How to Develop Artificial Intelligence Software?

According to a report by Statista, the global AI software market will reach 126 billion by 2025. The above statistics show in the next few years, artificial intelligence will scale at incredible speed.

The use of AI in software helps industries like healthcare, logistics, and finance automate tasks, increase sales, improve customer experience and detect fraud. In addition, it enhances the efficiency of the organization by reducing the number of human errors.

So, if you are an entrepreneur looking for ai development company or planning to implement an AI system in the business, you are at the right place. With the help of our software development experts, we have curated a guide on how to develop artificial intelligence software.

Let’s understand the meaning of Artificial Intelligence.

Introduction of Artificial Intelligence and Evolution

What is Artificial Intelligence?

Artificial Intelligence is a jumble of two different words; artificial means man-made, while another word intelligence refers to studying facts, information, and understanding of principles by the human brain. It means it can be learned and implemented into practice.

It can even be referred to as a machine or program programmed to think and learn, possibly like humans—for example, Tesla’s driverless cars.

In computer science, it refers to creating software algorithms that categorize, analyze and make predictions from data. It also can act on data, learn from data and improve itself. AI is programmed in a way that it knows to perform a task.

Artificial Intelligence works with its sub-fields like machine learning and deep learning that operate on neural networks. A neural network is a replica approach of the human brain for analyzing the data.
pixabay

Source: Pixabay.com

Data scientists create and use such models with algorithms to process vast amounts of structured and unstructured data. So, computer engineers develop and train AI/machine learning apps to solve complex problems.

Here’s the interesting part about the evolution of AI over the period of time.

The Evolution of Artificial Intelligence

The Evolution-of AI

Past

In 1956, John McCarthy and Minsky, Herbert A. Simon, Allen Newell, and Alan Turing introduced the term “Artificial Intelligence”.

From 1956-1974, gradually, the use of computer systems started, and with time they could store more information, and data processing became faster. Thus, AI flourished, and ML algorithms improved.

In 1980, with the expansion of funds and the use of algorithmic tools, the research on AI got a boost.

By 2000, despite several attempts of failure, they achieved the successful landmark on the technology.

Present

In the current situation, AI has become a common part of our lives. AI uses a huge amount of data to automate the process and make predictions on information beyond human capabilities. Although, with the increasing influence of AI, organizations are implementing AI to increase work efficiency.

Furthermore, if we talk about spam or fraud, it provides a real-time warning to alert organizations to safeguard resources. The implementation of smarter robots in the manufacturing industries has increased efficiency and reduced the irregular patterns of errors.

Future

We might wonder about what would be the future of AI? Will it create a positive impact on our lives or negative? Although, the implementation of AI in healthcare, education, e-commerce, electricity, and production lines helped humans achieve efficiency. Hence, such machines and technologies are capable of providing outstanding results in every business vertical.

Let’s check the use of AI for software development.

Use of AI in Software Development

Use of AI in Software Development

The traditional software development life cycle includes different stages, such as:

  • Planning
  • Requirement Analysis
  • Prototyping
  • Software Development
  • Testing
  • Deployment
  • Maintenance

However, building AI software is different from the traditional approach of software development. For example, if you are building AI-driven software for your healthcare, it requires massive data for decision making, planning, and evaluation.

An AI software solution works on large datasets. These large datasets are used to train the AI algorithms to make predictions, identify patterns and take necessary actions just like humans.

Furthermore, let’s check what the steps to use AI in your software development are.

Identification of Problem

Identifying the problem to develop an AI system will need human intervention. At first, you will be required to identify the problem. Then, think about the desired outcomes like what end goals you want to achieve with an AI system. Remember, AI is a tool that is used to solve problems, not an entire solution. Once you know where to use AI, it helps you make an AI system that understands your requirements and convert them into high-level software.

Data Preparation

The next step, before writing a single piece of code, data scientists majorly focus on selecting the right data by organizing, cleaning, and training data to make it perfect for use. Furthermore, to design a model, data must be labeled and set in chronological format. There are two major types of data: structured data and unstructured data.

  • Structured data: The data that refers to a fixed format and remains consistent is called structured data.
  • Unstructured data: A form of data that does not have a fixed format. It can be an example of audio or video files and is called unstructured data.

Preparing Algorithms

Now, this is the most crucial part of developing AI systems. Choosing a type of algorithm is the core part of creating an AI system. There are two types of algorithm learning, i.e. supervised learning and unsupervised learning.

  • Supervised learning: In this learning, a dataset is given to a machine on which it would train itself to provide the required outcome of the dataset. There are several types of supervised learning algorithms, such as Support Vector Machine, Logistic Regression, Large Scale Refactoring, naïve Bayes classification, and others.
  • Unsupervised learning: This learning is differentiated into three categories: clustering, association, and dimensionality reduction. Whereas clustering is group things, the association is trying to find links between the objects, and dimensionality reduction reduces the number of variables to decrease noise.

Training Model with Platform

After that, model learning is the part where a data scientist needs to ensure model accuracy. For achieving model learning, the logical training of algorithms is carried out with a basic level of accuracy.

For model training, choosing the right platform is essentially important. There are technologies to ease the process of building machine learning applications, such as Microsoft Azure Machine Learning, Google Cloud Prediction API, and Tensorflow that help create AI software and model deployment.

Choosing Programming Language

The next step is to look for a programming language. There are various programming languages available to choose from, like C++, Java, Python, and R. Moreover, python and R programming languages have extensive machine learning libraries. The code of AI systems is written in “neural network weights” to develop machine learning methods.

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Furthermore, let’s check the use of AI software in different industries.

Industries Implementing AI and ML

Healthcare

AI software helps to minimize human errors in administrative tasks and increase efficiency. It transcripts the medical notes through NLP and makes it easier for doctors to read patients’ medical history.

Secondly, the use of a vital monitoring stats system helps when a person’s health is not stable. With the use of wearable devices, it will help to find actionable insight into their body. As vital signs have the potential to predict health fluctuations before a person feels, you can also build such AI-driven healthcare management software to manage hospital operations.

E-commerce

In the e-commerce business, AI helps to provide better recommendations to users. Amazon and eBay are two examples of using AI to provide the best product recommendations to their users. Thus, it leads them to increase their revenues.

The use of chatbots is another prominent example. These chatbots serve customers by providing them assistance as and when needed, eliminating limited human resources.

AI is also useful in managing the stock of different products based on the predictions of products. Thus, it helps stores’ to manage the appropriate supply of stock to meet the market demand.

Logistics

In the logistics industry, using machine learning and data analytics, businesses will predict the demand of the future. Hence, it will transform supply chain management. For example, AI algorithms will help organizations to find the quickest route to support fast deliveries. And the use of optic sensors in the vehicles will send alerts to users on road conditions and traffic to help drivers.

Entertainment Industry

The use of AI in entertainment organizations helps producers and broadcasters recognize the individual’s activity to recommend shows or programs. For example, it helps Netflix or Amazon Prime to provide personalized content to users.

Besides, the programs are becoming more powerful and intelligent with time. They can even predict whether users want to purchase a product for themselves or to gift someone.

Talking about the music industry, Spotify and Apple Music have implemented AI to understand users’ music preferences. So, they can provide the right recommendation of songs to users.

Manufacturing Industry

In the manufacturing industry, AI is being implemented across several layers of operations. It includes workforce planning, admin tasks, supply of raw material, and product design. Hence, it brings improvement in efficiency, the safety of employees, and product quality. AI algorithms estimate product demands by looking into patterns of criteria like location, economic factors, political status, consumer behavior, and weather patterns.

Organizations are involving AI algorithms to improve the quality of products as well. These algorithms help the manufacturing team by notifying them about possible production faults that can impact product quality.

Examples of AI used in Software

Informatica Big Data

Informatica Big Data Management is software for businesses to process big, diverse data. So, they can take insights from data and perform extensive data integration. Using Big Data Management, the company can maintain extensive data integration without writing any external code. The software enables you to reduce the high financial costs because of manual processing.

Various companies are using Informatica Big Data Software. A few of them are Avis Budget Group, Celcom, and Comcast.

Salesforce Einstein

Salesforce brings the first AI for CRM named Salesforce Einstein. It’s a combination of AI and its sub-technologies that makes the customer platform more successful. By using an AI model, you will predict the next opportunities as per the customer’s needs. Additionally, it helps provide personalized experiences for customers, predict business outcomes, build custom bots and deploy on digital channels. It even helps you in managing the right data for your business by preparing data itself. Companies using the Salesforce Einstein AI platform are Sun Basket, US Bancorp, and Arizona State University.

Google Assistant

Google Assistant is one of the best AI systems examples. It searches the internet, sets alarms, events and connects with hardware on the user’s device. This AI system sets reminders, checks the weather, and prepares a list of shopping items. You can say it is the next generation of computer vision that helps humans in day-to-day tasks.

Netflix

Netflix is the most popular video streaming app. The company has over 208 million subscribers on the platform. Netflix uses AI and machine learning to provide personalized movie recommendations.

They even use auto-generation of thumbnails to predict which thumbnail of a movie can have the highest clicks. That helps the company understand that one could like certain movies and be likely to click on certain actors or movie genres. So, it allows them to implement thumbnails on movies/shows/web series accordingly.

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Frequently Asked Questions

  1. Which software is used for AI?

  2. There are various softwares used for AI. Some of them are as below,

    • Tensorflow
    • MATLAB
    • ArcGIS
    • Azure AI Platform
    • Google Cloud AI
  3. Can I create my own AI?

  4. Yes, it is possible to create your own AI, personal assistant, using a free tool called API.AI. It is a framework that uses natural language processing which helps you build an AI assistant that is self adaptable to learn new phrases.

  5. What is required to build an AI solution?

  6. You should know programming languages such as C++, Java, or Python. After that, You will be required to study machine learning as a part of AI. Using machine learning, natural processing language, and deep learning, you will design your AI system.

  7. How is AI used in software development?

  8. AI is used in software development which needs less human intervention and automates most of the tasks. Thus, it can decide based on the data patterns and learn itself to automate the tasks for users.

Conclusion

Therefore, the implementation of AI-based software in your business will reduce repetitive tasks and improve the productivity of your employees. Additionally, it provides personalization which leads to a better customer experience. The use of AI in the software will simplify the complex tasks of your project and provide a predictive solution using machine learning.

Do you want to create AI software for your business? Or Are you planning to implement AI in your organization and want to excel in your business operations? We are a prominent software and app development company based in Canada. Our expert team of software developers will guide you. Contact us by filling the form below. We will get back to you within three working days.

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Author Bio
Rakesh Patel
Rakesh Patel
Designation: Co-founder and CEO of Space-O Technologies

Mr. Rakesh Patel is a Founder and CEO of Space-O Canada. He has 28 years of IT experience in business strategies, operations & information technology. He has expertise in various aspects of business like project planning, sales, and marketing, and has successfully defined flawless business models for the clients. A techie by mind and a writer at heart, he has authored two books – Enterprise Mobility: Strategy & Solutions and A Guide To Open311.