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Unraveling the technical terms of AI

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A Artificial Intelligence is the hot topic in the technology world right now – many companies are talking about how they are developing or using AI-based solutions to advance and transform your business. However, the field of AI is full of technical terms, which can make it difficult to understand the real innovations that are happening.

To help you better understand what’s behind these innovations, we’ve gathered some of the most common terms used in AI, explaining what they mean and why they’re important.

What is AI?

A artificial intelligence, or AI, is a field of computer science focused on the development of systems that simulate human reasoning. However, nowadays, the term “AI” is widely used to describe technologies that, although they seem “intelligent”, have more specialized functionalities. Examples of this are language models (MLs) OpenAI's GPT and Google's Gemini, which represent significant advances in this area. Companies like these promote AI as an innovation that is being integrated into a wide range of products and solutions, while others, like Meta, are initially exploring the use of AI to enhance chatbots.

So artificial intelligence goes beyond tools or software that appear “smart”; it involves systems with the ability to learn, adapt, and in some cases even make decisions based on complex data. In other words, while many innovations are labeled as artificial intelligence, the term actually refers to systems designed to evolve based on experience and pattern analysis, being a complex and constantly developing field. Understanding these differences is essential to demystify the area and understand where AI actually fits in among technological innovations.

Key AI Terms

A IA is expanding rapidly and being applied in various areas of everyday business. With this evolution, important concepts that we need to understand to get the most out of these technologies also emerge. From tools that learn from large volumes of data, to (LLMs - Large Language Models) up until systems capable of generating new content and making complex decisions, fully autonomous, AI offers powerful solutions, but it also presents challenges. Here are some of the main AI terms that have been applied in the business world:

  1. Machine Learning (ML): This technology allows systems to learn from past data, helping to make predictions and find patterns. It is widely used to analyze the market, personalize offers and predict sales.
  2. Natural Language Processing (NLP): Focused on understanding and generating human language, this technology is behind chatbots, customer sentiment analysis and document automation.
  3. Computer vision: With this technology, companies can analyze and interpret images and videos. It is used for safety monitoring, product quality control and factory automation.
  4. Predictive Analysis: Powerful tool that uses historical data to predict the future. Very useful in marketing, inventory management and identifying business risks.
  5. Robotic Process Automation (RPA) with AI: Combines AI with automation to perform repetitive tasks quickly and accurately. This makes administrative, financial, and customer service operations more efficient.
  6. AI-Powered Personalized Recommendations: These systems use algorithms to suggest products or content in a personalized way, being essential in e-commerce, streaming platforms and digital marketing.
  7. Sentiment Analysis: Tool that identifies emotions in texts, helping companies understand what customers are thinking and monitor brand image.
  8. Natural Language Generation (NLG): Technology that transforms data into clear and understandable texts, ideal for creating automatic reports, summaries and even personalized content.
  9. Deep Learning: A more advanced machine learning technique, based on complex neural networks, which allows handling large volumes of data and solving more challenging problems, such as image recognition and consumer behavior analysis.
  10. Generative AI: A subfield of AI focused on creating new content — such as text, images, videos, and music — from existing data. It has gained ground in marketing, design, and product development.

These terms describe some of the most widely used AI tools and technologies to help businesses better understand their customers, optimize operations, and innovate in the marketplace.

AI Models

As artificial intelligence advances, the term “AI models” has become increasingly common, but understanding what it actually means can be challenging. Essentially, AI models are systems that, after being trained with large volumes of data, perform tasks autonomously or assist in decision-making, applying algorithms and machine learning techniques and, often, deep learning (Deep Learning). There are several types of models, each with specific characteristics and applications, ranging from natural language processing to image and video creation.
Main types of AI models:
  • Generalist AI Models: These models are trained on large, comprehensive datasets, allowing them to perform a variety of tasks without the need for application-specific training. They can process multiple data types, such as text, images, and video, and are often described as “multimodal.”
  • Large Scale Language Models (LLMs): These are models focused on processing and generating text in natural language. Anthropic's Claude and OpenAI's GPT are examples of LLMs designed to function as conversational assistants, helping users with various tasks such as conversational chatbot.
  • Diffusion Models: These models are used to generate images (and, in newer versions, audio and video) from textual descriptions. They work by gradually adding and removing noise in visual data, which allows them to create detailed, personalized results based on prompts.
  • Base Models: These are models that, trained on vast volumes of data, can be applied to a wide range of tasks, without the need for specific adjustment for each function. They offer a flexibility that makes them ideal for various business areas, such as data analysis and customer service.
  • Border Models: Refers to next-generation AI models that are being developed to achieve higher levels of performance and autonomy. While promising advances, these models also generate discussions about potential risks and the need for regulation to ensure their safe use.
Each of these model types plays an important role in the AI ​​ecosystem, offering solutions ranging from customer support content creation and data analysis. Understanding these distinctions helps companies and users apply AI technologies strategically and responsibly.

Other relevant terms:

  • Natural Language Processing (NLP): It refers to the ability of machines to understand human language, as OpenAI's ChatGPT does.
  • Inference: It is the process of generating an output, such as when ChatGPT creates a response to a question.
  • Tokens: Small chunks of text that AI models use to understand the relationship between words and generate appropriate responses.
  • Neural Network: An architecture that mimics the functioning of the human brain and is essential for generative AI to recognize complex patterns.
  • Transform: A type of neural network that processes the relationship between words in a sequence and speeds up model training, and is crucial to the success of generative AI.
  • RAG (Recovery Augmented Generation): Allows AI models to supplement their responses with external information, improving response accuracy.

Top AI Companies and Applications

With the rapid advancement of artificial intelligence, several companies are making a name for themselves in the development of AI-based tools and solutions. Some are tech giants that already dominate the market, while others are emerging startups. Below, we explore some of the top companies leading the AI ​​race:
  • OpenAI / ChatGPT: The main driver of the current AI boom is undoubtedly OpenAI's ChatGPT, launched in late 2022. Its popularity has taken tech giants by surprise, and now virtually every company in the industry is trying to make a name for itself in AI.
  • Microsoft / Copilot: Microsoft is integrating Copilot, its AI assistant, into several of its products, using OpenAI’s GPT models. The company also holds a significant 49% stake in OpenAI, reinforcing its bet on the future of AI.
  • Google/Gemini: Google is rapidly incorporating AI into its products through Gemini, which encompasses both the company's AI assistant and several AI models used across its platforms.
  • Goal / Llama: Meta focuses its AI efforts on the Large Language Model Meta AI (Llama) model. Unlike other major players, Llama is open source, making it accessible to developers and researchers.
  • Apple / Apple Intelligence: Apple is introducing new AI features under the name Apple Intelligence. One of the highlights is the integration of Chat GPT directly into Siri, expanding the capabilities of the virtual assistant.
  • Anthropic / Claude: Founded by former employees of OpenAI, Anthropic develops the Claude AI models. The company has received significant investment, including $4 billion from Amazon and hundreds of millions from Google. Instagram co-founder Mike Krieger was recently hired as chief product officer.
  • xAI/Grok: This is Elon Musk's AI company, responsible for developing Grok, an advanced language model. xAI has already raised $6 billion in funding to continue driving its innovations.
  • Perplexity: Known for its AI-powered search engine, Perplexity has drawn attention for its scraping practices, which have generated some scrutiny in the market.
  • Hugging Face: A platform dedicated to hosting and sharing AI models and datasets, becoming a reference point for developers and researchers in the field.

Conclusion

A artificial intelligence AI is emerging as a transformative force in the business world, bringing innovations ranging from recommendation systems to virtual assistants. Understanding the key concepts and terms related to AI is essential for businesses to make the most of this technology. From machine learning and artificial general intelligence to the practical applications of generative AI, as businesses continue to explore and implement AI solutions, they must be aware of the nuances and complexities involved, ensuring that they can not only benefit from the opportunities but also address the ethical and social concerns that arise with the advancement of AI.
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