Welcome to the TOTVS Artificial Intelligence Guide, your definitive source for understanding and implementing AI technologies in your business. This guide was created with one goal: to empower you and your business to explore, adopt, and maximize the potential of AI, transforming challenges into opportunities for innovation.
Why is this guide essential for you?
Practical knowledge : Understand the fundamentals of AI, from basic concepts to more advanced applications.
Real-world use cases : Get inspired by concrete examples of how AI is being used across industries to solve complex problems and create value.
Tools and techniques : Learn about key AI tools and techniques that can be directly applied to your work environment.
Strategy and implementation : Discover the practical steps for launching AI projects, from identifying opportunities to implementing and evaluating results.
What will you find in this guide?
What is Artificial Intelligence and the main types of AI : an panama whatsapp data objective introduction to what AI is and the different categories that comprise it.
The Role of Data for AI : Understanding the importance of data in building and training AI models.
Generative AI, the hype of the moment? : Explore the world of generative AI, its key components and its most interesting applications.
Top Use Cases of Generative AI – Learn how different industries are using generative AI to transform their businesses.
How do we address/prioritize data and AI at TOTVS? : Learn about TOTVS' approach to integrating artificial intelligence and data into its business strategy.
5 Steps to Starting an AI Project in Your Company : A step-by-step guide to start your journey with AI, from conception to scale.
Glossary : Familiarize yourself with the most important terms and concepts in the world of AI.
Welcome to a new era of possibilities with Artificial Intelligence. Let's get started!
1. WHAT IS ARTIFICIAL INTELLIGENCE AND THE MAIN TYPES OF AI
Artificial Intelligence (AI) is a field of computer science dedicated to creating systems that can perform tasks that normally require human effort. This includes skills such as reasoning, learning, environmental perception, and even object manipulation.
Advances in AI allow machines to learn from experiences, adjust to new data inputs, and perform human tasks efficiently. In fact, there are different accepted ways of grouping and classifying known types of Artificial Intelligence. But briefly, we can list the following existing types of AI:
Machine Learning (Machine Learning or Deep Learning): is a field of AI that focuses on interpreting and making predictions or classifications based on data through algorithms capable of learning from data and making predictions or decisions based on that data. The central idea is to develop models that can improve their performance over time without direct human intervention in the programming of specific tasks. Deep learning can be understood as a subcategory of machine learning, which uses deep neural networks (with many processing layers) to model and solve complex problems, such as speech recognition or image analysis.
Computer vision is a field of Artificial Intelligence that involves the development of techniques and algorithms that enable computers to interpret and understand the visual content of the world around them. This field is critical to many practical applications where visual perception is needed, such as in security systems, quality control in manufacturing, medical imaging, object recognition, and much more.
Natural Language Processing (NLP) – focuses on the interaction between computers and humans using natural language. The main tasks of NLP include machine translation, speech recognition, sentiment analysis, information extraction, and text generation, among others. The goal of NLP is to understand, interpret, and manipulate human language in a way that is useful for practical applications. Text generation is just one of the many applications of NLP.
Generative AI: This is a broad field and includes different techniques that are used to generate new data that can be images, music, video, and text, among others. The goal here is to create content that cannot be distinguished from the real thing or to generate new examples within a certain data domain. But when we refer specifically to text generation, then the models used are the so-called LLMs (Large Language Models), which use various techniques both to understand human language and to produce text that makes sense for human understanding.
It is worth noting that the 4 types mentioned above can be interrelated to solve certain use cases where different techniques or models are required to be applied together to achieve the expected result. For example, generative AI for text generation and interactions with humans requires NLP models to enter the field beforehand to determine the understanding of the subject of the ongoing interaction.
2. THE ROLE OF DATA FOR AI
Data plays a fundamental role in Artificial Intelligence (AI), being essential for training, testing and improving AI models. It is the foundation on which machine learning and deep learning algorithms operate, and the quality, quantity and relevance of this data have a direct impact on the effectiveness and efficiency of AI systems. In this sense, we can list some essential roles that data plays in any project involving AI:
Model training:
Supervised learning: AI models are trained using large data sets that include inputs and expected outputs. This data allows the model to learn to make predictions or decisions based on past examples.
Unsupervised learning: In unsupervised learning tasks, AI models are trained to identify patterns and relationships in data sets without predefined labels.
Reinforcement learning: Data about interactions with the environment and feedback on model performance are used to learn strategies or behaviors.
Evaluation and validation: Data is used to validate and test the accuracy of AI models after training. This is often done using a separate test dataset, which the model has not seen during training, to ensure that the model can generalize well to new data.
Continuous improvement: Data collected from real-life user interactions with AI systems can be used to continuously improve models. For example, identified errors or new examples that were not predicted correctly can be added to the training set for future learning iterations.
Personalization: User-specific data enables AI systems to personalize their responses and recommendations. The more data a system has about a user’s preferences, behavior, and history, the more precise and personalized the interactions can be.
Anomaly detection: In many industries, data is monitored by AI systems to detect abnormal patterns or suspicious activities. This is critical for fraud prevention, predictive maintenance, and security monitoring.
Simulations and modelling: Historical and simulated data are used to model complex scenarios and perform simulations in controlled environments. This is widely used in fields such as meteorology, economics and urban planning.
Data quality is crucial. Bad data can lead to the “garbage in, garbage out” phenomenon, where AI models trained on poor quality data produce useless or incorrect results. Accurate, diverse, and representative data is essential to the success of AI systems.
Data is not just the fuel of AI; it is the backbone that determines how well an AI system can operate, adapt, and evolve over time. And precisely to highlight the importance of this stage in any AI project, here at TOTVS we always repeat the mantra: “WITHOUT INTEGRATED, ACCESSIBLE, AND READY DATA, THERE IS NO AI.”
3. GENERATIVE AI, THE “HYPE” OF THE MOMENT?
3.1 What is it?
The essence of generative AI models (both LLMs for text generation and models for image generation, for example) lies in their ability to evaluate and manipulate probabilities. These models use advanced neural networks (often called deep neural networks or deep learning) to learn patterns in large data sets. They are trained to generate new data that resembles the training data. Practical implications of this capability:
In text generation, the model uses these probabilities to create coherent and contextually appropriate text sequences. For example, in a sentence where the context suggests a conversation about the weather, the model will calculate which weather-related words are more likely to follow than words related to irrelevant topics.
On diversity and variety: Manipulating these probabilities also allows LLM models to vary the style, tone, or specificity of the generated text. For example, by adjusting temperature (a parameter that affects the probability distribution during text generation), you can make the model generate more conservative (more likely) or more creative (less likely) responses.
In comprehension and correction: The ability to assess probabilities also allows LLMs to be used for tasks such as sentence completion, grammar correction, and even translating texts, always depending on which sequence of words is statistically most probable given an initial sequence or context.