Which technologies Are Used in Artificial Intelligence?

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?

AI-oriented programming differs greatly from conventional programming, as it focuses on data, pattern recognition, and model training.
Here are the most widely used programming languages for AI development:

• Python: The most popular AI language due to its simplicity, versatility, and extensive libraries (NumPy, Pandas, TensorFlow, PyTorch).
  
• R: Created for statistical analysis and data visualization, ideal for researchers and data scientists.
  
• SQL: Used for managing structured data in relational databases, essential for cleaning and organizing datasets.
  

Other languages like Java, C++, and Julia are also used depending on performance requirements or project scope.

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.

Smart Assistants: AI-powered assistants like Siri, Google Assistant, and Alexa perform tasks like setting reminders, controlling smart home devices, and even making calls. They use natural language processing (NLP) to understand voice commands and machine learning to improve responses over time.

Social Media: AI is used to curate feeds, detect inappropriate content, and suggest connections. It analyzes user behavior to deliver personalized content and advertisements, using deep learning algorithms and NLP.

Navigation Apps: Apps like Google Maps and Waze use AI to predict traffic, suggest optimal routes, and estimate travel times. They analyze vast amounts of data from users in real-time and improve accuracy using machine learning.

Spam Filters and Email Sorting: Email services like Gmail use AI algorithms to detect and filter spam, as well as categorize emails into primary, promotional, and social folders, providing a streamlined email experience.

Banking and Finance: AI helps with fraud detection by identifying unusual patterns in transactions and supports customer service through chatbots that handle common inquiries. AI in investment platforms also enables robo-advisors that offer financial advice based on risk tolerance and goals.

Emerging AI Trends

As AI continues to evolve, several trends are shaping its future:

• Generative AI: Models that can create text, images, music, and code autonomously.
  
  
• Edge AI: Running models directly on devices (phones, IoT) for faster and more private computation.
  
  
• Explainable AI (XAI): Ensuring transparency and interpretability in AI systems.
  
  
• AI in Healthcare: From diagnostics to personalized treatments, AI is revolutionizing medicine.
  
  
• Responsible AI: Growing focus on ethics, fairness, and bias mitigation.

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!