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.”
3.1 What is it?
The essence of generative AI models (both LLMs for text senegal whatsapp data 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.
The most advanced generative AI models for text generation (LLM) use a specific neural network architecture known as a “Transformer” (more on this later). These models are initially pre-trained on large amounts of unsupervised text (learning to predict the next word in sentences, for example) and can then be fine-tuned for specific tasks.
Generative Artificial Intelligence has gained prominence and become the “hype of the moment” for several reasons, reflecting both technological advancements and innovative, practical applications. Here are some of the main factors contributing to its popularity:
Groundbreaking creative capabilities: Generative AI is capable of creating new and unique content, such as text, images, music, and videos, that are often indistinguishable from those created by humans. This ability to “create” rather than simply “analyze” or “predict” captures the imagination of audiences and businesses alike, suggesting a future where AI is not just an analytical tool but also a creative partner.
Significant advances in specific models: Some models have demonstrated extraordinary capabilities for generating coherent text and creative images from simple textual descriptions. The ability of these models to generate detailed and highly specific content has attracted significant media attention and commercial interest.
Compelling commercial applications: Companies are finding practical uses for generative AI that can transform industries. This includes everything from automating graphic design and content production to personalized applications in fashion, advertising, and entertainment. The ability to customize products and services at scale, without the costs associated with traditional human creation, is particularly appealing.
Accessibility and usability improvements: Platforms with increasingly user-friendly interfaces have made generative AI more accessible to developers and creatives without specialized training in AI or programming. This democratizes the power of generative AI, allowing a broader range of users to experiment and implement their own creative ideas.
Cultural and social impact: Generative AI has also sparked important debates about ethics, authorship and creativity. Questions about copyright, authenticity and the role of the machine in art and content creation stimulate public debates that increase the visibility and fascination of these technologies.
Technology evolution and investment: Continuous advancement in computational power and deep learning algorithms has enabled constant improvements in generative AI techniques. Furthermore, substantial investment in research and development by major technology players such as Google, OpenAI, and others is accelerating the development and application of these technologies.