error method.
11. Machine
learning requires careful preparation of lots of data.
12. ML
is used to find out which product you are looking to buy on Amazon.
13. ML
algorithm consists of Representation, Evaluation and Optimization.
14. Recent
example of ML is FB News Feed.
15. Other
example of ML in Govt service is to find the fraud in the banking sector. Another
ex is Image recognition, Speech recognition, Medical diagnosis etc.
16. Another ex.GPS services, Video navigation, Email
Spam, and malware filtering.
|
RPA |
AI |
ML |
|
Process
driven |
Data
driven |
Data
driven |
|
It
is a software robot. |
It
is a simulation of human intelligence. |
It
is the application of AI. |
|
Automation
of high-volume repetitive tasks |
It
performs all the operations as like the humans do. |
Ability
of the machine to work and learn independently without any external support. |
|
It
is used to reduce human interference |
It
is used with the help of human commands. (It can work on its own) |
It
does not require any external interference. |
|
Good
for clerical work |
Good for daily work. |
Good
for daily and professional work. |
|
Coding is not required. |
Slightly
coding is needed. |
Slightly
coding is needed. |
|
Ex.
Applications in BPO, bank, and inventory management. |
Ex.
Alexa, Siri, Tesla. |
Ex.
FB news feed. |
