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Overview:

* Artificial intelligence and machine learning are expected to expand the corporate market with new solutions and products for companies, according to Gartner research.

* The technology grew significantly in recent years and made apps with these capabilities gain popularity and space in the market;

* Within AI and machine learning, deep learning is the key driver behind these advances. Learn more below.

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With constant growth in recent years, the presence of artificial intelligence and machine learning in our lives is undeniable.

A recent Gartner report points out that, by 2023, a large part of companies and corporations worldwide will have a product built with this technology, aiming to improve results and create new offerings for customers.

Today, artificial intelligence is used more casually and less in the corporate sector. But that is expected to change.

From the moment we listen to music on our smartphones or watch a series on streaming services, machines are frequently tracking our behavior so companies can understand our content preferences.

These techniques are based on teaching computers to understand our choices using probability and large volumes of data.

Within artificial intelligence, we can separate two main areas: machine learning and deep learning.

Want to learn more?

See the details below.

Differences between artificial intelligence and machine learning

It’s easy to mix up these terms, precisely because one is connected to the other.

The concept of artificial intelligence (AI) is broader—it is essentially an umbrella idea.

What does that mean?

It’s simple: artificial intelligence is based on building solutions and products capable of performing tasks that would otherwise require humans.

It’s common to imagine AI as something extremely complex—like super robots that help us in daily life.

That may happen in the future, like in movies, but not yet.

For now, what AI can offer is the ability to perform specific tasks to help companies and create new services.

That is where machine learning comes in. Within AI, machine learning makes it possible for software to carry out those “intelligent actions.”

In practice, machines can be trained using large datasets and algorithms that give them the ability to learn how to perform a task.

With results produced by machine learning, it becomes possible to build new applications, products, and projects—like the ones we already use today (which we’ll also cover in this article).

Going deeper, within AI and machine learning there is also deep learning, which we’ll explain next.

What is deep learning?

Deep learning is a technique developed to implement machine learning.

Based on artificial neural networks, this technology can train computers to perform tasks that humans do, such as image identification, prediction, and speech recognition.

The process involves organizing and executing data through predefined equations. In other words, deep learning trains the computer to perform a task on its own, using parameters for pattern recognition and the probability of something being correct or not.

Major companies like Spotify and Netflix use deep learning to better understand their audience profiles and suggest content based on what users are most likely to enjoy.

Where can we notice artificial intelligence and machine learning?

1. In data analysis

One of the major uses of AI and machine learning today is managing and analyzing large amounts of data.

As technology evolves, it becomes possible to build increasingly specific Big Data and Business Intelligence (BI) tools for different business niches, serving more professionals.

That way, you can manage your company’s data, generate insights, and do meaningful work to increase results and outperform competitors.

Today, systems like Microsoft Power BI and Tableau are successful worldwide and are constantly updated with new capabilities involving machine learning and deep learning.

By combining AI and BI, your company can uncover new business opportunities, make faster and more accurate decisions, and create new products and services.

2. In text translators

Another product built on AI advances is text translation.

Today, there are many websites, software, and services that offer automatic translation for different types of content.

The most interesting part is that translation can be done quickly with a high level of accuracy, regardless of the size of what you want to translate.

On mobile devices, some apps even allow users to translate a phrase by simply pointing the phone camera at it—translating signs, products, and more, regardless of the country you’re in.

Thanks to machine learning, translations are becoming increasingly precise, since each language and country has regional expressions and phrases that can have different meanings depending on context.

AI can detect these differences and translate more faithfully.

3. In product recommendations

Have you ever searched for a product you wanted to buy and then immediately started seeing ads for similar items?

That frequent product recommendation is also the result of AI and machine learning.

With this technology, it’s possible to create algorithms that identify user interests on the web—whether on social networks or e-commerce stores.

From there, Google’s systems use advertising tools to drive traffic between pages and generate leads for companies.

As mentioned earlier, this also happens with Spotify.

The subscription music player can track your musical interests and recommend new songs in the same style so you stay in the app as long as possible.

Video streaming apps work the same way.

Products like Netflix and Prime Video use these capabilities to select content users are most likely to watch, based on their choices within the platform.

4. In data security

Data security is another area where AI brings major benefits.

With companies investing heavily in service security—especially after multiple data leaks in recent years—this technology supports stronger encryption, making data harder to access during malicious attacks.

In addition, most robust banking systems and payment services use machine learning to identify potential fraud and scams, blocking many of these attempts.

This is also present in online stores. When you buy or sell a product, it’s essential that your system is secure to build credibility and trust.

With AI, it becomes possible to protect banking data such as credit card information.

Another interesting aspect is facial recognition.

With AI-powered security cameras, it becomes possible to build intelligent software that identifies criminals from a database, assisting government authorities.

5. In apps we use every day

When talking about AI, machine learning, and deep learning, we can’t ignore the apps we use daily.

Today, a wide range of apps already use this technology and can offer valuable features—often without requiring users to pay for them.

One of the biggest examples is navigation apps like Google Maps and Waze. These solutions use AI to combine data and route options for drivers and can even estimate how long a trip will take.

Virtual assistants also grew significantly in recent years. Services like Alexa and Siri are now common and accessible.

With them, you can schedule meetings, check weather forecasts, and use many other features via voice commands or through an app.

6. In chatbots

Chatbots are another good example of AI and machine learning. This capability is growing, and many large companies are moving to it.

Chatbots are bots that can interact with a user or customer in a way that is similar to a human conversation.

Here’s an example (which we’ve also discussed here at X-Apps):

Imagine your doctor asked you to take a series of exams and you want to know if a lab in your city can do them all in one place. You go to the website and discover that instead of calling, you can ask anything via WhatsApp.

When you send a message, you almost instantly get a welcome response and then another one offering options such as scheduling exams, getting support information, learning how to download results online… all by pressing 1, 2, 3, or 4 and waiting for the reply.

That’s what a chatbot is. This lab example shows how a question can be answered in just a few minutes without a long wait on the phone.

What is the future of artificial intelligence and machine learning?

According to Gartner experts, the coming years will be marked by a shift in how companies view AI.

Today, investments in this technology are often in an exploration and research phase, aiming to better understand the limits of machine learning and deep learning and what can actually be built.

That’s why it’s common to find apps that simulate “What would you look like in the future?” and other entertainment-focused products.

This should change.

Studies suggest that companies will start developing more corporate products where AI is the foundation.

As a result, AI will move beyond casual use in apps and become more consolidated in products and solutions designed to increase business outcomes.

According to Gartner, by 2023, 80% of the solutions offered by the digital business industry will be powered by AI.

Another interesting projection is that, in the coming years, with increased use of cloud computing, 70% of companies will use cloud infrastructure to develop AI—scaling results even further.

This evolution signals technological maturity and a better understanding of the risks involved.

For that reason, decisions involving AI, machine learning, and deep learning must follow ethical principles, ensuring this technology is not used in harmful ways and is instead applied to benefit people and companies.

So, what do you think about building a web system or an app using the best that modern technology can offer?

Request a quote from X-Apps. In addition to evaluating the costs of your app, we can provide strategic consulting and help you assess the most interesting alternatives for your company in terms of quality and cost-benefit.