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8 minutes read

How to Make An AI: Everything You Need to Know

By Jonathan Tarud
YouTube video
By Jonathan Tarud
8 minutes read
YouTube video

How to Make An AI? Artificial Intelligence and Machine Learning have been hot topics lately in technology as more businesses explore how AI technology can be harnessed to improve business operations and outcomes. 

Several AI models are currently available, including the popular ChatGPT, one of the key AI systems driving public and corporate interest in AI technology. However, using a pre-built AI model like ChatGPT might not be the most effective solution to meet your organization’s unique needs. 

As a result, there is growing business interest in moving away from pre-built AI solutions, and more companies are considering building their own AI model. While spearheading an AI project for your organization might seem out of reach, building AI is not as challenging as you think. 

This post will explore what your business will need to build a successful AI software development project. However, before we cover how to create an AI, it is vital to understand the different types of Artificial Intelligence. In addition, there are varying levels of AI capabilities that you should consider before beginning an AI operation. 

What Is Artificial Intelligence? 

This term is often used, but it is usually not fully understood. AI is a branch of computer science concerned with developing programs capable of thinking and completing tasks that humans perform

Popular science fiction representations, such as HAL or terminators, are far from the reality of what AI systems are capable of. AI is more data science than science fiction. 

There are three types of Artificial Intelligence that you should understand before beginning to build an AI system. The three types of AI include the following: 

  • Artificial Narrow Intelligence (ANI)
  • Artificial General Intelligence (AGI)
  • Artificial Super Intelligence (ASI)

Artificial Narrow Intelligence (ANI)

Artificial Narrow Intelligence is also referred to as weak AI. A weak AI model is designed to complete a specific task. For example, an AI model built for natural language processing, playing chess, language translation, facial recognition, speech recognition, etc., is considered Artificial Narrow Intelligence. 

ANI systems are only programmed to complete a single task. While the completion of this task might be impressive, it is only adept at a particular task, which is a far cry from the AI models we see in science fiction books and movies. 

Every instance of AI you have ever interacted with or heard about, from Alexa and Siri to ChatGPT, is an example of Artificial Narrow Intelligence. 

Artificial General Intelligence (AGI)

Artificial General Intelligence is also referred to as strong AI. Strong AI technologies can accomplish any intellectual task that a human can. Strong AI is merely hypothetical. No AI has ever exhibited the capacity to match a human’s intelligence and problem-solving skills. 

Data scientists and software engineers are working towards building this type of AI project. However, there is real debate among researchers and scientists if it’s even possible to develop AI models like that. 

Artificial Superintelligence (ASI)

If artificial general intelligence were merely hypothetical, ASI systems would be even more so. In theory, Artificial Superintelligence would surpass human intelligence in all possible aspects

This type of AI is most often depicted in science fiction, but if achieving general intelligence has proven impossible, achieving superintelligence is far beyond our current technical capabilities. 

AI Development for Your Business: Key Steps 

Now that you know more about what Artificial Intelligence is and what is possible, it is helpful to understand the steps involved in building your own AI model. The critical steps to build AI software include the following: 

  • Identify a problem 
  • Obtain data
  • Choose a programming language
  • Choose a platform 
  • Write algorithms 
  • Model Training 
  • Deploy 

Identify a Problem 

The first step in developing AI software is to identify a problem or task it can solve. For example, ChatGPT helps people write content, and Dall-E helps people create unique image content. What task will your AI perform?

We have already explained that general and superintelligence are mere hypotheses when it comes to AI. As a result, your AI will need to do something. Before you begin writing and designing Machine Learning algorithms, you must decide what your AI will do. 

Obtain Data 

Once you have identified a problem to solve, the next step is to obtain training data. Getting high-quality data is simpler than making improvements to the AI model. After collecting training data, you must clean it whether you have structured or unstructured data. 

Data must be processed and cleaned before it can be used to train an AI system. Data cleaning fixes or removes errors from the data to improve its quality. Using good data to train AI systems is essential or will not function reliably. 

Choose a Programming Language 

There are several tech stack options you can choose from. The best programming languages for AI software are C++, Python, Java, and R. Depending on the goals of your AI tool, some languages will be better suited than others.

For example, C++ is your best option to build video game AI. However, R is your best option if you want to develop deep learning models for predictive analysis. In addition, Python is flexible and the most beginner-friendly programming language for AI tasks. If you want to enhance your skills in any of these languages, consider exploring free courses such as C++, Python, etc., which provide comprehensive learning resources and support for various programming languages. You should learn from the best artificial intelligence courses to ensure your model is written correctly and trained.

Choose a Platform 

After you have decided on a programming language, it is time to choose a development framework platform. Development frameworks simplify designing, writing, training, and debugging your AI models

Frameworks also offer templates and guides to help your team build neural networks and other predictive models. Scikit, Pytorch, and Tensorflow are the most popular frameworks and libraries for developing Machine Learning models. 

No-code AI Platforms

Unleash the Power of AI Without Coding!

Traditional machine learning often requires extensive coding expertise. But what if you could leverage the power of AI for your business without writing a single line of code? That’s the beauty of no-code AI platforms! These innovative tools allow anyone, regardless of technical background, to build and deploy custom AI models.

Here’s how it works:

  1. Data Quality: You simply provide your data – the raw material that fuels your AI model. These platforms can handle various data formats, from spreadsheets and databases to images and text.
  2. Automated Training: Gone are the days of complex algorithms and hours spent training models. No-code platforms handle the heavy lifting, automatically selecting the best training methods and optimizing your model for accuracy.
  3. Rapid Deployment: Once trained, your AI model is ready to be deployed in minutes. These platforms offer seamless integration with various applications and services, allowing you to put your AI to work quickly.

Popular No-Code AI Platforms:

  • Google Cloud AutoML: Specializes in making AI accessible with a user-friendly interface and pre-built models for common tasks like image and text classification.
  • Amazon SageMaker: Offers a comprehensive suite of tools for building, training, and deploying models. SageMaker Autopilot automates the entire process for a truly no-code experience.
  • Microsoft Azure Machine Learning: Provides a visual designer tool alongside its powerful cloud infrastructure, making it easy to build and manage AI workflows without coding.

The Benefits of No-Code AI:

  • Democratizes AI: Opens up AI capabilities to a wider range of users, fostering innovation across departments.
  • Faster Time to Value: Eliminates the learning curve associated with coding, allowing you to deploy AI solutions quickly.
  • Reduced Costs: Saves on development resources by streamlining the AI development process.
  • Increased Efficiency: Automates tasks and improves decision-making with data-driven insights.

Write Algorithms 

Algorithms are mathematical instructions that tell your AI what to do and how to improve its existing systems. The crux of an AI is the algorithms that power it. You can write your algorithms once you have chosen a programming language and platform

Typically, writing Machine Learning algorithms requires data science experts and data analysts or software developers who have experience with ML models and algorithms. 

Train Algorithms 

Writing an algorithm is not enough; you must also train it using the existing data you have collected. In addition, you may have to acquire additional data to improve the accuracy of your AI model. During the training process, it will also be necessary to tweak your algorithms to improve their accuracy

An inaccurate model is worthless to your organization. Therefore, ensure that you take algorithm training seriously. 


If you have successfully built and trained your model, it is time to deploy it. Of course, you should monitor it to ensure it performs as expected. Additional training will likely be necessary over time to hone the accuracy and improve the performance of your AI model. 

Final Thoughts

In a nutshell, that is all you have to do to build an AI of your own. But, of course, writing and training algorithms are far more complicated than they appear on paper. You will need a data science expert or team of data scientists to ensure your model is written correctly and trained. 

If you want help creating an AI solution for your organization, contact an experienced AI and Machine Learning development expert like Koombea

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