The power and potential of artificial intelligence cannot be overstated. It has transformed how we interact with technology, from introducing us to robots that can perform tasks with precision to bringing us to the brink of an era of self-driving vehicles and rockets

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How to build an AI app?
leewayhertz.com/how-to-build-an-ai-app
The power and potential of artificial intelligence cannot be overstated. It has transformed
how we interact with technology, from introducing us to robots that can perform tasks
with precision to bringing us to the brink of an era of self-driving vehicles and rockets.
And this is just the beginning. With a staggering 270% growth in business adoption in the
past four years, it has been clear that AI is not just a tool for solving mathematical
problems but a transformative force that will shape the future of our society and economy.
The time to invest in AI is now.
Artificial Intelligence (AI) has become an undeniable force in our lives which is
everywhere, from robots that can perform tasks with precision to autonomous cars that
are changing how we travel. It has become an essential part of everything, from large-
scale manufacturing units to the small screens of our smartwatches. Today, companies of
all sizes and industries are turning to AI to improve customer satisfaction and boost sales.
AI is the next big thing, making its way into the inner workings of Fortune 500 companies
to help them automate their business processes. Investing in AI is not just a trend, and it’s
a necessary step toward staying competitive in today’s fast-paced business world.
It shouldn’t be a surprise that artificial intelligence is expected to grow in market share
due to organizations’ increasing adoption of AI. According to the latest data, the market
for artificial intelligence was valued at $51.08 million in 2020, according to verified
market research (2021). This number is expected to rise more than tenfold in eight years
and reach $641.3 billion by 2028. The rising adoption of cloud-based services and the
increased demand for AI-based virtual assistance are two of the key drivers of this
remarkable artificial intelligence growth. Companies are increasingly relying upon AI to
offer personalized services to customers. The increasing importance of customer services
will drive the adoption of AI to help customers. This raises the next question – how to
build an AI app?
This article describes the fundamentals of AI and a step-by-step guide to building an AI
system.
What is artificial intelligence?
Artificial intelligence (AI) is a field of computer science that focuses on solving cognitive
programs associated with human intelligence, such as pattern recognition, problem-
solving and learning. AI refers to the use of advanced technology, such as robotics, in
futuristic scenarios. There have been many definitions surfaced of artificial intelligence,
but John McCarthy provides the following definition in his 2004 paper – ” It is the
science and engineering of making intelligent machines, especially intelligent computer
programs. It relates to the similar task of using computers to understand human
intelligence, but AI does not have to confine itself to biologically observable methods.”

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Artificial intelligence, in its most basic form, is a field that combines computer science
with robust datasets to facilitate problem-solving. Artificial intelligence also includes sub-
fields like machine learning and deep learning, which are often mentioned together. The
algorithms of these technologies are used to build expert systems that can make
predictions and classifications based on input data. AI encompasses many disciplines,
including computer science, data analytics, statistics, hardware, software engineering,
neuroscience, psychology, and philosophy.
What are the components of AI?
LeewayHertz
Components of Artificial Intelligence
Learning Reasoning
Perception Language
understanding
Problem solving
Learning
Computer programs learn differently from humans. Computer learning can be further
divided into many forms where learning for AI is one of the most important components.
It includes solving of problems using the trial-and-error method. The program also keeps
track of the positive moves and saves them in its database for the next time it faces the
same problem. Learning in AI is memorizing individual items, such as vocabulary and
solutions to problems. It’s also called rote learning. This learning method can later be
applied using the generalization technique.
Reasoning
Until five decades ago, the art of reasoning was a skill limited to humans. The ability to
distinguish makes reasoning an essential component of artificial intelligence. This ability
allows the platform to draw inferences compatible with the given situation. These
inferences can be classified as either deductive or inductive. There is a great success rate

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using deductive inferences via programming computers. Inferential cases provide
guarantees that a problem can be solved. For example, the accident is an inductive case;
however, always due to instrument failure.
Reasoning involves drawing inferences relevant to the current situation.
Problem-solving
AI’s problem-solving ability is basic, including data where the solution must find an
unknown value. AI witnesses many problems being solved on the platform. These
methods are essential components of artificial intelligence that divide queries into general
and special purposes. A special-purpose method is a solution that is tailored to solve a
particular problem which is often done by leveraging some of the features found in the
case in which the problem was embedded. A general-purpose approach can solve many
different problems. At the same time, AI’s problem-solving component allows programs
to reduce differences step-by-step between goals and current states.
Perception
Artificial intelligence’s ‘perception’ component allows the element to scan any
environment using different sense organs. The internal processes allow the perceiver the
ability to examine other scenes and determine their relationship. This analysis can be
complicated and similar objects might appear differently at different times depending on
how the angle is viewed.
Perception is a component of artificial intelligence that can propel self-driving cars at
moderate speeds. FREEDY is one of the first robots to use perception to identify different
objects and assemble artifacts.
Language-understanding
Language can be described as a collection of system signs that are consistent with each
other. Language understanding is a widely used component of artificial intelligence that
uses distinct types of language to understand natural meanings, such as overstatements.
Human English is one of the most important characteristics of languages allowing us to
distinguish between objects. AI is designed in such a way that it can understand English,
the most common human language. The platform makes it possible for computers to
understand different computer programs that are executed on them easily.
Different elements of AI
Artificial intelligence encompasses many techniques. Let’s learn more about the main
subfields within AI.

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LeewayHertz
6 Elements
of AI
Machine learning
Fuzzy logic
Robotics
Natural language
processing
Neural networks
Expert systems
Machine learning
Machine learning is a very important field in advanced technology. It is a must to have
term company introduces a new product that uses ML algorithms and techniques to
deliver to the consumer highly creatively. This technique allows computers to learn
without being explicitly programmed and used in real-life use cases. It is fundamentally
the science that allows machines to interpret, execute, and analyze data to solve real-
world problems. Programmers use complex mathematical knowledge to design machine-
learning algorithms written in a machine-readable language to create a complete ML
system. Besides, ML allows us to decode, categorize and estimate data from a dataset.
It has provided self-driving cars, image and speech recognition, demand forecasting
models, useful search, and many other applications over the years. It focuses on
applications that can adapt to experience and improve their decision-making or predictive
accuracy over time.
Data professionals also choose types of machine learning algorithms as described below
depending on data availability.

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Supervised learning: Data experts feed labeled training data into algorithms and
assign variables to the algorithms to access and find correlations. Both the input and
output of the algorithm are particularized.
Unsupervised learning: These types of learning use algorithms that train with
unlabelled data. An algorithm analyzes datasets to draw meaningful connections or
inferences. Cluster analysis, for example, uses exploratory data analysis to find
hidden or grouping patterns in data.
Reinforcement learning: Reinforcement learning is used to teach a computer to
follow a multi-step process with clearly defined rules. Programmers create an
algorithm that will perform a task. They then give the algorithm positive or negative
signals to execute the task. Sometimes the algorithm decides for itself what actions
to take.
Neural network
The neural network combines cognitive science with machines to complete tasks. It is a
branch of artificial intelligence that uses neurology, a part of biology that deals with the
nerve and nervous system. The neural network is a way to simulate the human brain,
where there are infinite numbers of neurons.
A neural network, in simple terms, is a collection of algorithms used to discover the
elemental relationships among the data sets. It mimics the human brain’s operating
process. A neural network is a system of neurons that are either artificial or original in
nature. A neuron is a mathematical function in a neural network whose job is to collect
and classify information according to a specific structure. The network strongly
implements statistical techniques such as regression analysis to complete tasks. They are
used extensively for everything from market research to forecasting, fraud detection, risk
analysis, and stock exchange prediction.
Robotics
This is an emerging field of artificial intelligence and a fascinating field of research and
innovation that focuses mainly on the design and construction of robots. Robotics is an
interdisciplinary field of science and engineering that incorporates mechanical
engineering, electrical engineering and computer science. It is the study of designing,
manufacturing, operating and using robots that involves computer systems that control
them, produce intelligent outcomes and transform information.
Robots are often used to do tasks that would be difficult for humans to complete
repetitively. For example, the majority of robotics tasks were related to assembly lines for
automobile manufacturing and the transportation of large objects in space by NASA. AI
researchers are also working on robots that use machine learning to enable interaction at
the social level.
Expert systems

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The first successful AI software model was the expert system created in the 1970s and
became more popular in the 1980s.
An expert system is a computer system that imitates human experts in decision-making.
This is done by using its knowledge base to derive knowledge and then applying reasoning
and insights rules to the terms of user queries. Expert systems’ effectiveness is dependent
on the knowledge of the expert. The more information the system has, the greater its
efficiency will be. The expert system offers suggestions for spelling and grammatical
mistakes in the Google search engine. The system can be used to solve complex problems
by reasoning with proficiency. This is especially true when using “if-then” rules rather
than traditional agenda to code. Expert systems are highly responsive, reliable,
understandable, and efficient in execution.
Fuzzy logic
Fuzzy logic is a type of mathematical logic that deals with approximate reasoning rather
than fixed and exact reasoning. It simulates the ambiguity and uncertainty that frequently
exists in real-world situations. Fuzzy logic is used to process and analyse data from
various sources in order to make decisions.
Natural language processing (NLP)
In layman’s terms, NLP is a part of computer science and AI that allows communication
between humans and computers using natural languages. It allows computers to
understand and read data mimicking natural human language. NLP refers to a method of
searching, analyzing and understanding text data. Programmers use the NLP library to
teach computers how useful the information is from text data. NLP is commonly used to
detect spam. At the same time, computer algorithms can look at the subject or text of an
email to determine if it is junk or not.
How does AI work?
As we have mentioned in the previous section, AI is a superset of machine learning and
deep learning, it can be used as a decisive instrument for these techniques. AI works
through patterns learned from data sets. The intelligent and iterative collection process
allows for accumulating large amounts of data which the AI tool uses to learn patterns.
The AI model then predicts the outcome based on the learned patterns. Many iterations
are associated with building the AI model, and each iteration is used to test its activity and
measure the accuracy level. It can process huge amounts of data quickly due to its
computational power. AI empowers a computer to solve problems by itself.
As a method, artificial intelligence can be classified into the following categories:
Artificial Narrow Intelligence (ANI): This form of artificial intelligence is used
in most practical applications. This concept is primarily about making the computer
learn how to solve a specific problem by itself.

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Artificial General Intelligence (AGI): AGI is concerned with computers that
mimic human cognition.
Artificial Super Intelligence: This is an abstract form of AI.
The following subfields of AI help AI models work using data patterns:
Machine learning: It automates the building of analytic models that draws on
methods from statistics, physics, and neural networks to uncover hidden insights in
data.
Neural network: This machine learning type comprises interconnected units like
neurons that process information in each unit. This process involves multiple passes
at the data in order to identify connections and derive meaning from undefined
data.
Deep learning: It utilizes huge neural networks with many processing units that
take advantage of advancements in computing power and better training techniques
to learn complex patterns from large amounts of data. Image and speech recognition
are two common applications. Algorithms of deep learning, such as generative
adversarial networks (GAN) and variational autoencoders (VAEs), are widely used
in generative AI to generate highly realistic data similar to existing data.
Computer vision: It uses pattern recognition and deep learning to recognize what
is in a photo or video. Machines can process, analyze, and understand images
utilizing computer vision. Alongside they can capture images and videos in real-time
and interpret the surroundings.
Natural language processing: NLP allows computers to understand, analyze
and create human language, including speech. Natural language interaction is the
next stage in NLP which allows humans to use everyday language to communicate
with computers to accomplish tasks.
A variety of technologies enable and support AI, namely
Graphical processing units: They are key to AI as they provide the high
computing power required for iterative processing. Big data and computing power
are required to train neural networks.
Internet of Things: This technology generates large amounts of data via
connected devices, although not all are analyzed. AI-based models will enable us to
generate more of it.
Advanced algorithms: They are used to combine data at different levels and
analyze it faster. Intelligent processing is crucial for identifying and predicting rare
events, understanding complex systems, and optimizing unique situations.
APIs: They are portable code packages that allow adding AI functionality to existing
products and software packages. For example, they can be used to add image
recognition capabilities for home security systems and Q&A capabilities that provide
data descriptions, headlines, and interesting patterns.
Artificial intelligence applications in business

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AI is an emerging technology whose full potential benefits are yet to be realized. AI
innovation is just one of many forces disrupting markets and creating new opportunities
for digital businesses. AI can also be applied to various industries, functions, and
organizations in various ways. Here are some business applications of AI:
Machine learning is the backbone of human-like communication: ML
drives common AI applications like chatbots, robots, and autonomous vehicles.
Deep learning: This method uses facial, voice, and neural networks to provide
biometric solutions. These techniques hyper-personalize content using data mining
and pattern recognition across large datasets.
Artificial intelligence in IT operations: Virtual support agents (VSA) provide
IT support in IT service management alongside the IT service desk. AI can route
tickets, pull information from knowledge management sources, and provide
common answers.
AI in supply chain management: These use cases include predictive
maintenance, risk management and procurement. Because AI is consistent and
quicker than humans at certain tasks, it can be used for decision-making
automation.
AI for sales enablement: AI identifies and nurtures new ideas and prospects
based on existing customer data. It also uses guided selling to increase sales
execution and revenue.
AI in marketing: AI acts as a tool that can assist with real-time personalization
and content and media optimization, campaign orchestration, and other tasks
otherwise limited by human resources and capabilities. AI’s ability to uncover
customer insights and speed up the deployment of products at scale is the most
compelling value proposition.
AI in customer service: Customers have 24/7 access to virtual customer
assistants (VCAs), which include speech recognition, sentiment analysis and
automated/augmented quality control.
AI in human resource: Use cases include recruitment (matching demand and
supply or predicting success with recruitment) and selection of skills using NLP for
consistent skill and job descriptions for next-generation match and search. HR also
uses recommendation engines to find learning, content, mentors and career paths,
and adaptive learning.
AI in finance: It includes reviewing expense reports, processing vendor invoices
and complying with accounting standards.
AI in vendor management: While basic ML technologies can be used for contract
classification and spend classification, more advanced use cases are emerging in areas like
risk management, candidate matching, virtual purchasing assistance, voice recognition,
and sourcing automation.
Streamline your business processes with powerful AI apps

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This step-by-step guide will show you how to build and use an AI app. Whether you are a
researcher, business owner or just curious about AI technology, these instructions will
help you navigate the steps of creating an AI system that can transform your industry.
Steps to create an Ai app
Problem
identification
Preparation
of data
Choosing an
algorithm
Training the
algorithms
Choosing the best
language for AI
Final
development
Deploy and
monitor
Platform
selection
2
1 3 5 7
4 6 8
LeewayHertz
Step 1: Problem identification
First, identify the problem to be solved before you build an AI app. Consider the functions
and processes of the app in which you want to use the AI technology stack. What result
should you expect from it? How will you benefit? Once you have identified the problem
and the idea, you can start to create product requirements. Based on the requirement
analysis, developers can understand the purpose of creating products and find
technologies and tools to help them.
You will also need to do the following during the planning stage:
Decide the composition of the technical and non-technical team- from project
managers and business analysts to data engineers and backend programmers.
Discuss your work schedule with professionals.
Start exploring the data needed to build an AI/ML model.
Step 2: Preparation of data
AI-powered apps are data-driven and typically require large amounts of data in order to
function. However, prior to applying the data, it must be collected and prepared
appropriately to create an accurate data model. AI labeling team of professionals
specialized in AI, and ML-based software solutions can label the collected data. These

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software engineers carefully study the input information and sources to prepare the data
for further use. They often use the Cross-Industry Standard Process for Data Mining
(CRISP-DM).
The next step involves verifying the input data for any errors, missing values or incorrect
labels and then preparing the data, which includes the following steps:
Uploading and selecting raw data
Selecting annotation tools
Labeling and highlighting the data
Processed data selection and saving in a file
Using the collected data, you can compare the solutions and move on to the modeling
phase. The data previously collected is used to train the ML model via different methods.
Step 3: Choosing an algorithm
Now, we come to the core and arguably the most important part of building an AI system:
choosing the right algorithm. While the technical details can be complex, it is important
to understand the fundamental concepts involved in selecting the right algorithm for the
task at hand. The algorithm can be of different forms based on the learning type.
There are two main types of learning: supervised and unsupervised learning.
Supervised learning involves providing the machine with a dataset on which it trains itself
to provide the desired results on a test dataset. Several supervised learning algorithms are
available, such as SVM (Support Vector Machine), Logistic Regression, Random Forest
Generation, and Naive Bayes Classification. These algorithms can be used for
classification tasks, such as determining the likelihood of a loan defaulting, or for
regression tasks, such as determining the amount that might be lost if a loan defaults.
On the other hand, unsupervised learning differs from supervised learning because it does
not provide the machine with a labeled dataset. Instead, unsupervised learning
algorithms are used for clustering, where the algorithm tries to group similar things;
association, where it finds links between objects; and dimensionality reduction, where it
reduces the number of variables to decrease noise.
Choosing the right algorithm is crucial to building a sound AI system. By understanding
the fundamental concepts of supervised and unsupervised learning and familiarizing
oneself with the various algorithms available, you can ensure that your AI system is able
to accurately and effectively solve the problem at hand.
Step 4: Training the algorithms
Training an algorithm after selecting it is critical to verify its accuracy. Although you can
not set any standard metrics or threshold to ensure model accuracy, it is important to
ensure that the algorithm works within the chosen framework through training and
retraining until it achieves the desired accuracy. As an AI system is data-centric, its

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efficiency depends solely on the data performance. So, the data is expected to be diverse
enough to make the model perform as expected. So, investing time and resources into
training the algorithm is beneficial and a mandatory step. This, in turn, will result in
increased efficiency, cost savings, as well as a competitive advantage.
Step 5: Choosing the best language for AI
A clear set of requirements is essential for building an AI solution. It also requires the
right choice of technologies and AI programming language that will make it possible to
help create intuitive AI systems offering users a robust experience. There are many
programming languages available, each with its strengths and weaknesses. Depending on
your specific needs, you need to select the specific programming language for your AI
project. While some AI programming languages are great at processing large amounts of
data and crunching huge numbers, others excel at natural language programming. You
can determine which language is best suited for your project by understanding the
strengths and limitations of each language. Here are some of the most popular
programming languages to consider when building an AI app.
Python
Java
C++
R
Prolog
Lisp
Haskell
Smalltalk
Rust
Step 6: Platform selection
While creating an AI app, we often use a wide variety of frameworks and APIs to create
smart AI algorithms easily. These frameworks and APIs come with in-built features of
deep learning, neural networks and NLP applications. Almost all major cloud platforms
for AI provide these AI platforms and APIs, which make it easy to implement ready-made
solutions for speech, image and language recognition, as well as provide high-level
abstractions of complex machine-learning algorithms.
These are the main factors that influence your choice of APIs and platform for AI:
Selecting your preferred cloud, e.g., a hybrid cloud.
Data storage location and ownership details.
The selected language limitations.
Availability of APIs in a particular region.
Cost of AI development life-cycle.
Step 7: Final development

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As mentioned above, creating an AI-driven software application is similar to other
software development, except for CRISP-DM. The following steps are integral parts of AI
development:
Architecture design of the solution
Design of the user interface
Frontend and backend creation
Also, during development, you can optimize performance, expand functionality, and
adapt the product for updates.
Step 8: Testing, Deployment and Monitoring
Once the development stage is over, you must test the product with the help of QA
engineers. They can use automated, manual or mixed tools. You can deliver the app only if
it has been thoroughly tested and functions as expected. Once the testing is done, the
product must be deployed to the production server. Post-deployment, the support team
offers regular maintenance to your solution in order to prevent data drift. AI maintenance
is unique in that it requires continuous data and concept updates. This will ensure that
your algorithm accuracy does not suffer any degradation, including regular updates like
security patches and version changes.
Endnote
Industries are on their way to digital transformation and automation, and artificial
intelligence is one of their best travel companions. Artificial intelligence is here to stay
and will continue gaining ground due to its unrivaled automation capabilities and human-
like abilities. AI is a key enabler of successful business development, and its usage is in the
surge to provide more value to end users. AI automation is everywhere, from fintech
products to social media and telemedicine apps. However, all types of AI development
require deep experience and extensive algorithm expertise, which needs professional help.
Although you know how to create an AI app, it is evident that AI app development is a
complex process that requires advanced technical knowledge in AI, machine learning and
data science. You can always hire an experienced AI development company with
experience in end-to-end AI app development for guaranteed success.
Wondering how to build a high-quality AI app? Contact LeewayHertz’s AI specialists for
your requirement; we build robust AI apps with advanced features.