6 key technologies in AI app development

14 min read
November 14, 2023

Developing an AI app can be a game-changer for your business.

But, before you can do it, you need to understand how the technology behind AI apps works.

That’s why we’ll cover 6 key technologies in AI app development and how you can use them in your AI app.

Let’s go!

What is artificial intelligence (AI)? 

Before diving deep into the key technologies behind it, let’s first define AI.

So, what exactly is artificial intelligence (AI)?

AI is a branch of computer science that aims to build machines and programs that can mimic human intelligence.

In other words, AI can perform tasks we normally would associate with human intelligence, such as:

  • Language recognition
  • Translation
  • Pattern recognition
  • Image recognition

But, how does it work?

In simple terms, AI works by processing and then learning from a large amount of data.

It then uses that learning to make informed decisions and predictions.

Of course, these processes are much more complex in reality.

But, on a basic level, that’s how AI works.

Now, let’s talk about the key technologies that make up artificial intelligence.

Key technologies in AI app development

Machine learning

Machine learning is a cornerstone of AI, and you’ll likely use it in one form or another when developing your AI app.

But, what exactly is machine learning?

Machine learning is a subset of AI that focuses on building systems that can learn from and make decisions from the data they’re trained on.

They learn from patterns they observe in the data, and they don’t have to be explicitly programmed to learn from that data.

Here’s how a standard machine learning workflow looks like:

Machine learning workflow

source: Hazaq

Machine learning systems are based on machine learning algorithms.

They’re computational models that are the building blocks of machine learning and AI systems.

And there are 3 main ways you can train a machine-learning model:

  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning

In supervised learning, the data it’s trained on is labeled, and the model can access the correct answers.

On the other hand, in unsupervised learning the model is trained on unlabeled data and the goal is that it finds patterns in the data and learns on its own.

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Finally, in reinforcement learning the model is rewarded for desired behaviors and punished for undesired ones.

Now, let’s discuss how you can use machine learning in your AI app.

How you can use machine learning in your AI app

We’ve mentioned that machine learning is the cornerstone of AI.

And this means that there are a lot of use cases for machine learning models, more than we can cover here.

So, we’ll go through each training approach and cover some of the most common algorithms and their use cases.

Let’s start with supervised learning.

Supervised learning

source: V7 Labs

Supervised learning algorithms excel at classification and regression.

For example, you can use a Naive Bayes algorithm for e-mail spam detection – it can classify emails as spam or not spam at scale.

Another use case for supervised learning algorithms is sales forecasting.

You can use a Random Forest algorithm to estimate future sales based on historical data.

Unsupervised learning algorithms are suited for clustering and association tasks.

For example, you can use a K-means clustering algorithm for market segmentation.

This will help you focus your marketing efforts and better personalize your app’s user experience (UX).

Another use case for unsupervised algorithms is fraud detection.

AI fraud detection

source: Penta Security

You can use an algorithm like Isolation Forest to detect anomalies and abnormal patterns in financial transactions that might point to fraud.

And finally, reinforcement learning algorithms are suited for especially complex use cases.

Autonomous vehicles are an example of AI that uses reinforcement learning algorithms like Deep Q-Networks and Proximal Policy Optimization.

Of course, the model you use in your app will depend on your specific goals and needs.

And that should be your main consideration when picking a machine learning model.

Natural language processing (NLP)

Natural language processing (NLP) is an AI technology whose goal is to understand and interpret human language in a valuable way.

And it can be a great addition to the AI app you’re developing.

It’s also a rapidly growing market, too.

According to Grand View Research, the NLP market was valued at $27.73 billion in 2022 and it’s expected to grow to a staggering $439.85 billion by 2030 – that’s a compound annual growth rate (CAGR) of 40.4%.

So, why is NLP so valuable?

Natural language processing

source: Github

When we talk, we’re often ambiguous and our speech is filled with irregularities that we can easily understand.

But, computers can’t.

And that’s where NLP comes in.

NLP helps bridge the gap between human communication and computer understanding.

It does that by breaking down text and voice data in a number of different ways, including:

  • Part of speech tagging
  • Speech recognition
  • Word sense disambiguation
  • Co-reference resolution
  • Named entity recognition
  • Sentiment analysis

These tasks help the NLP AI model break down and interpret language in a way that a computer can understand.

And they’re what allows computers to understand language and respond to queries similarly to how we communicate with each other.

Now, let’s discuss how you can use NLP in your app.

How you can use natural language processing (NLP) in your AI app

You can use NLP in various ways when developing your AI app.

One of the most common use cases for NLP are customer service chatbots.

Now, chatbots have been around for a while.

But, NLP takes them to the next level.

AI chatbots like Tidio’s Lyro and Intercom can quickly solve common problems your users might face with your app.

Lyro chatbot

source: Tidio

This will allow the rest of your customer service team to focus on solving more complex issues.

Another way you can use NLP models is for sentiment analysis.

Let’s say you’re doing market research for your app and you’ve interviewed a lot of different users.

You’ll get a lot of feedback and it would usually take you days to go through it all.

With AI sentiment analysis, you can do it in minutes.

This will help you address and act on your users’ feedback quickly and efficiently.

And that can help you stand out from the crowd.

Deep learning and neural networks

Deep learning and neural networks are the best choice if you need a complex AI model in your app.

But, we’re getting ahead of ourselves.

So, what is deep learning?

Deep learning is a subfield of AI that uses AI models to mimic how the human brain works.

And neural networks are foundational to deep learning.

Neural networks are designed to imitate the connection between neurons and synapses in the brain.

Here’s an example of what that looks like:

Neural network

source: V7 Labs

Neural networks have an input and output layer with a hidden layer in between them that does all the computations.

As you train the neural network, it adjusts the connections between the layers based on the input data and the feedback it gets.

And over time, it learns to make increasingly accurate predictions and decisions.

Now, let’s cover how you can use deep learning and neural networks in your AI app.

How you can use deep learning and neural networks in your AI app

It goes without saying that neural networks are difficult to build and maintain.

That’s why you should use deep learning in your AI app if you have a complex problem you need to solve.

Of course, there’s a huge variety of use cases for deep learning and neural networks.

That’s because there are a number of neural network types, 3 of the most popular being:

  • Recurrent neural networks (RNN)
  • Convolutional neural networks (CNN)
  • Generative adversarial networks (GAN)

And you can combine each of these with other AI technologies like computer vision and even other neural networks.

Tesla’s Autopilot is a great example.

Tesla Autopilot

source: CNN Business

Autopilot uses CNNs and deep learning combined with computer vision to power Tesla’s self-driving features.

CNNs are particularly good at image recognition tasks so they’re a good option if you need advanced computer vision in your app.

RNNs, on the other hand, are best suited for analyzing sequential data like:

  • Text
  • Speech
  • Time series

For example, Google Translate uses RNNs to deliver accurate translations.

Finally, GANs are fundamental to generative AI image generators like DALL-E and Midjourney

So, if you need AI to solve complex problems and deliver extra value to your app, deep learning and neural networks are the right choice.

Computer vision

Humans are visual creatures by nature.

So it’s no surprise that computer vision has emerged as a key AI technology in recent years.

Computer vision is what allows computers to “see” the world and opens up a lot of possibilities for your AI app.

And it’s a thriving market, too.

According to GlobalData, the value of the computer vision market reached $17.7 billion in 2023 and is expected to grow to $30.3 billion by 2030, at a CAGR of 19.6%.

But, how does computer vision work?

First, images and videos are captured by cameras or sensors.

Then, that visual data is processed and the AI computer vision model identifies patterns and important features in the images.

Computer vision

source: Perficient

These are then analyzed to understand what they represent e.g. if a particular pixel pattern shows a cat or a dog.

Finally, once it’s done analysing, the model produces an output that’s presented to the end-user.

Of course, this is a very simplified version of the process which is much more complex in reality.

But, on a basic level, that’s how AI computer vision models work.

Now, let’s discuss how you can use them in your AI app.

How you can use computer vision in your AI app

Computer vision is a versatile technology you can use in many ways in the AI app you’re developing.

Let’s say you’re developing a photo editing app.

Computer vision can help you take it to the next level.

With a computer vision model, your app can automatically enhance an image by adjusting:

  • Brightness
  • Saturation
  • Contrast
  • Sharpness

And all that without your users’ explicit input.

Also, it can automatically categorize your users’ images into albums based on their content.

And these are just two ways computer vision can significantly improve your app’s UX.

But, that’s just the tip of the iceberg – there are plenty of innovative ways you can use it as a core part of your AI app.

Take Aysa, for example.


source: Ask Aysa

Aysa is an AI skin monitoring tool that gives you insights about skin conditions.

All you have to do is take a picture of your skin, answer a few follow-up questions, and the app will give you guidance on your next steps.

Of course, its output is not a medical diagnosis by any means.

But, it can point you in the right direction and alert you if a rash or other skin condition is a symptom of a more serious condition which can potentially save your life.

And that’s a great example of just how impactful AI technologies like computer vision can be.

Robotic process automation (RPA)

Robotic process automation (RPA) is an AI technology that automates routine, repetitive tasks in business processes.

In RPA, software bots mimic actions a human user would take when completing a task.

RPA can take care of repetitive tasks like:

  • Data entry
  • Generating reports
  • Form filling

Like Leslie Willcocks, professor at the London School of Economics, said: “RPA takes the robot out of the human.”

The main benefit of RPA is that it frees up employees’ time and allows them to focus on solving more complex tasks.

Benefits of RPA

source: Nintex

That’s a great way to improve productivity and motivate employees.

And the RPA market is expected to strongly expand in the coming years.

Grand View Research estimates that it reached $2.94 billion in 2023 and is expected to reach $30.85 billion by 2030 at an impressive CAGR of 39.9%.

These numbers show that interest in RPA will only get stronger in the near future.

And that’s why investing in RPA makes sense.

Now, let’s talk about how you can use it in your app.

How you can use robotic process automation (RPA) in your AI app

By its nature, RPA is geared towards optimizing and automating business processes.

But, you can use it in your AI app, too.

One way you can use it is if your app relies on data entry e.g. if you have a finance app.

You can integrate RPA tools in your app that can automate the process, saving time for your users and improving their experience.

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It’s especially useful if your users need to migrate large volumes of data from other platforms.

Another way you can use RPA in your app is for quality assurance (QA).

RPA tools for QA can automatically run repetitive tests with better accuracy.

And the best part?

They can run them 24/7, so if any problems crop up, it will alert you immediately.

And that will help your team solve them quickly.

Generative AI

Generative AI has taken the world by storm in 2023 and is at the forefront of the AI revolution.

And you can use it both to help you build an AI app and deliver a better user experience.

But, what exactly is generative AI?

Generative AI refers to AI models that can generate original content based on the data they’re trained on.

Generative AI tools

source: AIMultiple

Generative AI models can create:

  • Text
  • Video
  • Images
  • Audio
  • Code

And it can do all that just from natural language prompts.

On top of these impressive capabilities, generative AI is positively impacting worker productivity and it can significantly increase revenue.

McKinsey estimates that generative AI can potentially increase corporate profits by up to $4.4 trillion a year.

And using it in your AI app is a great way to get a slice of that pie.

Let’s discuss ways you can do that.

How you can use generative AI in your AI app

If you use generative AI in your AI app, you can significantly improve its capabilities and offer unique features to your users.

And that extra value is what will help you retain them for the long term.

How you use generative AI will depend on your specific needs and the type of app you’re developing.

But, one way you can use it is by adding a chatbot for customer service or user engagement.

And you don’t even have to build your own from scratch.

OpenAI offers their GPT-3 and GPT-4 models as APIs you can integrate in your app.


source: Soft Kraft

And you can fine-tune the models based on your data to get even better results.

You can also use generative AI to personalize your users’ experience.

Let’s say you have an educational app.

With generative AI, you can add adaptive educational content to your app.

These can be quizzes and interactive scenarios that adjust to each users’ learning pace and style.

And that can make your AI app stand out from the crowd.

Key technologies in AI app development: Recap

Developing an AI app is a great way you can stand out from your competitors.

But, you need to do it right.

And to do that, you need to know the key technologies used in AI apps and their development.

To recap, here are the main technologies in AI app development:

  • Machine learning
  • Natural language processing (NLP)
  • Neural networks and deep learning
  • Computer vision
  • Robotic process automation (RPA)
  • Generative AI

If you want to learn more, check out how we build AI apps and get in touch with us if you need help building one.

Written by

Mario Zderic

Co-founder and CTO

Mario makes every project run smoothly. A firm believer that people are DECODE’s most vital resource, he naturally grew into the role of People Operations Manager. Now, his encyclopaedic knowledge of every DECODEr’s role, and his expertise in all things tech, powers him to manage his huge range of responsibilities as COO. Part developer, and seemingly part therapist, Mario is always calm under pressure, which helps to maintain the office’s stress-free vibe. In fact, sitting and thinking is his main hobby. What’s more Zen than that?

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