Guide to understanding the most common AI terms

Artificial Intelligence (AI) is a constantly evolving field that covers a wide range of disciplines and technologies. As a result of this constant progress, new terms are often introduced, making it important to understand the meaning of the most commonly used.

In this article, we will explore some of the concepts that have become fundamental to the field of artificial intelligence. These terms are relevant not only to technology professionals, but also to anyone interested in understanding how AI is revolutionizing many aspects of our daily lives. From the basics of machine learning to the latest trends in neural networks, knowing these concepts is essential to staying current in a field that is constantly redefining the boundaries of reality as we know it.

Artificial intelligence (AI)

When we talk about artificial intelligence (AI), we are referring to the ability of a machine to perform tasks that typically require human intelligence. These tasks include pattern recognition, decision making, problem solving, and learning.

On the other hand, within the concept of AI, it is divided into two main categories:

• Weak AI: These are AI systems that are designed and trained to perform specific tasks. These systems are extremely efficient in their domain, but they lack the versatility to perform tasks outside of their domain. Some examples would be virtual assistants, such as Siri or Alexa, which can answer questions, manage calendars, control devices, and perform tasks based on voice commands, or recommendation systems, which are used to suggest movies, shows, or products based on users’ past behavior and preferences.
• Strong AI: This is a theoretical concept that represents a level of AI that has not yet been achieved. We would be talking about AI systems that are not only capable of performing any cognitive task, but also have deep understanding and human-like consciousness. These systems would have the ability to deliberate, plan, learn, and even display emotions in a way that is indistinguishable from humans.

Machine learning

Machine learning (ML) is a subfield of AI that focuses on developing algorithms that allow machines to learn from data. Rather than being explicitly programmed to perform a task, machines in ML learn and improve autonomously through experience.

The different types of learning with respect to machines in ML are presented in the following:

• Supervised learning: The algorithm learns from a labeled data set and generates a prediction function. An example of this is spam filtering in email. The model is trained on a set of emails labeled as “spam” or “not spam. As the model learns the features that distinguish a spam email from a legitimate one, it can automatically classify new emails as spam or non-spam with high accuracy.
• Unsupervised learning: The algorithm works with data that does not have predefined labels. In this approach, the goal of the algorithm is to identify patterns, structures, or relationships hidden in the data without any prior guidance. This type of learning is particularly useful in situations where manual classification or labeling of data is not practical or possible, such as in marketing, where analyzing macro data about people’s behavior can identify and segment groups with similar interests to adjust sales strategies.
• Reinforcement learning: Instead of working with labeled data, the model learns by interacting with the environment and adjusts its actions based on a system of rewards and punishments. An example of this would be the development of AI to play games such as chess, where it uses reinforcement learning to learn and improve its playing strategy through millions of simulated games, continually adjusting its decisions to maximize wins.

Natural language processing

Natural language processing (NLP) is a branch of AI that deals with the interaction between computers and human language. The main goal of NLP is to enable machines to understand, interpret, and respond to human language in a way that is intuitive and natural to users.

• Sentiment analysis: A technique used to detect and classify the emotion or sentiment expressed in a text. This type of analysis is particularly useful in social media opinion mining, customer satisfaction surveys, and brand reputation monitoring. Sentiment analysis algorithms classify text into categories such as positive, negative or neutral, based on keywords, context and linguistic patterns.
• Chatbots: These are NLP-based applications that enable human-machine interaction in natural language. These bots can answer questions, perform specific tasks, and carry on conversations, making them useful in a variety of contexts, from customer service to personal assistance.
• Named entity recognition (NER): This is the process of identifying and classifying identities in text, such as names of people, places, organizations, or dates. This process is fundamental to information extraction, enabling machines to understand and organize large amounts of textual data.

Neural networks

Neural networks are machine learning models inspired by the human brain. They are made up of layers of artificial neurons that are interconnected to process information and make decisions based on the data they receive.

• Deep learning: This is a type of machine learning that uses deep neural networks, which are networks with multiple hidden layers that allow the model to learn complex representations of data.

• Convolutional neural networks (CNNs): CNNs are a type of neural network used primarily in image and video processing and analysis. They are typically used to identify spatial and structural patterns in data, making them an ideal choice for tasks such as object recognition, image segmentation, and facial recognition.
• Recurrent neural networks (RNNs): Designed to process sequential data, such as time series, text, or audio streams. Unlike traditional neural networks, RNNs have cyclic connections that allow them to maintain a “memory” of previously processed data. This ability to recall previous information is critical for tasks where context and order of data are important.

Generative artificial intelligence

Generative AI or GenAI refers to AI models that can generate content that appears to have been created by humans. This content can be in the form of text, images, video, music, code, or a conversation that imitates a human voice. For this reason, GenAI is the most controversial, as the levels of realism it achieves are often difficult to distinguish from the real thing.

For example, in the following photos, it seems obvious which one is made with AI, right?

But… what if I told you that both are fictional people created with artificial intelligence?

Here are the different GenAI models that make it possible to generate content like the one we just observed:

• Generative pre-trained models (GPT): This would be an example of generative AI, where the model is trained on large amounts of text to generate coherent natural language responses.
• Generative adversarial networks (GANs): GANs consist of two neural networks that are trained together in a competitive process: a generative network and a discriminative network. The generative network generates images, videos, or even audio, while the discriminative network evaluates whether the generated content is realistic or not. The goal is to improve the generative network to the point where the generated content is indistinguishable from the real thing.
• Procedural content generation (PCG): In this case, generative AI is used in video games to procedurally create worlds, creatures, and environments, allowing developers to create unique content without having to design it manually.

Computer vision

Computer vision is an area of artificial intelligence that allows computers to acquire, process, and analyze data from images or video. This technology is important for applications such as facial recognition, where the system can not only recognize a human face in an image, but also identify the person based on unique facial characteristics.

• Image recognition: The ability of a computer to identify and classify objects, features, or patterns in an image. For example, by analyzing a photo, a computer vision system can determine whether it contains a face, a car, a road sign, etc.
• Image segmentation: This is the process of dividing an image into smaller parts or distinct objects for detailed analysis. This allows an AI system not only to identify objects, but also to understand the spatial relationship between them. For example, in autonomous vehicles, image segmentation is key to enabling the driving system to identify and separate critical elements of the environment, such as lanes, traffic signs, vehicles, pedestrians, and obstacles. This enables the car to make decisions while driving.

Robotics

Robotics is an interdisciplinary field that combines artificial intelligence (AI), mechanical engineering, electronics, and computer science to design, build, and operate robots capable of performing physical tasks in the real world, often in environments that are difficult or inaccessible to humans.

A key aspect of modern robotics is the use of AI algorithms to provide robots with advanced capabilities, enabling them to make decisions, adapt to dynamic environments, and perform complex tasks autonomously.

• Autonomous robots: These are robots designed to perform tasks without direct human intervention, thanks to their ability to sense their environment, process information, and make decisions based on real-time data. A few examples of this are drones, vacuum cleaner robots, and autonomous cars.
• Human-Robot Interaction (HRI): This area focuses on how robots and humans can work together effectively, safely, and naturally as robots become more common in places such as homes, hospitals, and factories. For this reason, the need to develop efficient methods of communication and interaction between robots and humans is becoming increasingly important.

Edge AI

Perimeter processing in AI, better known as Edge AI, refers to the deployment of artificial intelligence algorithms directly on local devices or at the “edge” of a network, rather than relying solely on centralized servers in the cloud. The term “edge” in this context refers to devices or equipment that are closest to the data source and end user, such as sensors, cameras, smartphones, or Internet of Things (IoT) devices. In other words, data processing and analytics take place where the data is generated, rather than being sent to a remote data center for processing.

Conclusion

Artificial intelligence is a field that never ceases to amaze us with its rapid evolution and still has a lot of room for growth. Therefore, understanding these terms is key to better understanding the world we are heading towards.

On the other hand, all the areas we have broken down are tirelessly following the race of the big tech companies that want to position themselves in the AI sector, implementing innovations that will have an impact on our lives and how we interact with the environment around us.

With this guide, we hope to provide a solid starting point for understanding the language of AI and all that this technology entails.

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