Unlike other technologies, AI can mean many things to many different people. It is the voice that speaks to you from your Amazon Echo speaker, but also the robot that automates an industrial process, the virtual assistant that saves you time when dealing with your bank, and the brain in the GPS that helps guide you around a city you don't know well.
For this reason, it is necessary to state that artificial intelligence encompasses a wide range of technologies, from machine learning to artificial vision, from neural networks to data modeling.
These are some of the most commonly used technologies in AI
⮚ Machine Learning is the discipline within Artificial Intelligence dedicated to creating systems that learn and connect information the way a person would. To do this, it uses algorithms capable of detecting patterns in previously collected data and uses those patterns to create future predictions and new trends. Machine Learning provides a foundation for computers to gain the ability to develop their own algorithms automatically, training different models for this purpose.
⮚ Deep Learning is a sub-discipline of Machine Learning. It is a learning system inspired in how the human brain’s neural networks process information, backed by a very complex mathematical basis. Although it relies on experience—either historical data collected from the environment or self-generated—Deep Learning does not start from strict definitions of what is correct and what is not, so the system can determine conclusions on its own. Unlike Machine Learning, Deep Learning creates new layers of Machine Learning beyond the first decision point. These new layers are called neural networks and theoretically simulate how human brains operate.
⮚ Machine Vision involves making a computer capable of recognizing images in the same way a human being would, which is not so much a matter of collecting a set of pixels but of understanding what they represent in the real world and how some objects relate to others. Two main areas are currently being explored in this field: facial recognition and autonomous navigation.
⮚ Cognitive Intelligence is a combination of the technologies mentioned above to create Artificial Intelligence services capable of human-level understanding. Within Cognitive Intelligence, Virtual Agents are computer mechanisms capable of interacting with humans. They combine visual and sound recognition, reading comprehension, NLP (Natural Language Processing), and Machine Learning to create systems capable of understanding information related to human interaction and enabling the system to respond accordingly.
⮚ Speech Recognition Systems are systems capable of understanding the human voice. This is one of the most used AI technologies and is available to everyone through human language voice transcriptions used in interactive voice systems in mobile apps, smart speakers, and some vehicles, among others.
⮚ Augmented Reality and Content Generation. Augmented Reality is a technology that superimposes virtual elements on the user’s field of view, producing attractive and enriched visual experiences. This technology is appealing to most companies as an investment to modernize their businesses. Content Generation is automatically producing content for human consumption, whether through videos, advertisements, blogs, infographics, or other visual media.
With this overview of the most commonly used AI technologies and on a more practical level, what are the specific programs or programming languages used to develop AI algorithms and applications?
Which are the most common programming languages to develop AI algorithms and applications?
To answer this question, we need to consider that AI-oriented programming is very different from conventional programming; therefore, programming languages are used in a very different way.
With that in mind, the main and most widely used programming language in AI is Phyton, mainly because the most popular machine learning and deep learning packages in the world—including Pandas, NumPy, and others—are written in Python. This makes Python the most complete language for AI, with outstanding support for every step of the machine learning process.
Another popular programming language in AI is R. It was created specifically for data analysis and machine learning, which gives it a great advantage over other general-purpose languages. It is easy to learn, cross-platform and can work with functional and object-oriented programming paradigms.
Finally, SQL is a declarative language designed to create data queries in a format similar to natural speech—in English—and is the main way to communicate with any relational database management systems, so it is focused mainly on databases.
Examples of the applications of AI in common life
AI is now part of everyday life, streamlining tasks and personalizing experiences. From virtual assistants to AI-powered navigation, these technologies make routines simpler and smarter. Here are some common ways AI is transforming daily activities.
Conclusion
These are just some of the different technologies currently used in Artificial Intelligence and its applications in common life. However, take into account that there are many more technologies helping companies continue to grow and invest in the future. Many of them still have a long way to go, especially considering that AI is still a nascent field.
Now that you know the technologies and the most used languages.. try them! Be part of the change.
Thanks for reading!
